構(gòu)建可以檢測(cè)潮熱并觸發(fā)動(dòng)作來緩解人的設(shè)備

描述

概述

潮熱是上半身突然感到溫暖，通常在面部、頸部和胸部最為強(qiáng)烈。盡管其他醫(yī)療條件也可能導(dǎo)致潮熱，但潮熱最常見的原因是更年期。在這個(gè)項(xiàng)目中，我構(gòu)建了一個(gè)可以用來檢測(cè)潮熱并觸發(fā)一些動(dòng)作來緩解人的設(shè)備，在這種情況下，使用紅外發(fā)射器打開空調(diào)冷卻系統(tǒng)。作為輸入，它采用多維紅外熱傳感器數(shù)據(jù)。它的輸出將是一個(gè)簡單的分類，如果有人被識(shí)別并且最近發(fā)生了突然的溫度變化，它會(huì)通知我們。

硬件考慮

我想做一個(gè)可以讀取體溫?cái)?shù)據(jù)的可穿戴設(shè)備。在做了一些研究和實(shí)施之后，我發(fā)現(xiàn)一直佩戴傳感器，尤其是在睡覺時(shí)，在很多情況下都是不舒服的，而且是不可行的。潮熱也會(huì)導(dǎo)致出汗，佩戴電池供電的設(shè)備需要特殊的外殼才能準(zhǔn)確測(cè)量數(shù)據(jù)。此外，對(duì)于冷卻，我研究了熱電 Peltier 冷卻器模塊，發(fā)現(xiàn)它們中的大多數(shù)都需要更大的 12V 電池才能正常運(yùn)行以實(shí)現(xiàn)最佳冷卻，如果不仔細(xì)控制它也是不安全的并且可能導(dǎo)致灼傷。對(duì)于非侵入式解決方案，我發(fā)現(xiàn)低分辨率熱像儀可以從遠(yuǎn)處可靠地讀取溫度，并且它們可以在黑暗中工作。雖然讀取精度可能會(huì)受到距離的影響，但通過計(jì)算可以準(zhǔn)確記錄熱圖像數(shù)據(jù)的連續(xù)或突然變化。我選擇 Atom Matrix 作為微控制器設(shè)備，因?yàn)樗w積小、價(jià)格便宜、可以運(yùn)行 TensorFlow 模型并且有一個(gè)可以用作遙控器的 IR 發(fā)射器。我選擇 Wio Terminal 來捕獲熱像儀數(shù)據(jù)進(jìn)行訓(xùn)練，因?yàn)樗幸粋€(gè)帶有許多按鈕的 LCD 屏幕，用于捕獲不同類別的數(shù)據(jù)。

訓(xùn)練數(shù)據(jù)收集

機(jī)器學(xué)習(xí)項(xiàng)目的第一步也是最重要的一步是以這樣一種方式收集訓(xùn)練數(shù)據(jù)，即它應(yīng)該涵蓋給定識(shí)別任務(wù)的大多數(shù)代表性案例。Wio 終端上的 3 個(gè)按鈕用于標(biāo)記 3 個(gè)類別。

捕獲的數(shù)據(jù)將保存到連接到 Wio 終端的微型 SD 卡上的文件中。每個(gè)熱圖像數(shù)據(jù)都被捕獲為一個(gè)單獨(dú)的文件。該文件不包含標(biāo)題行，只有逗號(hào)分隔的 768 (24x32) 溫度讀數(shù)。示例讀數(shù)文件內(nèi)容如下所示。

26.47,25.97,25.85,25.72,26.90,26.12,26.60,26.86,27.00,26.68,26.90,26.74,27.78,27.21,27.75,29.12,31.29,31.50,32.24,31.95,31.72,30.80,31.29,30.69,31.18,30.86,31.46,31.37,29.21,28.23,28.18,28.03,25.83,26.33,25.55,26.56,26.59,26.90,26.52,27.38,26.94,27.39,26.85,27.21,27.32,27.66,28.81,30.45,30.97,31.74,31.55,32.14,31.37,31.03,30.63,30.69,31.03,31.52,30.85,31.14,28.80,28.57,27.81,28.39,26.43,26.24,26.67,26.71,27.13,26.99,27.63,28.07,28.59,28.39,27.80,28.19,28.11,28.25,30.91,32.15,31.78,31.46,31.82,31.33,31.10,30.43,30.37,30.06,29.77,29.84,30.58,30.45,29.28,28.42,28.34,27.76,26.31,26.62,26.38,27.24,27.27,27.91,28.94,29.11,30.11,29.73,30.25,29.53,29.59,29.22,32.01,32.70,33.17,32.00,31.15,31.52,30.59,30.46,29.87,30.07,29.43,30.09,30.01,30.68,29.10,28.91,27.99,28.34,26.59,26.60,26.99,27.49,28.68,29.64,31.88,33.14,33.41,33.02,32.48,32.83,32.60,32.60,33.58,34.16,34.79,34.58,32.43,32.15,31.07,30.77,29.84,29.89,30.01,29.48,29.99,29.63,29.33,28.47,28.23,27.90,26.26,26.26,27.13,28.21,30.50,31.41,33.23,33.70,33.44,33.40,33.38,33.18,33.31,33.12,33.65,34.33,34.81,34.93,33.96,32.50,31.29,30.82,29.37,29.93,29.13,29.93,29.29,30.07,28.76,29.00,28.38,28.69,26.83,26.55,27.36,28.68,32.50,33.12,33.78,33.40,34.17,34.14,33.80,33.56,33.84,33.35,33.85,33.54,34.63,34.45,34.68,34.49,31.76,30.94,29.87,29.38,29.67,29.20,29.45,29.68,29.02,28.42,28.63,28.49,26.29,27.30,27.51,28.71,31.96,33.70,33.86,33.76,33.87,34.32,33.60,33.94,33.41,33.39,33.74,34.04,34.32,34.95,34.24,34.63,31.81,31.15,29.59,29.78,29.19,29.62,28.77,29.72,28.88,29.12,28.51,28.79,26.45,27.15,28.06,28.72,32.45,33.05,33.89,33.84,33.60,33.39,34.02,33.69,33.64,33.29,34.11,33.81,34.68,34.55,34.75,34.15,32.55,31.42,30.25,29.76,29.56,29.42,29.64,28.59,28.87,28.66,29.10,28.79,26.76,27.06,27.39,28.97,31.63,33.46,33.78,34.22,33.97,34.11,33.58,34.20,34.01,33.97,33.69,34.16,34.54,34.90,34.33,34.63,33.09,31.81,29.81,30.15,28.89,29.81,28.96,29.39,28.70,29.35,28.54,29.07,27.18,26.59,27.31,27.98,31.52,32.56,33.37,33.52,33.72,33.72,33.52,33.22,33.75,33.27,33.93,34.13,34.69,34.65,34.26,34.12,32.70,31.12,30.58,30.45,29.91,29.42,29.72,28.82,29.66,29.10,28.91,29.04,26.26,26.72,26.66,27.70,29.47,31.74,32.43,33.58,33.48,33.45,32.63,32.79,31.94,33.32,33.36,34.14,34.42,34.55,33.99,33.87,31.15,30.99,30.15,30.84,29.81,30.02,29.24,29.61,29.15,29.18,28.82,29.32,27.21,26.60,27.01,26.83,27.83,27.64,29.27,29.08,30.97,29.96,29.96,28.30,29.66,30.77,34.03,33.60,34.23,33.26,32.36,31.42,30.61,30.63,30.44,30.50,30.45,30.46,29.70,29.37,29.70,29.34,29.17,28.83,26.28,26.53,26.36,27.31,26.68,27.55,27.46,28.27,27.83,28.53,27.72,28.01,28.14,30.84,32.27,33.38,32.09,32.70,31.33,31.32,30.03,30.53,29.86,30.86,30.35,30.88,29.78,29.66,29.58,29.62,29.06,29.77,26.63,26.16,26.65,26.85,27.10,26.79,27.12,26.67,27.47,27.14,27.15,27.15,27.93,28.86,31.42,31.62,31.87,31.27,31.11,30.65,30.47,30.46,30.62,30.17,30.45,29.94,29.73,29.38,29.33,29.33,29.18,29.06,25.92,26.74,26.20,26.93,27.03,26.89,26.71,26.92,26.90,27.08,26.74,27.46,27.06,28.62,31.36,32.15,31.96,31.97,31.03,31.01,30.20,30.71,31.08,31.04,30.51,30.21,29.31,29.81,29.18,29.43,29.07,29.56,26.80,26.61,26.85,26.61,26.86,26.81,26.86,27.13,27.21,26.94,26.84,26.77,27.24,26.68,30.82,31.45,32.56,31.93,31.30,31.13,31.15,31.29,31.83,31.84,31.02,30.16,29.38,29.19,29.56,29.13,29.41,28.83,26.54,26.56,26.58,26.95,26.81,27.11,26.44,27.07,26.81,27.04,26.77,27.05,26.85,26.94,28.29,30.73,31.36,31.84,30.73,30.88,30.73,30.90,31.61,31.92,30.55,30.41,28.76,29.27,29.02,29.64,29.32,29.59,26.79,26.32,26.78,26.93,26.85,26.80,26.95,27.14,26.90,26.59,26.60,26.76,27.19,27.05,27.35,27.16,28.37,28.20,29.84,29.51,31.84,32.69,32.54,31.55,31.03,30.36,29.18,28.63,29.54,28.92,29.18,29.26,26.28,26.71,26.30,27.09,26.60,26.93,26.71,27.05,26.46,27.39,26.90,27.22,26.64,27.33,26.61,27.39,27.15,28.14,28.76,29.57,31.26,32.76,31.79,31.63,30.77,31.15,29.09,29.45,28.78,28.99,29.07,29.83,26.96,26.68,26.70,26.47,26.79,26.53,26.86,26.56,27.08,26.60,26.86,26.50,27.11,26.74,27.29,27.29,27.16,27.50,28.66,28.47,30.35,30.02,30.71,30.36,31.65,31.08,30.13,29.60,29.28,29.08,29.37,29.02,26.45,26.86,26.63,27.07,26.53,27.05,26.85,27.21,26.48,27.28,26.80,27.12,26.54,27.20,26.52,27.19,26.77,27.61,27.91,28.58,28.91,29.42,29.19,30.28,30.07,31.03,30.25,29.85,29.17,29.61,29.30,29.79,26.88,26.71,26.97,26.73,27.01,26.79,26.83,26.99,27.51,26.93,26.96,26.79,27.22,26.88,27.34,26.87,27.20,27.23,27.70,27.45,28.61,28.18,29.45,29.06,29.99,29.77,30.61,29.50,29.72,29.00,29.41,30.15,25.94,27.25,26.77,26.97,26.82,27.24,26.67,27.18,26.75,27.16,27.01,27.37,26.91,27.49,26.81,27.24,26.89,27.97,27.36,28.21,27.73,28.77,28.52,28.88,29.20,30.00,29.95,30.37,29.49,29.48,28.84,29.91

捕獲數(shù)據(jù)的可視化表示如下：

3 類熱圖像

?

代碼部分中提到的 Github 存儲(chǔ)庫中提供了用于捕獲訓(xùn)練數(shù)據(jù)的 Arduino 草圖 (Thermal_camera_data_collection.ino)。為每個(gè)類別捕獲了 100 多個(gè)樣本。收集的數(shù)據(jù)已分為訓(xùn)練 (60%)、驗(yàn)證 (20%) 和測(cè)試 (20%) 數(shù)據(jù)集。由于數(shù)據(jù)是從已經(jīng)校準(zhǔn)的紅外溫度相機(jī) (MLX90640) 收集的，并且它們已經(jīng)在指定范圍內(nèi)，因此我們可以按原樣使用原始數(shù)據(jù)進(jìn)行訓(xùn)練和推理。

模型架構(gòu)

我們可以將輸入數(shù)據(jù)視為 24x36 像素的圖像。卷積神經(jīng)網(wǎng)絡(luò)是適合識(shí)別圖像和時(shí)間序列數(shù)據(jù)中的模式的最佳選擇之一。前幾層是帶有少量其他正則化層的 2D 卷積神經(jīng)網(wǎng)絡(luò)。最后一層是具有softmax激活的全連接密集層，它輸出所有3個(gè)類別的概率。該模型的總結(jié)如下。

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 24, 32, 8)         80        
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 24, 32, 8)         584       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 12, 16, 8)         0         
_________________________________________________________________
dropout (Dropout)            (None, 12, 16, 8)         0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 12, 16, 8)         584       
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 6, 8, 8)           0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 6, 8, 8)           0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 6, 8, 16)          1168      
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 3, 4, 16)          0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 3, 4, 16)          0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 3, 4, 16)          2320      
_________________________________________________________________
flatten (Flatten)            (None, 192)               0         
_________________________________________________________________
dense (Dense)                (None, 64)                12352     
_________________________________________________________________
dropout_3 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_1 (Dense)              (None, 32)                2080      
_________________________________________________________________
dropout_4 (Dropout)          (None, 32)                0         
_________________________________________________________________
dense_2 (Dense)              (None, 3)                 99        
=================================================================
Total params: 19,267
Trainable params: 19,267
Non-trainable params: 0

模型訓(xùn)練和評(píng)估

該模型的訓(xùn)練是在帶有 Linux 和 eGPU (NVIDIA GTX 1080Ti) 的 Intel NUC 上完成的。盡管在 CPU 上訓(xùn)練只需要幾分鐘，但在測(cè)試不同的架構(gòu)和超參數(shù)時(shí)，開發(fā)過程變得非常緩慢。帶有 Keras API 的 TensorFlow 2.1 用于模型創(chuàng)建和訓(xùn)練過程。我創(chuàng)建了一個(gè) Jupyter notebook 用于數(shù)據(jù)處理、訓(xùn)練和最終模型轉(zhuǎn)換。所有代碼都可以在代碼部分提到的 Github 存儲(chǔ)庫中找到。訓(xùn)練準(zhǔn)確率為 99%，測(cè)試數(shù)據(jù)的評(píng)估準(zhǔn)確率為 92.86%，可以通過更多的訓(xùn)練數(shù)據(jù)集和模型超參數(shù)調(diào)整進(jìn)一步提高。

在設(shè)備上進(jìn)行推理

將創(chuàng)建的模型轉(zhuǎn)換為 TensorFlow Lite 模型，并將轉(zhuǎn)換后的模型轉(zhuǎn)換為 C 數(shù)組文件，以便與推理代碼一起部署。TensorFlow Lite Micro SDK 用于在設(shè)備上運(yùn)行推理。我創(chuàng)建了一個(gè) Arduino 草圖（Github 存儲(chǔ)庫中提供 Hot_flash_detector.ino），用于推斷和顯示結(jié)果。微控制器以 500 毫秒的間隔連續(xù)接收來自熱像儀傳感器的樣本，并根據(jù)最近 20 個(gè)平均最高溫度讀數(shù)檢測(cè)被識(shí)別人員的溫度突然變化。

共同利益和成本的用例

這是一種易于使用的低功耗設(shè)備，可以使用電池運(yùn)行數(shù)周。它可以安全地用于白天或晚上面臨潮熱問題的女性。該設(shè)備的成本也很低。最終工作產(chǎn)品（熱像儀 + Atom 矩陣）的總成本遠(yuǎn)低于 50 美元，如果批量生產(chǎn)，還可以進(jìn)一步降低。

改進(jìn)范圍

該設(shè)備可用于通過分析熱圖像時(shí)間序列數(shù)據(jù)來檢測(cè)睡眠問題，并可以觸發(fā)音樂系統(tǒng)播放一些舒緩的音樂或控制智能照明系統(tǒng)。此外，一些分析數(shù)據(jù)可以本地保存在 Atom Matrix 的 SPI 閃存中，并且可以使用手機(jī)應(yīng)用程序通過 BLE 進(jìn)行同步以進(jìn)行進(jìn)一步分析。