Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices

• URL: http://arxiv.org/abs/2305.03308v2
• Date: Sun, 13 Aug 2023 12:30:29 GMT
• Title: Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices
• Authors: Yali Zheng, Chen Wu, Peizheng Cai, Zhiqiang Zhong, Hongda Huang, Yuqi Jiang
• Abstract summary: Photoplethysmogram signals are easily contaminated by motion artifacts in real-world settings. This study proposed a lightweight deep neural network, called Tiny-edge, for accurate and real-time PPG artifact segmentation on IoT devices. Tiny-edge was successfully deployed on an STM32 embedded system for real-time PPG artifact detection.
• Score: 6.352499671581954
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Photoplethysmogram (PPG) signals are easily contaminated by motion artifacts in real-world settings, despite their widespread use in Internet-of-Things (IoT) based wearable and smart health devices for cardiovascular health monitoring. This study proposed a lightweight deep neural network, called Tiny-PPG, for accurate and real-time PPG artifact segmentation on IoT edge devices. The model was trained and tested on a public dataset, PPG DaLiA, which featured complex artifacts with diverse lengths and morphologies during various daily activities of 15 subjects using a watch-type device (Empatica E4). The model structure, training method and loss function were specifically designed to balance detection accuracy and speed for real-time PPG artifact detection in resource-constrained embedded devices. To optimize the model size and capability in multi-scale feature representation, the model employed depth-wise separable convolution and atrous spatial pyramid pooling modules, respectively. Additionally, the contrastive loss was also utilized to further optimize the feature embeddings. With additional model pruning, Tiny-PPG achieved state-of-the-art detection accuracy of 87.4% while only having 19,726 model parameters (0.15 megabytes), and was successfully deployed on an STM32 embedded system for real-time PPG artifact detection. Therefore, this study provides an effective solution for resource-constraint IoT smart health devices in PPG artifact detection.

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