Segmentation of Motion Artifacts in Wearable PPG Signals Using Lightweight Neural Networks
Year: 2025
Type: article
Abstract: The widespread adoption of wearable health monitoring devices has highlighted the need for robust algorithms to detect and mitigate motion artefacts in photoplethysmographic (PPG) signals. This paper introduces a novel approach to motion artifact segmentation, supported by the creation of a new dataset. PPG signals from publicly available repositories were curated and enhanced with precise segmentation masks, enabling rigorous model training and evaluation. Three deep learning architectures —UNet, LSTM-UNet, and Atrous-UNet— were assessed using a comprehensive set of metrics, including Dice Score, Intersection over Union, and Average Hausdorff Distance, to highlight the model performance beyond standard evaluations. This multi-metric approach underscored the importance of addressing diverse aspects such as boundary precision and overall robustness. Atrous-UNet emerged as the most effective model, offering a balance of high performance and computational efficiency suitable for real-time deployment. The models were validated on real-world data from open-source wearable devices, EmotiBit and Bangle.js 2 (accuracy above 80% for both), demonstrating generalizability across varying hardware and environmental conditions. Comparisons with expert and non-expert human annotators revealed that the models significantly outperformed the two groups in reliability and detection consistency. The models were optimized using quantization techniques to enable deployment on resource-constrained devices. Although this introduced some performance losses, the models retained robust artefact detection capabilities with an accuracy of over 78%.
Source: IEEE Sensors Journal
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,645
OpenAlex ID: W4409683102