# Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction

arXiv: https://arxiv.org/abs/1503.00688 · HTML: https://mlsanity.com/paper/1503.00688/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2015.

## Built on by (most cited first, 16 of 16)

- [WildPPG: A Real-World PPG Dataset of Long Continuous Recordings](https://mlsanity.com/paper/2412.17540/) (2024)
- [Q-PPG: Energy-Efficient PPG-based Heart Rate Monitoring on Wearable Devices](https://mlsanity.com/paper/2203.14907/) (2022)
- [Embedding Temporal Convolutional Networks for Energy-Efficient PPG-Based Heart Rate Monitoring](https://mlsanity.com/paper/2203.04396/) (2022)
- [BeliefPPG: Uncertainty-aware Heart Rate Estimation from PPG signals via Belief Propagation](https://mlsanity.com/paper/2306.07730/) (2023)
- [Robust and Energy-efficient PPG-based Heart-Rate Monitoring](https://mlsanity.com/paper/2203.16339/) (2022)
- [Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification](https://mlsanity.com/paper/2307.06380/) (2023)
- [Multi-Head Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation](https://mlsanity.com/paper/2210.11415/) (2022)
- [PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models](https://mlsanity.com/paper/2303.13636/) (2023)
- [Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation](https://mlsanity.com/paper/2510.27297/) (2025)
- [Exploring Interactions and Regulations in Collaborative Learning: An Interdisciplinary Multimodal Dataset](https://mlsanity.com/paper/2210.05419/) (2022)
- [Efficient and Direct Inference of Heart Rate Variability using Both Signal Processing and Machine Learning](https://mlsanity.com/paper/2303.13637/) (2023)
- [Joint Attention Mechanism Learning to Facilitate Opto-physiological Monitoring during Physical Activity](https://mlsanity.com/paper/2502.09291/) (2025)
- [TAU: Modeling Temporal Consistency Through Temporal Attentive U-Net for PPG Peak Detection](https://mlsanity.com/paper/2503.10733/) (2025)
- [Reliable Physiological Monitoring on the Wrist Using Generative Deep Learning to Address Poor Skin-Sensor Contact](https://mlsanity.com/paper/2504.02735/) (2025)
- [A Machine Learning-Based Multimodal Framework for Wearable Sensor-Based Archery Action Recognition and Stress Estimation](https://mlsanity.com/paper/2511.14057/) (2025)
- [Multi-site PPG: An In-the-Wild Physiological Dataset from Emerging Multi-site Wearables](https://mlsanity.com/paper/2605.17859/) (2026)

## Abstract (arXiv)

Goal: A new method for heart rate monitoring using photoplethysmography (PPG) during physical activities is proposed. Methods: It jointly estimates spectra of PPG signals and simultaneous acceleration signals, utilizing the multiple measurement vector model in sparse signal recovery. Due to a common sparsity constraint on spectral coefficients, the method can easily identify and remove spectral peaks of motion artifact (MA) in PPG spectra. Thus, it does not need any extra signal processing modular to remove MA as in some other algorithms. Furthermore, seeking spectral peaks associated with heart rate is simplified. Results: Experimental results on 12 PPG datasets sampled at 25 Hz and recorded during subjects' fast running showed that it had high performance. The average absolute estimation error was 1.28 beat per minute and the standard deviation was 2.61 beat per minute. Conclusion and Significance: These results show that the method has great potential to be used for PPG-based heart rate monitoring in wearable devices for fitness tracking and health monitoring.