亿迅智能制造网
工业4.0先进制造技术信息网站!
首页 | 制造技术 | 制造设备 | 工业物联网 | 工业材料 | 设备保养维修 | 工业编程 |
home  MfgRobots >> 亿迅智能制造网 >  >> Manufacturing Technology >> 制造工艺

Spectrino:TinyML Arduino 和基于物联网的无接触解决方案

组件和用品

Arduino Nano 33 BLE Sense
× 1
乐鑫 ESP8266 ESP-01
× 1
Arducam Mini 2MP plus
× 1
MAX7219 显示
此显示器建议为 4 in 1 , 32*8 显示器
× 1
蜂鸣器
× 1
SG90 微伺服电机
这是一个通用的舵机 - 我将使用 MG995 或 MG959
× 2
Adafruit Micro-Lipo 充电器
× 1
Adafruit 锂离子聚合物电池
× 1
Arduino MKR WiFi 1010
× 1
RGB 扩散共阴极
× 1
超声波传感器 - HC-SR04(通用)
× 1

必要的工具和机器

10 件。跳线套件,5 厘米长

应用和在线服务

Arduino 网页编辑器
ThingSpeak API
Edge Impulse Studio
TensorFlow

关于这个项目

概览

大流行给社交互动带来了限制:距离。 考虑到这一风险因素,世界各国都处于不同程度的隔离状态,许多购物中心因消费者数量显着减少而不得不关闭 .这导致了非常、非常高水平的裁员 商场人员 ,以及类似的企业主面临的经济挑战 .

由于 COVID-19,截至 2020 年 7 月 2 日,这已导致美国相对低收入(年收入低于 40,000 美元)的失业水平。住宿和餐饮服务以及零售贸易和娱乐业总共占估计损失的约 4,000,000 个工作岗位。

不同行业面临的经济挑战,食品、消费品和零售在裁员人数最多的前 6 个行业中名列前茅(估计相当于 20,000 多个工作岗位)。假设本研究将“国际”纳入所有非美国地区,全球裁员总数估计达到 103,000 多个。

并发症

考虑到给定的数据,该团队确定了一个主要挑战,即对于仍在营业的购物中心,不同商店的人口密度存在风险不​​确定性。除此之外,虽然在很多地方都强制要求戴口罩和避免接触,但仍然存在违规行为。这使得那些负担不起远程工作的商场人员和企业主更难安全地在这个新的正常工作空间中导航。这就引出了一个问题:我们如何才能保证在任何时间点都可以安全进入商场中的特定商店?

我们现在都在与流行的 COVID-19 大流行作斗争。而且,现在我们处于必须采取更多安全措施来适应当前条件的情况。在采取更多安全措施避免病毒感染的生活恢复正常的同时,增加公共场所和拥挤区域的安全性也在城市中普遍存在。但是在很多情况下,我们不得不打破安全措施并与不安全的元素互动以满足有需要的人。在这里,该项目正在处理通过触摸交互或触摸来预防 COVID-19 传播。

已经进行了一项实验,以观察任何病毒通过触摸的传播。结果评价如下:

因此,我决定将住宅和社会中最常用的设备自动化,以确保与设备的免提通信。

在制作这个原型时开发了以下解决方案

  • 使用在 Arduino33 BLE Sense 上部署的 TinyML 的智能对讲系统: 以下将是一个使用计算机视觉和 TinyML 模型的非接触式解决方案来检测门外的人并在没有人触摸铃铛的情况下进行铃响。
  • 温度监测系统使用物联网和警报系统: 疫情之下,安全成为重中之重。因此,温度监控系统利用 IoT Thingspeak 仪表板并在进入时检测人员并测量他们的温度。该温度显示在 IoT 仪表板上,以便及时进行趋势和数据分析。检测到异常温度后,系统会发出警报,人员将接受第二次检查。
  • 在 Arduino BLE 33 sense 上使用语音识别 TinyML 模型的免触摸电梯系统: 我们每天使用电梯在建筑物中上下几次,我总是害怕触摸被其他通勤者触摸过的受污染的开关。因此,此语音识别模型将识别一个人何时希望上升或下降,并以类似方式执行该动作。
  • 基于 TinyML 和 IoT 监控系统的 MaskModel 检测系统: 该方法将使用部署在 Arduino BLE 33 sense 上的计算机视觉模型来检测一个人是否戴过口罩,类似地,该数据将发送到 IoT 仪表板,以根据不安全时间进行监控和施加限制。
  • 使用 TinyML、物联网和计算机视觉在超市或商场中的智能队列监控和建立系统: 该模型将检测一个站在超市外的人,并允许一次允许 50 人进入超市。再等15分钟,让里面的人完成购物,让下一组50人再次进入商场。这将使用计算机视觉和部署在 Arduino 33 BLE 传感器上的 TinyML 来完成。然后,这些数据将被投射到物联网仪表板,在那里可以跟踪实时数据。
  • 购物中心过道中的人员监控系统和基于污染的消毒系统 :此解决方案使用部署在商场或超市区域的人员检测算法,如果某个区域的人员污染超过阈值,它会使用紫外线对该区域进行自我消毒。消毒周期和时间会在 IoT 仪表板上进行预测,供超市员工进行分析。

设置(项目要求):

硬件要求:

1)Arduino33BLE感

Arduino Nano 33 BLE Sense 是传统 Arduino Nano 的进化版,但具有更强大的处理器,Nordic Semiconductors 的 nRF52840,一个运行在 64 MHz 的 32 位 ARM® Cortex™-M4 CPU。这将允许您制作比 Arduino Uno 更大的程序(它有 1MB 的程序内存,大 32 倍),以及更多的变量(RAM 大 128 倍)。主处理器包括其他惊人的功能,例如通过 NFC 进行蓝牙配对和超低功耗模式。

嵌入式人工智能

除了令人印象深刻的传感器选择之外,该板的主要特点是可以使用 TinyML 在其上运行边缘计算应用程序 (AI)。您可以使用 TensorFlow™ Lite 创建机器学习模型,并使用 Arduino IDE 将它们上传到您的开发板。

2)ESP8266 ESP-01

ESP8266 ESP-01 是一个允许微控制器的Wi-Fi模块 访问 Wi-Fi 网络 .该模块是一个独立的SOC (片上系统)不一定需要微控制器来操作输入和输出,就像您通常使用 Arduino 所做的那样 例如,因为 ESP-01 充当小型计算机。根据 ESP8266 的版本,最多可以有 9 个 GPIO(通用输入输出)。因此,我们可以像 Wi-Fi 盾一样为 Arduino 提供微控制器互联网访问权限,或者我们可以简单地对 ESP8266 进行编程,使其不仅可以访问 Wi-Fi 网络,还可以充当微控制器。这使得 ESP8266 非常通用。

3)Arducam Mini 2MP plus

ArduCAM-2MP-Plus是ArduCAM shield Rev.C的优化版本,是一款高清2MP SPI摄像头,降低了摄像头控制接口的复杂度。它集成了2MP CMOS图像传感器OV2640,并提供微型尺寸,以及易于使用的硬件接口和开源代码库。

ArduCAM mini 可用于任何平台,如 Arduino、Raspberry Pi、Maple、Chipkit、Beaglebone black,只要它们具有 SPI 和 I2C 接口,并且可以与标准 Arduino 板很好地配合。 ArduCAM mini 不仅能够添加一些低成本微控制器所没有的摄像头接口,还能够将多个摄像头添加到单个微控制器中。

4) Arduino MKR WiFi 1010:

Arduino MKR WiFi 1010 是基本物联网和微微网络应用设计的最简单入口。无论您是要构建连接到办公室或家庭路由器的传感器网络,还是要创建一个 BLE 设备将数据发送到手机,MKR WiFi 1010 都是您许多基本物联网应用的一站式解决方案场景。

该板的主处理器是低功耗 Arm® Cortex®-M0 32 位 SAMD21,就像 Arduino MKR 系列中的其他板一样。 WiFi 和蓝牙® 连接是通过 u-blox 的模块 NINA-W10 实现的,NINA-W10 是一种在 2.4GHz 范围内运行的低功耗芯片组。除此之外,通过 Microchip® ECC508 加密芯片确保安全通信。除此之外,您还可以找到一个电池充电器和一个可定向的 RGB LED 板载。

软件工具:

1)Arduino 网页编辑器

Arduino Create 是一个集成的在线平台,使创客和专业开发人员能够编写代码、访问内容、配置板和共享项目。从一个想法到完成物联网项目的速度比以往任何时候都快。使用 Arduino Create,您可以使用在线 IDE,将多个设备连接到 Arduino IoT Cloud,浏览 Arduino 项目中心上的项目集合,并使用 Arduino 设备管理器远程连接到您的开发板。您还可以分享您的作品以及分步指南、原理图、参考资料,并接收他人的反馈。

2)Edge Impulse Studio:

在微控制器上运行机器学习的趋势有时被称为嵌入式机器学习或微型机器学习。 TinyML 有可能创建小型设备,无需将数据发送到云即可做出明智的决策——从效率和隐私的角度来看非常棒。即使是强大的深度学习模型(基于人工神经网络)现在也可以应用于微控制器。在过去的一年里,通过 TensorFlow Lite for Microcontrollers、uTensor 和 Arm 的 CMSIS-NN 等项目,在使深度学习模型更小、更快和可在嵌入式硬件上运行方面取得了长足的进步;但是构建一个高质量的数据集、提取正确的特征、训练和部署这些模型仍然很复杂。

使用 Edge Impulse,您现在可以快速收集真实世界的传感器数据,在云中根据这些数据训练 ML 模型,然后将模型部署回您的 Arduino 设备。从那里您可以通过单个函数调用将模型集成到您的 Arduino 草图中。然后,您的传感器变得更加智能,能够理解现实世界中的复杂事件。内置示例允许您从加速度计和麦克风收集数据,但只需几行代码即可轻松集成其他传感器。

3) 物语:

ThingSpeak™ 是一项 IoT 分析服务,可让您聚合、可视化和分析云中的实时数据流。 ThingSpeak 提供由您的设备发布到 ThingSpeak 的数据的即时可视化。凭借在 ThingSpeak 中执行 MATLAB® 代码的能力,您可以执行在线分析和处理数据。ThingSpeak 通常用于需要分析的物联网系统原型设计和概念验证。

您可以使用 Rest API 或 MQTT 将数据从任何联网设备直接发送到 ThingSpeak。此外,与 The Things Network、Senet、Libelium Meshlium 网关和 Particle.io 的云到云集成使传感器数据能够通过 LoRaWAN® 和 4G/3G 蜂窝连接到达 ThingSpeak。

实施入门:

第一个项目:在 Arduino BLE 33 sense 上使用语音识别 TinyML 模型的无接触电梯系统:

上下班时,我们一天要使用几次电梯!一个常用的开关被所有之前接触过电梯的人污染了。因此,我决定为电梯创建一个非接触式解决方案,该解决方案使用语音命令,无需使用 IoT 或 Wifi 网络,即可协作并执行操作。已经实施了使用手势控制或超声波传感器来执行操作的免触摸电梯系统的解决方案,但是这些传感器面临的问题是它们需要从更近的距离被激活,因此增加了触摸的风险。此外,这些传感器非常敏感,即使有小物体挡住并激活它们也会被激活。因此,我建议使用 Arduino 33 BLE 感知上的语音检测创建更准确的解决方案。这个模型接受两个命令,一个是 "up"“向下” 并相应地将数据发送到伺服器以按下开关上的相应按钮。将数据发送到伺服以激活开关的想法是因为大多数社会已经预先构建了开关和显示系统,因此不会妨碍这些预先存在的系统,这是为了添加外部硬件系统来控制功能。

用于执行此功能的核心逻辑是:

在 Edge Impulse Studio 上训练模型解释“up”和“Speech”命令

a) 使用训练和测试数据集积累原始数据。在这里,我累积了 1:30 分钟的数据,每个数据持续时间为“上”和“下”2 秒。

b) 根据所需参数创建脉冲:

在这里,我将窗口增量大小设置为 300 毫秒,并基于 Keras 神经网络进行训练,该网络专用于麦克风和加速度计数据。

c) 原始数据到处理数据的转换。在原始数据的正下方,我们可以看到原始数据的特征,处理后的数据被视为基于倒谱系数的 DSP 结果。

d) 训练输入数据以生成处理过的特征。我已经根据相同的数据记录了低频和高频的数据,以更好地训练冲动并使语音识别准确地基于所有类型的声音训练它。在这里,我们在 x、y 和 z 轴上具有中心微分的“S”曲线中得到一个特征输出。

e) 最后,在边缘脉冲神经网络分类器中设计神经网络架构并训练网络。

这里针对数据输入设计的神经网络架构如下:

  • 输入层
  • 重塑图层
  • 一维卷积池层(30 个神经元,5 个核)
  • 一维卷积池层(10 个神经元,5 个核)
  • 压平层

该模型表现相当不错,平均准确度为 91.2%,损失为 0.29。该模型经过 100 个训练周期(epochs)的训练。混淆矩阵看起来非常清晰和准确,大多数其余数据与其各自的标记类别相匹配。

模型训练好后,我用测试数据和实时数据对模型进行了测试,基于 24 秒的测试数据,模型得到了 75% 的准确率

最后,在模型经过训练并在逻辑上获得不错的准确性后,我将该模型部署为 Arduino 库并将其部署在 Arduino 33 BLE Sense 上。

脚本准备好后,为了便于定制,我开始在 Arduino Web Editor 上对其进行编辑,最终可以部署在 Arduino 33 BLE Sense 上的脚本输出如下:

你可以在这里看到 Arduino Web Editor 平台上的 main.ino 文件:

Main-script.ino - Arduino 网络编辑器

Main-script.ino - Github

部署到 Arduino 33 BLE Sense

在撰写本文时,唯一带有内置麦克风的 Arduino 板是 Arduino Nano 33 BLE Sense,因此我们将在本节中使用它。如果您使用不同的 Arduino 板并连接自己的麦克风,则需要实施

在撰写本文时,唯一带有内置麦克风的 Arduino 板是 Arduino Nano 33 BLE Sense,因此我们将在本节中使用它。

Arduino Nano 33 BLE Sense也有一个内置的LED,我们用它来表示一个词已被识别,并控制舵机执行功能

这是模型中的微特征代码片段

响应命令的逻辑如下:

// 如果我们听到命令,点亮相应的 LED 并打开相应的伺服
if (found_command[0] =='up') {
last_command_time =current_time;
digitalWrite(LEDG, LOW); //绿色为向上,通过LOW命令打开LED
servo_7.write(0); // 当说“向上”时,将舵机旋转到 0 度以单击电梯上的按钮
delay(100);
digitalWrite(LEDG, HIGH); // 闪烁命令后关闭 LED
servo_7.write(180); // 舵机返回其原始位置,即 180 度
}
// 如果我们听到命令,则点亮相应的 LED 并打开相应的舵机
if (found_command[0] =='down') {
last_command_time =current_time;
digitalWrite(LEDG, LOW); //绿色为向上,通过LOW命令打开LED
servo_7.write(0); // 当说“向下”时,将舵机旋转到 0 度以单击电梯上的按钮
delay(100);
digitalWrite(LEDG, HIGH); // 闪烁命令后关闭 LED
servo_7.write(180); // 伺服返回其原始位置,即 180 度
}

第二个命令响应与上面相同,但在听到“向下”命令时打开伺服。

训练数据后,我们得到一个训练好的 tflite 数据集输出如下:

在这里,我们使用 Arduino Nano 33 BLE Sense 上的内置麦克风,模型平均使用 Arduino Nano 板上 256kb 闪存中的约 24-28Kb。与图像识别模型相比,该模型重量相对较轻,并且可以以更高的速度处理信息。

语音识别模型的逻辑如下:

  • 数据被捕获
  • 从麦克风中捕获音频样本
  • 将原始音频数据转换成频谱图
  • Tflite 解释器运行模型
  • 使用推理输出判断命令是否被听到
  • 如果听到下降命令,则按下升降控制面板上的下降键,舵机移动。
  • 如果听到向上命令,则按下升降控制面板上的向上键,舵机移动。

上面是训练好的模型库中的数据。主要功能包含在Tensorflow文件中。

项目的电路图:

Arduino Nano 33 BLE Sense 使用内置麦克风来收集原始数据。由于 Arduino 上的推理需要时间,因此我添加了 100 毫秒的延迟以准确处理数据。因此,在两个录音样本之间,内置的蓝色 LED 闪烁,表示在两个 LED 闪烁之间听到/说出的响应。

这是 Arduino 33 BLE Sense 的模拟,演示了两个成功的麦克风输入间隔之间 LED 的闪烁


根据指令输入,舵机相应旋转

现在还有其他替代非接触式电梯自动化系统的方法,例如使用超声波传感器或手势传感器,但这两者都有其自身的缺陷,因此我决定制作语音控制的电梯自动化系统

超声波传感器面临的缺陷: 超声波传感器对运动高度敏感。如果在通道中移动的任何物体进入超声波传感器的范围内,它们就会被激活。超声波传感器也不够准确,有时会处理错误的信息。

手势控制传感器的缺陷: 这些传感器比超声波传感器更准确,但它们需要从更近的距离激活。这会增加手与升降面板之间接触的风险。

但是语音控制的电梯面板比上述两种方案更准确,并且可以从比超声波和手势传感器更远的距离激活。

这是精度与激活距离的关系图以及这些传感器的位置。

这显示了激活手势传感器所需的距离。由于这个距离看起来真的很小,污染率很高

接下来,我们进入主项目的第二个子项目部分

第二个项目:使用面部识别和 TinyML 的智能对讲系统:

CDC 更新其网站并发布新闻稿称,来自被新型冠状病毒污染的表面的间接接触(称为污染物传播)是感染新型冠状病毒的潜在途径。

研究发现,这种新型冠状病毒可以在塑料和金属表面以及纸板上持续长达 3 天的时间,长达 24 小时。但是,一个人要因接触受污染的表面而感染 Covid-19,需要发生很多事情。

首先,一个人必须接触到足够多的病毒才能真正引起感染。例如,要感染流感病毒,需要数百万份病毒从表面到达人脸,但当病毒直接进入肺部时,只需要几千份,纽约次 报告。

如果一个人碰巧接触到带有大量病毒痕迹的表面,他们就必须吸收足够多的病毒,然后再触摸自己的眼睛、鼻子或嘴巴——这就是为什么公共卫生专家说避免接触如此重要的原因过于频繁地接触被污染的物体或那些经常接触的物体。

COVID-19 病毒正在世界范围内传播。即使它最终消退,人们也会对在公共场合触摸事物产生敏感性。由于大多数对讲机都设计为需要按下按钮才能拨打电话,因此我决定需要不需要人们触摸任何东西的非接触式对讲机解决方案。

上图显示了一个基于人脸识别的对讲系统实现。

为了解决基于触摸的系统的问题,有必要减少接触和触摸的地方。在传统的对讲系统中,它们由基于开关的系统组成,按下时会响铃。为了改进会增加表面污染风险的基于触摸的系统,我决定构建一个部署在 Arduino 33 BLE Sense 上的非触摸面部识别系统,基于 tinyML 和 Tensorflow Lite。

在这个智能对讲系统中,我使用了部署在 Arduino 33 BLE Sense 上的人员检测算法,该算法可以识别人员,并相应地用 LED 矩阵显示器按响铃,读出“人员”。

走向实施智能对讲系统:

在设计此模型时使用了以下软件:

  • TensorFlow 精简版
  • Arduino 网络编辑器

这个人物检测模型,我使用了适用于该项目的预训练的 TensorFlow 人物检测模型。 This pre-trained model consists of three classes out of which the third class is with undefined set of data:

"unused",

"person",

"notperson"

In our model we have the Arducam Mini 2mp plus to carry out image intake and this image data with a decent rate of fps is sent to the Arduino Nano 33 BLE Sense for processing and and classification. Since the Microcontroller is capable of providing 256kb RAM, we change the image size of each image to a standard 96*96 for processing and classification. The Arduino Tensorflow Lite network consists of a deep learning framework as:

  • Depthwise Conv_2D
  • Conv_2D
  • AVERAGE Pool_2D
  • Flatten layer

This deep learning framework is used to train the Person detection model.

The following is the most important function defined while processing outputs on the Microcontroller via Arduino_detetction_responder.cpp

// Process the inference results.
uint8_t person_score =output->data.uint8[kPersonIndex];
uint8_t no_person_score =output->data.uint8[kNotAPersonIndex];
RespondToDetection(error_reporter, person_score, no_person_score);

In the following function defining, the person_score , the no_person_score have been defined on the rate of classification of the data.

The Logic works in the Following way:

├── Autonomous Intercom System
├── Arducam Mini 2mp plus
│ ├── Visual data sent to Arduino
├── Arduino 33 BLE Sense
│ ├── if person-score> no_person_score
│ │ ├── Activate the buzzer
│ │ ├── Display "Person" on the LED Matrix
│ │ └── ...Repeat the loop

Adhering to the above logic, The arducam Mini 2mp plus continuously takes in visual data and sends this data to the Arduino 33 BLE Sense to process and classify the the data collected. Once the raw data is converted to processed data, it is then classified as per the data trained. If a person is detected, the Arduino sends a signal to the buzzer to activate and the MAX7219 to display "person". In this way, the logic of the system works.

Functioning and Working of Logic in Code:

The following are the Libraries included in themain.ino code for functioning of the model.

#include 

#include "main_functions.h"

#include "detection_responder.h"
#include "image_provider.h"
#include "model_settings.h"
#include "person_detect_model_data.h"
#include "tensorflow/lite/micro/kernels/micro_ops.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"

In the following code snippet, the loop is defined and performed. Since this is the main.ino code, it controls the core functioning of the model - used to run the libraries in the model.

void loop() {
// Get image from provider.
if (kTfLiteOk !=GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
input->data.uint8)) {
TF_LITE_REPORT_ERROR(error_reporter, "Image capture failed.");
}

// Run the model on this input and make sure it succeeds.
if (kTfLiteOk !=interpreter->Invoke()) {
TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed.");
}

TfLiteTensor* output =interpreter->output(0);

// Process the inference results.
uint8_t person_score =output->data.uint8[kPersonIndex];
uint8_t no_person_score =output->data.uint8[kNotAPersonIndex];
RespondToDetection(error_reporter, person_score, no_person_score);
}

In the following code snippet, the necessary libraries required to inference the image to be captured is displayed. The images after captured are converted to a 96*96 standardised size which can be interpreted on the arduino board.

Here, the Arducam mini 2mp OV2640 library has been utilised.

This code has been provided in the arduino_image_provider.cpp snippet

#if defined(ARDUINO) &&!defined(ARDUINO_ARDUINO_NANO33BLE)
#define ARDUINO_EXCLUDE_CODE
#endif // defined(ARDUINO) &&!defined(ARDUINO_ARDUINO_NANO33BLE)

#ifndef ARDUINO_EXCLUDE_CODE

// Required by Arducam library
#include
#include
#include
// Arducam library
#include
// JPEGDecoder library
#include

// Checks that the Arducam library has been correctly configured
#if !(defined OV2640_MINI_2MP_PLUS)
#error Please select the hardware platform and camera module in the Arduino/libraries/ArduCAM/memorysaver.h
#endif

// The size of our temporary buffer for holding
// JPEG data received from the Arducam module
#define MAX_JPEG_BYTES 4096
// The pin connected to the Arducam Chip Select
#define CS 7

// Camera library instance
ArduCAM myCAM(OV2640, CS);
// Temporary buffer for holding JPEG data from camera
uint8_t jpeg_buffer[MAX_JPEG_BYTES] ={0};
// Length of the JPEG data currently in the buffer
uint32_t jpeg_length =0;

// Get the camera module ready
TfLiteStatus InitCamera(tflite::ErrorReporter* error_reporter) {
TF_LITE_REPORT_ERROR(error_reporter, "Attempting to start Arducam");
// Enable the Wire library
Wire.begin();
// Configure the CS pin
pinMode(CS, OUTPUT);
digitalWrite(CS, HIGH);
// initialize SPI
SPI.begin();
// Reset the CPLD
myCAM.write_reg(0x07, 0x80);
delay(100);
myCAM.write_reg(0x07, 0x00);
delay(100);
// Test whether we can communicate with Arducam via SPI
myCAM.write_reg(ARDUCHIP_TEST1, 0x55);
uint8_t test;
test =myCAM.read_reg(ARDUCHIP_TEST1);
if (test !=0x55) {
TF_LITE_REPORT_ERROR(error_reporter, "Can't communicate with Arducam");
delay(1000);
return kTfLiteError;
}

The following code is the Arduino_detection_responder.cpp code which controls the main output of the model. Here, we have taken into consideration, the classification score as defined in the main.ino code and according to the confidence of person score, I am providing outputs.

// Switch on the green LED when a person is detected,
// the red when no person is detected
if (person_score> no_person_score) {
digitalWrite(LEDG, LOW); // if a person is detected at the door, the buzzer switches on
digitalWrite(LEDR, HIGH); // the led matrix in the house displays "person"
digitalWrite(5, LOW);
myDisplay.setTextAlignment(PA_CENTER);
myDisplay.print("Person");
delay(100);
} else {
digitalWrite(LEDG, HIGH);
digitalWrite(LEDR, LOW);
}

TF_LITE_REPORT_ERROR(error_reporter, "Person score:%d No person score:%d",
person_score, no_person_score);
}

#endif // ARDUINO_EXCLUDE_CODE

Working of the Firmware:

This is the complete setup of the firmware designed on Fritzing.

This simulation shows the capture of data by the Arducam and similarly classification of this data by Arduino 33 BLE Sense

This model comprises of the following firmware used:

  • Arduino 33 BLE sense - Used to process the data gathered, classifies the data processes, sends the command according to the logic fed.
  • Buzzer - For Alerting when a person is at the door.
  • Arducam Mini 2mp plus - Continuous Raw data image accumulation from source.
  • Adafruit lithium ion charger - Used to deliver charge through the lithium battery
  • Lithium ion Battery - power source
  • MAX7219 4 in 1 display - Used for displaying "person" on the display screen.

Additional Features: Using the existing intercom system, it is possible to add a servo to push the button to view the person who is standing at the door as shown in the image:

This can be an additional setup in intercom system to switch on video when a person is detected. However, this additional system has to be deployed on the Existing Intercom system.

Additional code added to the existing code:

// Switch on the green LED when a person is detected,
// the red when no person is detected
if (person_score> no_person_score) {
digitalWrite(LEDG, LOW); // if a person is detected at the door, the buzzer switches on
digitalWrite(LEDR, HIGH); // the led matrix in the house displays "person"
digitalWrite(5, LOW);
myDisplay.setTextAlignment(PA_CENTER);
myDisplay.print("Person");
servo_8.write(0); // this switches on the intercom by rotating servo
delay(500);
servo_8.write(180); // this switches off the intercom by rotating servo
delay(100);
} else {
digitalWrite(LEDG, HIGH);
digitalWrite(LEDR, LOW);

I have added an additional function which rotates the servo to switch on the existing intercom and then switches back to its original place.

3rd Project:Autonomous IoT based person temperature sensing automation:

The temperature sensor for Arduino is a fundamental element when we want to measure the temperature of a process or of the human body.

The temperature sensor with Arduino must be in contact or close to receive and measure the heat level. That's how thermometers work.

These devices are extremely used to measure the body temperature of sick people, as the temperature is one of the first factors that change in the human body, when there is an abnormality or disease.

One of the diseases that alter the temperature of the human body is COVID 19. Therefore, we present the main symptoms:

  • Cough
  • Tiredness
  • Difficulty breathing (Severe cases)
  • Fever

Fever is a symptom whose main characteristic is an increase in body temperature. In this disease, we need to constantly monitor these symptoms.

The retail market has been hit with a great impact due to the pandemic. After the malls and supermarkets have re-begun, it is necessary to ensure safety of all the customers who have entered the premises. For this purpose, manual temperature checking techniques have been set up. This increases the labour and also a risk of contact between the person checking the temperature and the person whose temperature is being check. This is demonstrated as follows:

This image shows the close contact or less social distance maintained between the two persons.

There is a second flaw which is faced in manual temperature checking system:

For the temperatures which have been checked, the data of these recordings is not stored or not synchronised with an external device for monitoring the temperatures measured.

Taking in all these cons into consideration, I've come up with an Arduino based IoT solution deployed on the Arduino MKR WiFi 1010. The temperatures are measured using the Adafruit AMG8833 temperature module. Whenever a person is detected on the gate, the ultrasonic sensors, send the information to the Arduino MKR WiFi 1010 to give a command to the AMG 8833 module to take in temperature data. The module captures the data accurately and the data is projected on an IoT dashboard in real time. If an abnormal temperature reading of a person is detected, an alarm is set on so that the mall security and staff can immediately investigate upon the matter. The data collected is each given a timestamp and can be viewed on the ThingSpeak dashboard the temperature recording vs Time graph for each data.

Similarly, it can be also traced on which days and which time range, does the supermarket or the mall show abnormal temperature readings and more security measures can be implemented accordingly.

The below image shows wherethe setup can be embedded(The area where the setup can be installed)

On the entry checking gate, HC-SR04 Ultrasonic Sensors can be installed which detects the entry of a person and sends the command to the Arduino MKR WiFi 1010 if the person is detected in a 20cm range. The Arduino microcontroller passes on the same command to the AMG 8833 temperature module to read the temperature of the person. This complete process takes time from the Ultrasonic Sensor detecting the person, to sending the command to the temperature module to detect the temperature. Hence, in order to sync with the delay, the temperature module is attached a bit far way from the Ultrasonic sensor.

Whenever a person walks in, the gate is opened ( Here, in the prototype, we are using the servo to function as a gate, but in further implementation of the project, the servo will be replaced by a heavy duty gate motor and controlled via motor driver, wherein the Arduino will be sending commands ). The temperature reading of the person is taken and this reading is sent to the thingspeak IoT dashboard in real time via the Arduino WiFi 1010. For this, an active wifi connection is required in the mall premises which is usually available in most of the cases. According to Research, the body temperature of a person with fever is approximately 38.1C which is 104F. Hence, if the Temperature module detects a temperature above this threshold, the servo turns 180Degrees and the Buzzer goes on to alarm people about the Person. Similarly, security and other mall staff can reach the area in time to control the situation when such a case happens.

The Logic works in the Following way:

├── PersonTemperature Detection
├── HC-Sr04 Ultrasonic sensor
│ ├── Person Detection
│ │ ├── If( Person distance =20) ,send command
├── Arduino
MKR WiFi 1010
│ ├── if Ultrasonic sensor Sends a command;
│ │ ├── Open the gate - Servo(0)
│ │ ├── Activate the AMG8833 to take readings
│ │ ├── Send the Readings to ThingSpeak Dashboard
│ │ | ├── If Temperature> 38.1C
│ │ │ | ├── Close the gate - servo(180)
│ │ │ │ ├── Activate the Buzzer
│ │ └── ...Repeat the loop

In this way, the complete Logic of the Temperature Monitor Functions.

The circuit Diagram for the Temperature Model is given as follows:

This is the complete Firmware and setup used in the project

This simulation shows the data type captured by the AMG 8833 Thermal Cam and this data is sent to the Arduino MKR WiFi 1010 to transfer commands

This simulation shows the distance captured by ultrasonic Sensor and this command is sent to the Arduino MKR WiFi to activate the AMG 8833 thermal sensor

Instead of a servo based door opening system, I will be implementing a command system using motor driver to automate door sliding opening system as follows:

Setting up the IoT Dashboard:

Using Thingsepak IoT Dashboard Setup:

ThingSpeak™ is an IoT analytics service that allows you to aggregate, visualize, and analyze live data streams in the cloud. ThingSpeak provides instant visualizations of data posted by your devices to ThingSpeak. With the ability to execute MATLAB® code in ThingSpeak, you can perform online analysis and process data as it comes in. ThingSpeak is often used for prototyping and proof-of-concept IoT systems that require analytics.

Since, ThingSpeak was easy to setup and use, I preffered to go with that dashboard. This interface allows the user to share the dashboard the security or staff department in the mall so that they can continuously monitor the people visiting the mall, and similarly impose restrictions at certain times when they feel the temperature risk is higher.

The above image represents the versatility of the thingspeak dashboard and the creative visualisations potrayed.

Logic used in the ThingSpeak IoT data collection process

This image shows the creation of visualisations in different channels. Here, I've created a Temperature vs Time Graph in the visualisation section. The data collected by the AMG8833 sensor will be each allocated a timestamp and will be plotted on the graph to see the time for which each data is captured.

The data collected can be viewed in real time on the Public Dashboard here; ThingSpeak Temperature Dashboard

Similarly, this plot can be integrated cumulatively to a single Dashboard made open to the visitors of the mall to view the temperature data before entering the mall. If the visitors find an abnormal temperature reading at a particular day, they can prefer not to go to the supermarket or mall at that day to be safe,

The visualisation Integration data chart:

 

This chart can be embedded at a single dashboard with the readings of the charts of other malls and supermarkets in the particular area and hence, a unique visitor can check which mall is performing better than other mall in terms of safety and can prefer going to the mall where safety guidelines are followed and the people entering are not diagnosed with fever. This can help the Government help establish safety and trust within the people along with the re-opening of malls and retail sectors

The code of the following project can be either viewed in Github or in the Arduino Web editor here; Temperature-Model-code

The logic in the code goes as follows:

#include 
#include
#include
#include ESP32_Servo.h
#include
#include
Servo servo1;
int trigPin =9;
int echoPin =8;
long distance;
long duration;

WiFiMulti WiFiMulti;

const char* ssid ="JioFiber-tcz5n"; // Your SSID (Name of your WiFi) - This is a dummy name, enter your wifi ssid here
const char* password ="**********"; //I have not mentioned the password here, while running the cript, you may mention your pwd

const char* host ="api.thingspeak.com/1121969";
String api_key ="8LNG46XKJEJC89FE"; // Your API Key provied by thingspeak


Adafruit_AMG88xx amg;

Here, I have defined some of the Libraries and Firmware along with the required setup for the WiFi host and the API-Key for the temperature dashboard on thingspeak

Here's an image of the Code in progress:

The next part of the code is Setting up the required components before the actual loop begins:

These are the prerequisites before looping the actual code. I have defined the Servo pin and the Ultrasonic sensors while also begun the testing of the AMG 8833 to see if it can read data or if its connected

void setup()
{
Serial.begin(9600);
servo1.attach(7);
pinMode(trigPin, OUTPUT);
pinMode(echoPin, INPUT);// put your setup code here, to run once:
}



{
Connect_to_Wifi();
Serial.println(F("AMG88xx test"));

bool status;

// default settings
status =amg.begin();
if (!status) {
Serial.println("Could not find a valid AMG88xx sensor, check wiring!");
while (1);
}

Serial.println("-- Thermistor Test --");

Serial.println();

delay(100); // let sensor boot up
}

The next part is the Void Loop where the complete function is carried out. Here, Initially I have set the gate to open to allow the entry of each person. The AMG 8833 performs data collection and reads the temperature of the people coming inside. If the temperature is higher than the expected threshold, the gate is closed and an alarm(buzzer) is set on to alert people and not allow the entry of the person

void loop() 
ultra();
servo1.write(0);
if(distance <=20){
Serial.print("Thermistor Temperature =");
Serial.print(amg.readThermistor());
Serial.println(" *C");

Serial.println();
// call function to send data to Thingspeak
Send_Data();
//delay
delay(50);

if(amg.readThermistor()> 38.1) // if a person with fever is detetcted, he is not allowed to enter
// a person with fever has an avg body temperature of 38.1degree celsius
servo1.write(180);
digitalWrite(6, HIGH); //Turns on the buzzer to alarm people
}

The last part is sending the data to the ThingSpeak Dashboard:

Here, since I have only one field in the channel, all the data will be sent to that field. The captured, amg.readThermistor() data is sent to the dashboard.

void Send_Data()
{

Serial.println("Prepare to send data");

// Use WiFiClient class to create TCP connections
WiFiClient client;

const int httpPort =80;

if (!client.connect(host, httpPort)) {
Serial.println("connection failed");
return;
}
else
{
String data_to_send =api_key;
data_to_send +="&field1=";
data_to_send +=string(amg.readThermistor());
data_to_send +="\r\n\";

client.print("POST /update HTTP/1.1\n");
client.print("Host:api.thingspeak.com\n");
client.print("Connection:close\n");
client.print("X-THINGSPEAKAPIKEY:" + api_key + "\n");
client.print("Content-Type:application/x-www-form-urlencoded\n");
client.print("Content-Length:");
client.print(data_to_send.length());
client.print("\n\n");
client.print(data_to_send);

delay(200); // reduced delay to perform real time data collection
}

client.stop();

}

This ends the code section of the project and we move on to explanation of the use and GO TO MARKET part of the project

The above image shows the implementation of the methodology and model in supermarkets and malls.

GO TO MARKET &PRACTICALITY:

  • Malls and Supermarkets can use this to identify Abnormal Temperature data and this data can be observed even for a certain day at a certain point of time.
  • Implement Strategies using this data to ensure Safety and Compliance.
  • Decrease Labour and automate Temperature Monitoring Process
  • Offer Dashboard to the visitors to monitor if the mall is safe and and accordingly visit the mall at the safest point of time.
  • This product can be used to ensure the visitors that the mall is a safe place and hence, can increase the sales and visits following Government guidelines
  • Companies offering IoT based solutions can invest in this product for mass production and distribution.
  • The more the supermarkets using this product, the more the access to data to the government and more the choice to customers to select the preferable safest place in their locality.
  • Comparatively affordable solution as compared to manual temperature monitoring as it decreases the labour cost + decreases the rate of infection when compared to manual monitoring where the person taking temperature has to be close to the visitor to capture the temperature.

4th Project:TinyML &IoT based queue monitoring and establishing system deployed on the Arduino 33 BLE Sense:

Just as the mall and the retail sector has opened, queue management in malls and supermarkets has become a big problem. Due to the pandemic, restricting only a certain amount of people to go inside the mall has has to be followed by the malls to ensure safety compliance. But this is done manually which increases labour work. Also the data for the number of people inside the mall at a given point of time is not available to the visitors of the mall. If this data would have been made available to the the visitors of the mall, it would increase the percentage of people visiting the mall.

Trying to run a shop or a service during the ongoing Corona crisis is certainly a challenge. Serving customers while keeping them and employees safe is tricky, but digital queuing can help a lot in this regard. The technologies behind virtual queues are not entirely new; the call for social distancing just highlights some of the many benefits they offer.

Most countries have introduced legal measures to combat the spread of COVID-19. To ensure customer satisfaction whilst adhering to the new regulations, one thing is for sure:long queues and crowded lobbies need to go. Digital queuing (also referred to as virtual or remote queuing) technology allows businesses to serve their customers in a timely manner while they stay out of harm’s way.

Generally speaking, you will likely find one or more of the following types of queue management solutions in a given retail environment:

  • Structured queues: Lines form in a fixed, predetermined position. Examples are supermarket checkouts or airport security queues.
  • Unstructured queues: Lines form naturally and spontaneously in varying locations and directions. Examples include taxi queues and waiting for consultants in specialist retail stores.
  • Kiosk-based queues: Arriving customers enter basic information into a kiosk, allowing staff to respond accordingly. Kiosks are often used in banks, as well as medical and governmental facilities.
  • Mobile queues: Rather than queuing up physically, customers use their smartphones. They do not have to wait in the store but rather can monitor the IoT Dashboard to see wait time at the store.

Long queues, whether they are structured or unstructured, often deter walk-in customers from entering the store. Additionally, they limit productivity and cause excess stress levels for customers and staff.

Does effective queue management directly affect the customer experience?

There is an interesting aspect about the experience of waiting in line:The waiting times we perceive often do not correspond with the actual times we spent in line. We may attribute a period of time falsely to be “longer” than normal or deem another period “shorter” despite it actually exceeding the average waiting time. For the most part, this has to do with how we can bridge the time waiting.

“Occupied time (walking to baggage claim) feels shorter than unoccupied time (standing at the carousel). Research on queuing has shown that, on average, people overestimate how long they’ve waited in a line by about 36 percent.”

The main reason that a customer is afraid to visit any supermarket is the problem of insufficient data. Visitors do not know the density of people inside the mall. The higher the number of people inside the mall, the higher is the risk of visiting the mall. Manually calculating the number of people who go enter and exit the mall and updating this data in real time is not possible. Also, a vistor does not know which day and at which time is is best suited to visit the mall. The visitor also does not know the wait time of each mall so that he can go with other supermarkets nearby if the wait time over there is less. As a result, all this leads to less conversion of people and accordingly less number of people visiting the mall. If the visitors have the access to population data, they have a sense of trust and leads to increase in sales of malls. Hence, I've come up with an Arduino Based TinyML and IoT solution to make this data available to the visitors and also increase the conversion of visitors in the mall by following the necessary safety guidelines. If the visitors have the access to population data, they have a sense of trust and leads to increase in sales of malls. Hence, I've come up with an Arduino Based TinyML and IoT solution to make this data available to the visitors and also increase the conversion of visitors in the mall by following the necess

This solution is based on computer vision and person detetction algorithm based on tensorflow framework.

It functions in the following way :

This solution is implemented on the gates of the mall or the supermarket. The Arducam mini 2mp keeps capturing image data and sends it to the Arduino 33 BLE Sense to process and classify this data. If a person is detected, The arduino increases the count of the stored data of number of people inside the mall by 1. Since a person is detected, the Servo motor rotates, opening the entrance to allow the person inside. For each person allowed inside, the data is sent to the ThingSpeak IoT dashboard which is open for the vistors to view.

When the person count increases the 50 threshold limit(This threshold can be altered depending upon the the supermarket size), the gate is closed and a wait time of 15min is set until the customers inside exit the store. The wait time is then displayed on the LED matrix display screen so that the customers in the queue can know the duration they have to wait for.

The people can also keep a track of the number of people inside the store. The number of people allowed to enter inside the store at a single time is 50 people.

The physical queuing unit of this product, helps in establishing queues while the IoT Dashboard helps in projecting the total count of the customers going in the store and total count of the customers going out of the store. Currently this is the dashboard data that will be displayed but I am working on a logic for displaying the waiting time required for the customer to get inside the mall. The logic for it is pretty simple, it depends on the number of people inside the mall. The total no of people entering the mall displayed on dashboard minus the total no of people exited the mall displayed on the dashboard. This will give us an outcome of the total no of people inside the store. The next operation would be the total no of people outside the store subtracted by the threshold (limit that the store can accommodate). If the outcome is a negative integer, the waiting time would be multiplication of the negative integer by the negative of the average time a person spends inside the store. If the outcome of the operation would be a positive integer, the wait time would be none.

Heading towards the implementation of the physical queuing system:

The following Softwares have been used in designing this model:

  • TensorFlow lite
  • ThingSpeak
  • Arduino Web Editor

In this person detection model, I have used the Pre-trained TensorFlow Person detection model apt for the project. This pre-trained model consists of three classes out of which the third class is with undefined set of data:

"unused",

"person",

"notperson"

In our model we have the Arducam Mini 2mp plus to carry out image intake and this image data with a decent rate of fps is sent to the Arduino Nano 33 BLE Sense for processing and and classification. Since the Microcontroller is capable of providing 256kb RAM, we change the image size of each image to a standard 96*96 for processing and classification. The Arduino Tensorflow Lite network consists of a deep learning framework as:

  • Depthwise Conv_2D
  • Conv_2D
  • AVERAGE Pool_2D
  • Flatten layer

This deep learning framework is used to train the Person detection model.

The following is the most important function defined while processing outputs on the Microcontroller via Arduino_detetction_responder.cpp

// Process the inference results.
uint8_t person_score =output->data.uint8[kPersonIndex];
uint8_t no_person_score =output->data.uint8[kNotAPersonIndex];
RespondToDetection(error_reporter, person_score, no_person_score);

In the following function defining, the person_score , the no_person_score have been defined on the rate of classification of the data.

using these defined functions, I will be using it to give certain outputs on the basis of confidence of the person-score and the no_person_score .

The detection responder logic of the code works in the following way:

├── Person Detection and responder - Entry
├── Arducam mini 2MP Plus
│ ├──Image and Video Data to Arduino
├── Arduino BLE 33 Sense
│ ├── processing and classification of the input data
│ │ ├── If person detected, open the gate - servo(180)
│ │ ├── If no person detected, close the gate - servo(0)
│ │ ├── Send the number of people entered count to to ThingSpeak Dashboard via ESP8266 -01
│ │ | ├── If people count has exceeded 50
│ │ │ | ├── Close the gate &wait for 15min to let the people inside move out
│ │ │ │ ├── Display wait time on a LED Matrix
│ │ └── ...Repeat the loop

├── Person Detection and responder - Exit
├── Arducam mini 2MP Plus
│ ├──Image and Video Data to Arduino
├── Arduino BLE 33 Sense
│ ├── processing and classification of the input data
│ │ ├── If person detected, open the gate - servo(180)
│ │ ├── If no person detected, close the gate - servo(0)
│ │ ├── Send the number of people entered count to to ThingSpeak Dashboard via ESP8266 -01
│ │ └── ...Repeat the loop

Adhering to the logic used in the model, the Arducam mini 2mp plus will continuously capture Image data and sends this data to the Arduino 33 BLE Sense to process and classify the data. The overall model size is 125KB. If a person has been detected, the Arduino sends the command to the servo to rotate to servo to 180degree. If a person is not detected, the servo is rotated to 0degree and the gate is closed. Each time a person is detected, the the count increments by 1. If the count exceeds the 50 threshold, no more person is allowed inside and a wait time of 15min is set.

The wait time is continuously displayed and updated on the LED Matrix dislplay.

This count is also displayed on the ThingSpeak IoT dashboard via the ESP8266 01

Through the Dashboard, an individual can easily view the number of people are inside at a given day at a given point of time.

At the exit gate, the same logic is set. If a person is detected, the gate is opened while if no person is detected, the gate is closed. Each time a person is detected, the count increases by 1. This count is displayed on the ThingSpeak IoT dashboard.

In this way one can monitor the number of people entering and the number of people exiting.

Since the two models for entry and exit are deployed on two different microcontrollers, calculating the average wait time based on data from different microcontroller is a bit hard, but this uses a simple logic function.

x =No of people who have entered

Y =No of people who have exited

X - Y =No of people who are inside the mall

Z =Threshold of the no of people who are allowed to be inside the mall

let Z-(X-Y) =count {this is the number of people (in negative) who have either crossed the threshold limit or are below the threshold limit

If "count" is negative, The wait time is equal to count*(the negative of the average time a person spends inside the mall)

if "count" is positive, the wait time is zero

In this way, the average queue time calculating algorithm is imposed.

Working of the Firmware:

This is the complete setup of the firmware designed on Fritzing.

This model comprises of the following firmware used:

  • Arduino 33 BLE sense - Used to process the data gathered, classifies the data processes, sends the command according to the logic fed.
  • MG959 / MG995 Servo - Heavy duty servo( An external power supply may be applied) - To open and close the gates as per microcontroller command.
  • Arducam Mini 2mp plus - Continuous Raw data image accumulation from source.
  • Adafruit lithium ion charger - Used to deliver charge through the lithium battery
  • Lithium ion Battery - power source
  • ESP8266 - 01 - Used for sending data to the ThingSpeak dashboard via WiFi network.
  • MAX7219 4 in 1 display - Used for displaying the wait time on the display screen.

Functioning and Working of Logic in Code:

The following are the Libraries included in themain.ino code for functioning of the model.

#include 

#include "main_functions.h"

#include "detection_responder.h"
#include "image_provider.h"
#include "model_settings.h"
#include "person_detect_model_data.h"
#include "tensorflow/lite/micro/kernels/micro_ops.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"

In the following code snippet, the loop is defined and performed. Since this is the main.ino code, it controls the core functioning of the model - used to run the libraries in the model.

void loop() {
// Get image from provider.
if (kTfLiteOk !=GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
input->data.uint8)) {
TF_LITE_REPORT_ERROR(error_reporter, "Image capture failed.");
}

// Run the model on this input and make sure it succeeds.
if (kTfLiteOk !=interpreter->Invoke()) {
TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed.");
}

TfLiteTensor* output =interpreter->output(0);

// Process the inference results.
uint8_t person_score =output->data.uint8[kPersonIndex];
uint8_t no_person_score =output->data.uint8[kNotAPersonIndex];
RespondToDetection(error_reporter, person_score, no_person_score);
}

In the following code snippet, the necessary libraries required to inference the image to be captured is displayed. The images after captured are converted to a 96*96 standardised size which can be interpreted on the arduino board.

Here, the Arducam mini 2mp OV2640 library has been utilised.

This code has been provided in the arduino_image_provider.cpp snippet

#if defined(ARDUINO) &&!defined(ARDUINO_ARDUINO_NANO33BLE)
#define ARDUINO_EXCLUDE_CODE
#endif // defined(ARDUINO) &&!defined(ARDUINO_ARDUINO_NANO33BLE)

#ifndef ARDUINO_EXCLUDE_CODE

// Required by Arducam library
#include
#include
#include
// Arducam library
#include
// JPEGDecoder library
#include

// Checks that the Arducam library has been correctly configured
#if !(defined OV2640_MINI_2MP_PLUS)
#error Please select the hardware platform and camera module in the Arduino/libraries/ArduCAM/memorysaver.h
#endif

// The size of our temporary buffer for holding
// JPEG data received from the Arducam module
#define MAX_JPEG_BYTES 4096
// The pin connected to the Arducam Chip Select
#define CS 7

// Camera library instance
ArduCAM myCAM(OV2640, CS);
// Temporary buffer for holding JPEG data from camera
uint8_t jpeg_buffer[MAX_JPEG_BYTES] ={0};
// Length of the JPEG data currently in the buffer
uint32_t jpeg_length =0;

// Get the camera module ready
TfLiteStatus InitCamera(tflite::ErrorReporter* error_reporter) {
TF_LITE_REPORT_ERROR(error_reporter, "Attempting to start Arducam");
// Enable the Wire library
Wire.begin();
// Configure the CS pin
pinMode(CS, OUTPUT);
digitalWrite(CS, HIGH);
// initialize SPI
SPI.begin();
// Reset the CPLD
myCAM.write_reg(0x07, 0x80);
delay(100);
myCAM.write_reg(0x07, 0x00);
delay(100);
// Test whether we can communicate with Arducam via SPI
myCAM.write_reg(ARDUCHIP_TEST1, 0x55);
uint8_t test;
test =myCAM.read_reg(ARDUCHIP_TEST1);
if (test !=0x55) {
TF_LITE_REPORT_ERROR(error_reporter, "Can't communicate with Arducam");
delay(1000);
return kTfLiteError;
}

The final part where in the complete model is controlled is the Arduino_detection_responder.cpp.

This is a small code snippet of the entire logic used. When the confidence score of a person is greater than the confidence score of no person, the gate is opened and it is assumed that the person is detected. For this purpose the servo is moved to 0Degree to open the gate. On the detection of a person, the count is incremented by 1 =initially which began at 0. This count indicates the number of people coming inside. The num value of the count is sent to the ThingSpeak IoT dashboard which represents the number of people entering. When the count reaches the value of 50; the gate is closed and a wait time of 15min is imposed on the queue. The gate is closed and wait time is imposed each time 50 people enter. For this a logic of multiples of 50 is set

// Switch on the green LED when a person is detected,
// the red when no person is detected
if (person_score> no_person_score) {
digitalWrite(LEDG, LOW);
digitalWrite(LEDR, HIGH);
servo_8.write(0); // this servo moves to 0degrees in order to open the mall door when a person is detected to ensure no touch entry system
count++

} else {
digitalWrite(LEDG, HIGH);
digitalWrite(LEDR, LOW);
servo_8.write(180); // this servo moves to 180degrees when no person is detected
}

TF_LITE_REPORT_ERROR(error_reporter, "Person score:%d No person score:%d",
person_score, no_person_score);
}


// Now we have let in 50people inside the store, so we set a delay of 15min wait time for others waiting outside to let them in
// Displaying wait time on the screen every 1min

if(count =y*50) // when people are detected in multiples of 50, we instruct it to start displaying wait time to other people to wait until 15 min
myDisplay.setTextAlignment(PA_CENTER);
myDisplay.print("Waiting 15min");
delay(60000);

Setting up the ThingSpeak Dashboard:

Since the features in Thingspeak Dashboard are limited, I will not be implementing the Time prediction algorithm right now but I am working on the logic to communicate and write data from the Dashboard to microcontroller to perform the time calculation algorithm.

In the ThingSpeak Dashboard, I have added two fields; one for entry and the other one for exit.

The co-ordinates for the store or mall for which the queuing system is displayed, is also added in the form of a map.

The data displayed for the first field is gathered through the entry responding logic and the data displayed for the second field is gathered through the exit responding logic.

This is the snip of the two different logics used in the model.

The ThingSpeak Dashboard can be made available to the the staff of the store to check the number of people entering the store in real time and the number of people exiting the store in real time. This data can also be observed to see the analysis of the data at a given day at a a given time to check and impose further restrictions if required if the limit of people in the store exceeds the expected number of people at a given day

This Dashboard can be viewed here:IoT Dashboard

The following represents the field created for this purpose.

Now, a question might arise that for person detection model that this model can be replaced by ultrasonic or infrared sensors. Flaws in Ultrasonic Sensors or Infrared Based Sensors:These sensors are not exactly accurate and for the real time person count display, these sensors may provide wrong readings. Also, these sensors add as additional hardwares while in Go to Market Solutions, the person detection algorithm can be implemented in existing cameras and can reduce hardware cost. The data from these cameras could be sent to the Arduino BLE sense for central Classification and data processing.

GO TO MARKET &PRACTICALITY:

  • Malls and Supermarkets can use this to identify The count of people entering and exiting the mall in real time.
  • Implement Strategies using this data to ensure Safety and Compliance with efficient Queue management algorithms.
  • Decrease Labour and automate Queue Management Process
  • Offer Dashboard to the visitors to monitor the density of people inside the mall and accordingly visit the mall at the safest point of time.
  • This product can be used to ensure the visitors that the mall is a safe place and hence, can increase the sales and visits following Government guidelines
  • Companies offering Ai and IoT based solutions can invest for mass production and distribution.
  • The more the supermarkets using this product, the more the access to data to the government and more the choice to customers to select the preferable safest place in their locality along with the the queue time required for each store can be monitored. This will lead to a wide range of options of supermarkets in the locality comparing the queue time and safety.
  • Comparatively affordable solution as compared to manual queuing system and updating information manually to the Dashboard.
  • Utilize real-time CCTV footage to impose Queue management in a mall/shop through person detection in terms of timely trends and spatial analysis of person density in the mall.
  • Enable Stores to make better, data-driven decisions that ensure your safety and efficient Queues based on autonomous queuing system.

Github Code:Arduino Autonomous TinyML and IoT based queuing system.

Addition to the Existing Person Detection Algorithm- Mask Detection System:

Mask Detection Model based on TinyML :

Dr. Kierstin Kennedy, chief of hospital medicine at the University of Alabama at Birmingham, said, “Masks can protect against any infectious illness that may be spread by droplets. For example, the flu, pertussis (whooping cough), or pneumonia.”

Adding that wearing a cloth mask has benefits beyond slowing the spread of COVID-19, and that source control can reduce the transmission of many other easily spread respiratory infections — the kind that typically render people infectious even before they display symptoms, like influenza.

Until the threat of this pandemic has been neutralized, people should embrace the protection masks allow them to provide to those around them.

After all, it’s not necessarily about you — it’s about everyone you come in contact with.

It’s not at all uncommon to be an asymptomatic carrier of the new coronavirus — which means that even if you have no symptoms at all, you could potentially transmit the virus to someone who could then become gravely ill or even die.

Adhering to this, I decided to increase the necessity of wearing face-masks along with touch-free systems to increase safety in malls and supermarkets. Along with the person detection algorithm, I decided to make a custom face mask detection model which detects face masks and displays this data on the ThingSpeak IoT dashboard to increase awareness among mall staff as well as the visitors coming inside so that they are aware of the time trends when the most number of people are without masks. Through this there is a increases of sense of warning and awareness in people to wear masks. Accordingly, the store staff can keep a monitor on these trends and increase restrictions based on data driven statistics.

Deciding upon the Logic and Dataset of the Model:

This is an overall Logic used in most of face mask detection algorithms. Since, we are deploying this model to an Arduino 33 BLE Sense, the deployment process of this model will vary.

There are two steps involved in constructing the model for Face Mask Detection.

  • Training: Here we’ll focus on loading our face mask detection dataset from disk, training a model (using Keras/TensorFlow) on this dataset, and then serializing the face mask detector to disk
  • Deployment: Once the face mask detector is trained, we can then move on to loading the mask detector, performing face detection, and then classifying each face as mask or no_mask

Dataset used in training this model:

The dataset used in this process consists of 3500 images but to reduce the size of the model, and feed in accurate model images, I have used 813 images to increase accuracy of the model by decreasing bulk size. This model is an average 676K in size and utilizes nearly 440.3K Ram. Since this model is the optimized version of the original model, the accuracy of the model is 87.47% as compared to 98.15% in the non-optimized one.

The following Softwares have been used in designing this model:

  • TensorFlow lite
  • ThingSpeak
  • Arduino Web Editor

Heading towards designing the model in EdgeImpulse Studio:

Powerful deep learning models (based on artificial neural networks) are now reaching microcontrollers. Over the past year great strides were made in making deep learning models smaller, faster and runnable on embedded hardware through projects like TensorFlow Lite for Microcontrollers, uTensor and Arm’s CMSIS-NN; but building a quality dataset, extracting the right features, training and deploying these models is can still be complicated.

Using Edge Impulse you can now quickly collect real-world sensor data, train ML models on this data in the cloud, and then deploy the model back to your Arduino device. From there you can integrate the model into your Arduino sketches with a single function call.

Step 1 - Acquisition of Data in the Edge Impulse Studio:

Using the dataset of 3500 images, I filtered these images to the best performing images and finally fed in 813 images totally in the Training Data and 487 images in the testing Data. I labelled these classes as mask and no_mask.

Then, I went ahead creating an impulse design which best suited the Model type. For optimal accuracy its recommended to use a standard image size which is 96*96 and also works the best on the Arduino 33 BLE Sense. Since the input type was images, I went ahead and selected "images" in the processing block. For the transfer learning block, the type recommended for image learning is Transfer Learning (Images) which is a Fine tune a pre-trained image model on your data. with Good performance even with relatively small image datasets.

The next step was saving the parameters based on color depth. Here, I have selected RGB because in the dataset I am using for mask detection, color is also an important feature of classification instead of grayscale. In this page, we can also see the raw features with the processed features of the image

After Feature Generation I obtained the classification graph or the feature explorer where I could see the classes based on their classifications. The blue dots represent mask images and the orange dots represent the no_mask images.

In this feature generation, I obtained a fair classification with a distinct classification plot.

Finally, Moving on to the Transfer Learning Plot:

Here, I set the number of training cycles/epochs as 30 to get the highest accuracy with minimum val_loss. It so happens that if we train the model based on many training cycles, the accuracy graph starts to decrement after a certain number of epochs and the val_loss increases. Therefore I decided to limit the epochs to 30 which proved to be perfect. The learning rate is set to 0.0005 which is the default and proves to be the most appropriate. Here, I have used the MobileNetV2 0.35 (final layer:16 neurons, 0.1 dropout) model because this model is comparitively lightweight and accurate.

Finally after completing 30 epochs and 10 epochs of best model performance, I got the accuracy heading to 1.00 and the loss nearly 0 which was 0.0011. The following was the ouput during the training process:

Saving best performing model... Converting TensorFlow Lite float32 model... Converting TensorFlow Lite int8 quantized model with float32 input and output...

  • Epoch 9/10 21/21 - 5s - loss:0.0011 - accuracy:1.0000 - val_loss:0.0727 - val_accuracy:0.9755
  • Epoch 10/10 21/21 - 5s - loss:0.0012 - accuracy:1.0000 - val_loss:0.0728 - val_accuracy:0.9755 Finished training

This was the final output which I received after the training:

The accuracy to be 92.6% and the loss to be 0.19.

Finally Using the test data, I tested the accuracy and found it to be 98.15%.

Finally since I had the model ready, I deployed it as an Arduino Library with the firmware to be the Arduino 33 BLE Sense:I got the zip folder of the library ready and started to make changes as per our requirements.

For a last confirmation, I live classified the data to ensure that the data is classified properly. I got the perfect results as expected:

Changing the code as per the output required in the model:

Here is a snippet of the main.ino code of the mask_detection model

I have defined the arduino libraries required for the functioning of the model and based on the Tensorflow lite framework, I have designed this model.

This is the loop of some of the main functions in the model which are defined in the libraries. The main.ino code centralises these functions and accordingly loops them with a central code.

void loop() {
// Get image from provider.
if (kTfLiteOk !=GetImage(error_reporter, kNumCols, kNumRows, kNumChannels,
input->data.uint8)) {
TF_LITE_REPORT_ERROR(error_reporter, "Image capture failed.");
}

// Run the model on this input and make sure it succeeds.
if (kTfLiteOk !=interpreter->Invoke()) {
TF_LITE_REPORT_ERROR(error_reporter, "Invoke failed.");
}

TfLiteTensor* output =interpreter->output(0);

// Process the inference results.
uint8_t mask_score =output->data.uint8[kmaskIndex];
uint8_t no_mask_score =output->data.uint8[kno-maskIndex];
RespondToDetection(error_reporter, mask_score, no_mask_score);
}

The processed data of this model can be viewed here :(This file is relatively large and varies as per the dataset size of the model) Arduino_mask_detect_model_data.h

For providing image, I will be using the Arducam mini 2mp plus for visual data input. A snippet from the image_provider.h file is :

#include "image_provider.h"

/*
* The sample requires the following third-party libraries to be installed and
* configured:
*
* Arducam
* -------
* 1. Download https://github.com/ArduCAM/Arduino and copy its `ArduCAM`
* subdirectory into `Arduino/libraries`. Commit #e216049 has been tested
* with this code.
* 2. Edit `Arduino/libraries/ArduCAM/memorysaver.h` and ensure that
* "#define OV2640_MINI_2MP_PLUS" is not commented out. Ensure all other
* defines in the same section are commented out.
*
* JPEGDecoder
* -----------
* 1. Install "JPEGDecoder" 1.8.0 from the Arduino library manager.
* 2. Edit "Arduino/Libraries/JPEGDecoder/src/User_Config.h" and comment out
* "#define LOAD_SD_LIBRARY" and "#define LOAD_SDFAT_LIBRARY".
*/

#if defined(ARDUINO) &&!defined(ARDUINO_ARDUINO_NANO33BLE)
#define ARDUINO_EXCLUDE_CODE
#endif // defined(ARDUINO) &&!defined(ARDUINO_ARDUINO_NANO33BLE)

#ifndef ARDUINO_EXCLUDE_CODE

// Required by Arducam library
#include
#include
#include
// Arducam library
#include
// JPEGDecoder library
#include

// Checks that the Arducam library has been correctly configured
#if !(defined OV2640_MINI_2MP_PLUS)
#error Please select the hardware platform and camera module in the Arduino/libraries/ArduCAM/memorysaver.h
#endif

The Arduino_detection_responder.cpp code performs the inference and delivers the main output required for the code. Here, when a person with a mask is detected, we are opening the gate and if a person with no mask is detected, we are closing the gate.

// Switch on the green LED when a mask is detected,
// the red when no mask is detected
if (mask_score> no_mask_score) {
digitalWrite(LEDG, LOW);
digitalWrite(LEDR, HIGH);
servo_8.write(0); // this servo moves to 0degrees in order to open the mall door when a person is detected to ensure no touch entry system
count++
} else {
digitalWrite(LEDG, HIGH);
digitalWrite(LEDR, LOW);
servo_8.write(180); // this servo moves to 180degrees when no person is detected
count2++
}

The count and count2 are variable integers and increment everytime a mask is detected, or not detected. These counts are then displayed onto the ThingSpeak IoT Dashboard as follows:

The graph displays the mask and no mask count over time. This thingspeak dashboard is setup in the Arduino code as seen here:

#include 
#include
WiFiMulti WiFiMulti;
const char* ssid ="Yourssid"; // Your SSID (Name of your WiFi)
const char* password ="Wifipass"; //Your Wifi password
const char* host ="api.thingspeak.com/channels/1118220";
String api_key ="9BRPKINQJJT2WMWP"; // Your API Key provied by thingspeak

The complete code for responder can be viewed here:Arduino_detection_responder.cpp

Github Code can be viewed here:Arduino Mask Detetction

The ThingSpeak dashboard can be viewed here - IoT Dashboard

5th project - Person detection in supermarket Aisles and self-sanitisation system:

Just as malls and supermarkets and retail sectors have opened, a risk of contamination of the virus has increased. The main risk that is faced is in supermarkets and stores. The stores could be related to clothes, food and even electronics, the virus could deposit and stay on these surfaces for a long time.

Early data suggests the new coronavirus can live on surfaces for several days

The new coronavirus is, well, new — and there's still much to learn about how easily the virus can spread via contaminated surfaces. But early evidence indicates that the surface survivability of the new coronavirus is similar to that of SARS, a related coronavirus first identified in 2002. Depending on the surface, the virus can live on surfaces for a few hours or up to several days.

The new coronavirus seems to be able to survive the longest on plastic and stainless steel — potentially as long as three days on these surfaces. It can also live on cardboard for up to 24 hours.

Find out what this means for the things you touch throughout the day, including your:

  • Clothes
  • Food
  • Groceries
  • Packages
  • Electronics

The coronavirus pandemic has breathed new life into a decades-old technique that can zap viruses and bacteria:ultraviolet light.

Hospitals have been using it for years to cut down on the spread of drug-resistant superbugs and to disinfect surgical suites. But there is now interest in using the technology in spaces like schools, office buildings, and restaurants to help reduce coronavirus transmission once public spaces are open again.

The sanitizing effects of UV lights have been seen with other coronaviruses, including the one that causes severe acute respiratory syndrome (SARS). Studies have shown that it can be used against other coronaviruses. One study found at least 15 minutes of UVC exposure inactivated SARS, making it impossible for the virus to replicate. New York's Metropolitan Transit Authority announced the use of UV light on subway cars, buses, technology centers, and offices. The National Academy of Sciences says although there is no concrete evidence for UV’s effectiveness on the virus that causes COVID-19, it has worked on other similar viruses, so it would likely fight this one too.

Abiding by the given information, Malls and Supermarkets have started imposing solutions based on UV sanitisation on manual basis. This consumes a lot of time and also, the data is unavailable. In manual process, one does not know which area has been exposed to human touch more or which are has the highest risk of contamination.

To automate this process and to provide real time spatial analysis of the data collected and the rate of contamination in an area.

The above images demonstrate the distribution of population across the mall. As it is clearly seen in Figure B, the density near the escalator is comparatively high as compared to the density on the second floor. Manually keeping a track of this continuously changing data in real time and accordingly, sanitising the areas/aisles according to the rate of contamination is not a manual task.

Hence, I decided to make a robust solution for automating this process with the help of TinyML computer Vision &IoT deployed on the Arduino 33 BLE Sense.

The structured framework of this system is as follows:

These models are individually deployed on the Aisles and Areas of the malls and supermarkets close to the Area which has to be sanitised. Since the model utilises person detection Algorithm, the placement of the model is done accordingly.

In this model, for accumulation of Video data, the Arducam mini 2MP Plus is used. The Arducam Mini 2MP Plus continuously gathers visual data and sends this data to the Arduino Nano 33 BLE Sense for processing and classification. The Arduino classifies the data and accordingly processes commands. If a person is detected, the count of people increments by 1 on detection of each person near the Aisle.

A certain threshold for the count of people is set depending upon the rate of contamination based on the object type in that area. The rate of contamination and threshold for food aisle, clothes aisle, sports aisle and Electronic aisle is different.

Accordingly taking into consideration an average threshold, if 25 people are detected, the area is safe. If the count increases to 50, the area is heading towards contamination and the visitors are given an alert. If the count increases to 100, the area is declared contaminated and the people are warned to be careful while touching objects. Finally if the threshold limit is crosses which is 150, the area is autonomously sanitised using UV Light.

The alerts generated are based on LED Colours. Green is an indication for safe, blue is an indication for alert, and Red is an indication for Warning!. These LED based alerts are installed in the respective areas individually.

The above image shows the various aisles in the malls where the system can be installed.

The above image shows the area of installation of Arducam or similar visual data capturing device to cover a wide spectrum of people walking through the aisle.

The above Images show a demo implementation of the UV Sanitisation Lights in suitable areas of the aisles. This UV light covers a large spectrum of area that can be sanitised together.

Implementation of the autonomous sanitisation system.

The following Softwares have been used in designing this model:

  • TensorFlow lite
  • ThingSpeak
  • Arduino Web Editor

In this person detection model, I have used the Pre-trained TensorFlow Person detection model apt for the project. This pre-trained model consists of three classes out of which the third class is with undefined set of data:

"unused",

"person",

"notperson"

In our model we have the Arducam Mini 2mp plus to carry out image intake and this image data with a decent rate of fps is sent to the Arduino Nano 33 BLE Sense for processing and and classification. Since the Microcontroller is capable of providing 256kb RAM, we change the image size of each image to a standard 96*96 for processing and classification. The Arduino Tensorflow Lite network consists of a deep learning framework as:

  • Depthwise Conv_2D
  • Conv_2D
  • AVERAGE Pool_2D
  • Flatten layer

This deep learning framework is used to train the Person detection model.

The following is the most important function defined while processing outputs on the Microcontroller via Arduino_detetction_responder.cpp

// Process the inference results.
uint8_t person_score =output->data.uint8[kPersonIndex];
uint8_t no_person_score =output->data.uint8[kNotAPersonIndex];
RespondToDetection(error_reporter, person_score, no_person_score);

In the following function defining, the person_score , the no_person_score have been defined on the rate of classification of the data.

using these defined functions, I will be using it to give certain outputs on the basis of confidence of the person_score and the no_person_score .

The detection responder logic of the code works in the following way:

├── Person Detection and sanitisation
├── Arducam mini 2MP Plus
│ ├──Image and Video Data to Arduino
├── Arduino BLE 33 Sense
│ ├── processing and classification of the input data
│ │ ├── If person detected, increment the count by 1
│ │ ├── If no person detected, do nothing
│ │ ├── Send the number of people entered count to to ThingSpeak Dashboard via ESP8266 -01
│ │ | ├── If people count is uptil 25
│ │ │ | ├── Indicate the area to be safe by flashing green light

│ │ | ├── If people count is uptil 50
│ │ │ | ├── Indicate the people to be aware by flashing blue light

│ │ | ├── If people count is uptil 100
│ │ │ | ├── Indicate the area to be contaminated by flashing Red light

│ │ | ├── If people count is between 150 to 175
│ │ │ | ├── Sanitise the area by activating the UV light

│ │ │ | ├── Reset the person count to 0 since the area has been sanitised
│ │ └── ...Repeat the loop

According to the logic used in the model, the Arducam Mini 2MP Plus will continuously capture visual data and send this data to the Arduino Nano 33 BLE sense to process and classify. This model size is 125Kb. Once the image is processed, the Arduino starts to classify the data captured. If a person is detected in the model, the person count increases by 1. This person count is continuously sent to the ThingSpeak dashboard via ESP8266- 01 IoT module. The person count enables the supermarket staff with the data of number of visitors in a particular area at any given point of time. The staff can generate data driven decisions to take action in an area if the visitor count is significantly high.

Proceeding to the output, if the person count is between 1 to 25, the Area is declared safe by flashing the green LED. If the person count is between 26 to 50, the visitors are given an alert by flashing BLUE LED. If the count is between 51 to 100, the area is declared contaminated by flashing the RED LED. When the count surpasses a certain threshold, here taken to be 150, the UV light is turned on until the 165th person passes the area. In this way the Area is sanitised. Similarly the count of people is reset to 0 since the area has been sanitised.

Working of the Firmware:

This model comprises of the following firmware used:

  • Arduino 33 BLE sense - Used to process the data gathered, classifies the data processes, sends the command according to the logic fed.
  • Arducam Mini 2mp plus - Continuous Raw data image accumulation from source.
  • Adafruit lithium ion charger - Used to deliver charge through the lithium battery
  • Lithium ion Battery - power source
  • ESP8266 - 01 - Used for sending data to the ThingSpeak dashboard via WiFi network.
  • RGB LED - Used for flashing Signals based on the status of contamination
  • UV Light - Used to Sanitise the Area ( Since this is a prototype, an LED is displayed. In the actual solution, the UV light consumes a lot of energy hence an external power source needs to be provided. )

Functioning and Working of Logic in Code:

The following are the Libraries included in themain.ino c ode for functioning of the model.

#include 

#include "main_functions.h"

#include "detection_responder.h"
#include "image_provider.h"
#include "model_settings.h"
#include "person_detect_model_data.h"
#include "tensorflow/lite/micro/kernels/micro_ops.h"
#include "tensorflow/lite/micro/micro_error_reporter.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/micro/micro_mutable_op_resolver.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"

The main outcome of the model is dependent on the logic fed in detection_responder.cpp. This snippet shows the generalized threshold of the contamination of the object set to a count of 101.

void RespondToDetection(tflite::ErrorReporter* error_reporter,
uint8_t person_score, uint8_t no_person_score) {
static bool is_initialized =false;
if (!is_initialized) {
// Pins for the built-in RGB LEDs on the Arduino Nano 33 BLE Sense
pinMode(LEDR, OUTPUT);
pinMode(LEDG, OUTPUT);
pinMode(LEDB, OUTPUT);
is_initialized =true;
}

// Note:The RGB LEDs on the Arduino Nano 33 BLE
// Sense are on when the pin is LOW, off when HIGH.

// Switch the person/not person LEDs off
digitalWrite(LEDG, HIGH);
digitalWrite(LEDR, HIGH);

// Flash the blue LED after every inference.
digitalWrite(LEDB, LOW);
delay(100);
digitalWrite(LEDB, HIGH);

// Switch on the green LED when a person is detected,
// the red when no person is detected
if (person_score> no_person_score) {
digitalWrite(LEDG, LOW);
digitalWrite(LEDR, HIGH);
count++
} else {
digitalWrite(LEDG, HIGH);
digitalWrite(LEDR, LOW);
}

TF_LITE_REPORT_ERROR(error_reporter, "Person score:%d No person score:%d",
person_score, no_person_score);
}


if (constrain(count, 1, 50)) {
digitalWrite(LEDGREEN, LOW); // This is a green LED which means area is not contaminated yet
}
if (constrain(count, 51, 75)) {
digitalWrite(LEDBLUE, LOW); // This is a b led so that people are alert that more than 50 people
}
if (constrain(count, 76,100)) {
digitalWrite(LEDRED, LOW); // This is a red LED which warns people that this area is contaminated and one should take care while touching objects
}
if (constrain(count, 101, 110)) {
digitalWrite(UV, LOW); // This is an Ultraviolet Light which ensures that after 100 people have touched the aisle objects, it sanitises the area with UV light
}
if (count> 111) {
(count =0); // this resets the person count to 0 once the area has been sanitised by UV light
}
} // In this way, a simple math function has been written by me on the arduino nano 33 ble sense which alerts people and sanitizes areas
/* cc - Dhruv Sheth */

The below snippet is carried out by the ESP8266 -01 module to send data to the allocated field in the ThingSpeak Dashboard:

void Send_Data()
{


// Use WiFiClient class to create TCP connections
WiFiClient client;

const int httpPort =80;

if (!client.connect(host, httpPort)) {
Serial.println("connection failed");
return;
}
else
{
String data_to_send =api_key;
data_to_send +="&field1=";
data_to_send +=String(count);
data_to_send +="\r\n\";

client.print("POST /update HTTP/1.1\n");
client.print("Host:api.thingspeak.com\n");
client.print("Connection:close\n");
client.print("X-THINGSPEAKAPIKEY:" + api_key + "\n");
client.print("Content-Type:application/x-www-form-urlencoded\n");
client.print("Content-Length:");
client.print(data_to_send.length());
client.print("\n\n");
client.print(data_to_send);

delay(10); // reduced delay to perform real time data collection
}

client.stop();

}

The threshold for each Aisle inside the mall is set to be different according to the rate of contamination of the model. This data can be altered according to the need of the malls or supermarkets. These thresholds can also be altered according to the time trends and the population density in that area.

Threshold set for the Clothes Aisle:

if (constrain(count, 1, 50)) {
digitalWrite(LEDGREEN, LOW); // This is a green LED which means area is not contaminated yet
}
if (constrain(count, 51, 75)) {
digitalWrite(LEDBLUE, LOW); // This is a b led so that people are alert that more than 50 people
}
if (constrain(count, 76,100)) {
digitalWrite(LEDRED, LOW); // This is a red LED which warns people that this area is contaminated and one should take care while touching objects
}
if (constrain(count, 101, 110)) {
digitalWrite(UV, LOW); // This is an Ultraviolet Light which ensures that after 100 people have touched the aisle objects, it sanitises the area with UV light
}
if (count> 111) {
(count =0); // this resets the person count to 0 once the area has been sanitised by UV light
}

Threshold for the Electronics Aisle, since the virus stays on these surfaces comparatively less, we have set a higher threshold in this case.

if (constrain(count, 1, 75)) {
digitalWrite(LEDGREEN, LOW); // This is a green LED which means area is not contaminated yet
}
if (constrain(count, 76, 180)) {
digitalWrite(LEDBLUE, LOW); // This is a b led so that people are alert that more than 50 people
}
if (constrain(count, 181,225)) {
digitalWrite(LEDRED, LOW); // This is a red LED which warns people that this area is contaminated and one should take care while touching objects
}
if (constrain(count, 225, 240)) {
digitalWrite(UV, LOW); // This is an Ultraviolet Light which ensures that after 100 people have touched the aisle objects, it sanitises the area with UV light
}
if (count> 111) {
(count =0); // this resets the person count to 0 once the area has been sanitised by UV light
}

Threshold set for Food and eatables:, Since these are consumptives, they face a higher risk of contamination. Hence, a lower threshold is set in this case.

if (constrain(count, 1, 25)) {
digitalWrite(LEDGREEN, LOW); // This is a green LED which means area is not contaminated yet
}
if (constrain(count, 25, 50)) {
digitalWrite(LEDBLUE, LOW); // This is a b led so that people are alert that more than 50 people
}
if (constrain(count, 51,75)) {
digitalWrite(LEDRED, LOW); // This is a red LED which warns people that this area is contaminated and one should take care while touching objects
}
if (constrain(count, 76, 90)) {
digitalWrite(UV, LOW); // This is an Ultraviolet Light which ensures that after 100 people have touched the aisle objects, it sanitises the area with UV light
}
if (count> 91) {
(count =0); // this resets the person count to 0 once the area has been sanitised by UV light
}

Setting up the Thingspeak IoT Dashboard:

On the thingspeak dashboard, there are 4 fields which have been added for the 4 aisles that have been set up namely:

  • Food-aisle
  • Sports-aisle
  • Clothes-aisle
  • Electronics-aisle

The co-ordinate locations of the mall can be seen in the channel visualization which can be cumulatively displayed on a single dashboard through which the data for each supermarket and mall in a locality can be accessed.

The IoT Dashboard can be viewed here:IoT Dashboard

The fields and channels of aisles can be all displayed together on a single Thingspeak IoT Dashboard.

The above image shows a density plot of the density population in a given region in the store of a mall.

Currently I have been using the ThingSpeak Dashboard to plot the data in a graphical format. But, using the same data and Tableau visualizations, it is possible to plot the given data on the floor plot map of the Supermarket.

This data will be easily accessible to other visitors entering the supermarket and will be able to interpret data driven decisions like visiting the store which has a low population density.

Now a question might arise that what makes the product unique:

Currently, the UV sanitisation system is implemented on manual basis and hence the sanitisation system is carried out manually monitoring these areas. Autonomous UV robots are deployed in malls to gather plot data of mall and map these areas, hence autonomously sanitising these areas. These solutions are not capable of sanitising each aisle and area in the mall along with the flowing population of the people in the mall. The sanitisation is only limited to sanitising the lower part of these aisles and hence the upper parts are left unsanitised. These solutions are also comparatively expensive and they are not capable of continuous data monitoring and sanitisation respective to the rate of contamination. This means that they are unable to monitor the number of people who have passes through a certain area or aisle and hence are unable to sanitise areas according to their contamination.

Go to Market and Viability:

  • Malls and Supermarkets can use this to identify The count of people in and density of people in a certain area in a store and impose self sanitisation process within the malls.
  • Implement Strategies using this data to ensure Safety and Compliance with efficient Population Density monitoring algorithms.
  • Decrease Labour and automate Sanitisation process.
  • Offer Dashboard to the visitors to monitor the density of people at a certain area inside the mall and accordingly the visitors can have a view of the store with lowest density population and make decisions based on this data. The visitors can also have a view of the timely trends of sanitisation by viewing the abrupt change of person density graph in an area when sanitisation is conducted. ( Since, each time sanitisation in an area takes place, the person count is reset to 0 because the place is sanitised)
  • This product can be used to ensure the visitors that the mall is a safe place and hence, can increase the sales and visits following Government guidelines
  • Companies offering Ai and IoT based solutions can invest for mass production and distribution.
  • The more the supermarkets using this product, the more the access to data to the government and more the choice to customers to select the preferable safest place in their locality and which store is continuously sanitised to be up to mark in terms of safety. This will lead to a wide range of options of supermarkets in the locality comparing the queue time and safety.
  • Comparatively affordable solution as compared to autonomous UV Robot system and highly scalable in terms of data and services with IoT Dashboard provided.
  • Utilize real-time CCTV footage to impose autonomous sanitisation system through person detection in terms of timely trends and spatial analysis of person density in the mall.
  • Enable Stores to make better, data-driven decisions that ensure your safety and efficient Queues based on autonomous queuing system.

Github Link :https://github.com/dhruvsheth-ai/self-sanitisation-person-detection

PROJECT VIDEO:

Thank you for viewing my project!

代码

  • Elevator Automation using "up" - "down" speech command
Elevator Automation using "up" - "down" speech commandArduino
The below is a .zip file of the Arduino Library
No preview (download only).
Mall Aisle Self Sanitization with Person Detection System
https://github.com/dhruvsheth-ai/self-sanitisation-person-detetction
Temperature monitoring system based on IoT
https://github.com/dhruvsheth-ai/temperature-arduino-iot
Mask Detection Algorithm
https://github.com/dhruvsheth-ai/ble-mask-detection-optimised
Autonomous Person Intercom based on TinyML
https://github.com/dhruvsheth-ai/person-autonomous-intercom
Elevator Automation using TinyML
https://github.com/dhruvsheth-ai/elevator-Automation-ARDUINO
Mall Entrance Person Detection and Queuing System
Queuing System based on TinyML person detection on Mall Entrance and exit connected via IoT Thingspeak dashboard for Live data monitoringhttps://github.com/dhruvsheth-ai/Person-queuing-system-arduino33

示意图

This is the Schematic for the Mall Aisle Person detection and self sanitization system that transfers data to ThingSpeak IoT Dashboard Detects a person present at the door and rings the bell and displays "person" on the LED Matrix as an alternative

制造工艺

  1. 基于 Raspberry PI 的 IoT 项目连接 DHT11 传感器
  2. 基于物联网资产跟踪解决方案的无线协议
  3. Arduino Pong Game - OLED 显示器
  4. 使用物联网的心率监测器
  5. Pixie:基于 Arduino 的 NeoPixel 手表
  6. 玩 Nextion Display
  7. 使用 TinyML 确定植物健​​康
  8. Arduino Apple Watch
  9. 真空荧光显示控制器
  10. 基于 Arduino 和 OLED 的元胞自动机
  11. Azure IoT 游泳池
  12. 基于物联网的敞篷货车解决方案