mlsanityData to 7 Oct 2026

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

arXiv 2305.03308 ↗First posted 5 May 2023eess.SP cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 11 of its 43 references that are in the map. First posted on arXiv 5 May 2023.

Builds on

11 of 43 references.

References · 11

  1. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  2. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  3. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  4. 2017Learning Efficient Convolutional Networks through Network Slimmingcited by 673
  5. 2015Learning Deconvolution Network for Semantic Segmentationcited by 365
  6. 2014TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercisecited by 40
  7. 2022Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devicescited by 14
  8. 2021An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGANcited by 10
  9. 2022Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoringcited by 8
  10. 2021Smartphone Camera Oximetry in an Induced Hypoxemia Studycited by 7
  11. 2022Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devicescited by 1

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Cited by year

Citing papers per year: 2023: 1; 2024: 1; 2025: 2.

4 papers.

  1. 2024A Scoping Review of Deep Learning Methods for Photoplethysmography Datacited by 11
  2. 2023Sparse learned kernels for interpretable and efficient medical time series processingcited by 6
  3. 2025Reliable Physiological Monitoring on the Wrist Using Generative Deep Learning to Address Poor Skin-Sensor Contactcited by 0
  4. 2025Generalised Label-free Artefact Cleaning for Real-time Medical Pulsatile Time Seriescited by 0