  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.
