# TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise

arXiv: https://arxiv.org/abs/1409.5181 · HTML: https://mlsanity.com/paper/1409.5181/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2014.

## Built on by (most cited first, 30 of 40)

- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking](https://mlsanity.com/paper/2302.10902/) (2023)
- [Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learning](https://mlsanity.com/paper/2309.13439/) (2023)
- [WildPPG: A Real-World PPG Dataset of Long Continuous Recordings](https://mlsanity.com/paper/2412.17540/) (2024)
- [GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals](https://mlsanity.com/paper/2503.08015/) (2025)
- [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)
- [KID-PPG: Knowledge Informed Deep Learning for Extracting Heart Rate from a Smartwatch](https://mlsanity.com/paper/2405.09559/) (2024)
- [PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection](https://mlsanity.com/paper/2509.19774/) (2025)
- [Robust and Energy-efficient PPG-based Heart-Rate Monitoring](https://mlsanity.com/paper/2203.16339/) (2022)
- [Self-Supervised PPG Representation Learning Shows High Inter-Subject Variability](https://mlsanity.com/paper/2212.04902/) (2022)
- [Sparse learned kernels for interpretable and efficient medical time series processing](https://mlsanity.com/paper/2307.05385/) (2023)
- [An Unsupervised Approach for Periodic Source Detection in Time Series](https://mlsanity.com/paper/2406.00566/) (2024)
- [Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learning](https://mlsanity.com/paper/2502.19921/) (2025)
- [Tiny-PPG: A Lightweight Deep Neural Network for Real-Time Detection of Motion Artifacts in Photoplethysmogram Signals on Edge Devices](https://mlsanity.com/paper/2305.03308/) (2023)
- [TimelyGPT: Extrapolatable Transformer Pre-training for Long-term Time-Series Forecasting in Healthcare](https://mlsanity.com/paper/2312.00817/) (2023)
- [Multi-Head Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation](https://mlsanity.com/paper/2210.11415/) (2022)
- [A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables](https://mlsanity.com/paper/2307.05339/) (2023)
- [Bidirectional Generative Pre-training for Improving Healthcare Time-series Representation Learning](https://mlsanity.com/paper/2402.09558/) (2024)
- [SensorBench: Benchmarking LLMs in Coding-Based Sensor Processing](https://mlsanity.com/paper/2410.10741/) (2024)
- [A General Framework for Generative Self-supervised Learning in Non-invasive Estimation of Physiological Parameters Using Photoplethysmography](https://mlsanity.com/paper/2604.22780/) (2026)
- [Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error](https://mlsanity.com/paper/2107.00693/) (2021)
- [PPG-based Heart Rate Estimation with Efficient Sensor Sampling and Learning Models](https://mlsanity.com/paper/2303.13636/) (2023)
- [Multi-Scale and Multi-Modal Contrastive Learning Network for Biomedical Time Series](https://mlsanity.com/paper/2312.03796/) (2023)
- [Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation](https://mlsanity.com/paper/2510.27297/) (2025)
- [Deriving Health Metrics from the Photoplethysmogram: Benchmarks and Insights from MIMIC-III-Ext-PPG](https://mlsanity.com/paper/2603.21832/) (2026)
- [Synthesizing Skeletal Motion and Physiological Signals as a Function of a Virtual Human's Actions and Emotions](https://mlsanity.com/paper/2102.04548/) (2021)
- [Motion Artifact Reduction In Photoplethysmography For Reliable Signal Selection](https://mlsanity.com/paper/2109.02755/) (2021)
- [FastBVP-Net: a lightweight pulse extraction network for measuring heart rhythm via facial videos](https://mlsanity.com/paper/2206.12558/) (2022)

## Abstract (arXiv)

Heart rate monitoring using wrist-type photoplethysmographic (PPG) signals during subjects' intensive exercise is a difficult problem, since the signals are contaminated by extremely strong motion artifacts caused by subjects' hand movements. So far few works have studied this problem. In this work, a general framework, termed TROIKA, is proposed, which consists of signal decomposiTion for denoising, sparse signal RecOnstructIon for high-resolution spectrum estimation, and spectral peaK trAcking with verification. The TROIKA framework has high estimation accuracy and is robust to strong motion artifacts. Many variants can be straightforwardly derived from this framework. Experimental results on datasets recorded from 12 subjects during fast running at the peak speed of 15 km/hour showed that the average absolute error of heart rate estimation was 2.34 beat per minute (BPM), and the Pearson correlation between the estimates and the ground-truth of heart rate was 0.992. This framework is of great values to wearable devices such as smart-watches which use PPG signals to monitor heart rate for fitness.