# Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices

arXiv: https://arxiv.org/abs/2208.07981 · HTML: https://mlsanity.com/paper/2208.07981/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 49 references that are in the map.
- First posted on arXiv 16 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods](https://mlsanity.com/paper/2111.02398/) (2021)
- [Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning](https://mlsanity.com/paper/1905.13628/) (2019)
- [AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis](https://mlsanity.com/paper/2112.12744/) (2021)
- [Measuring what Really Matters: Optimizing Neural Networks for TinyML](https://mlsanity.com/paper/2104.10645/) (2021)
- [Robust and Energy-efficient PPG-based Heart-Rate Monitoring](https://mlsanity.com/paper/2203.16339/) (2022)
- [TinyML Platforms Benchmarking](https://mlsanity.com/paper/2112.01319/) (2021)
- [Improving Photoplethysmographic Measurements under Motion Artifacts using Artificial Neural Network for Personal Healthcare](https://mlsanity.com/paper/1807.05331/) (2018)
- [Heartbeat Anomaly Detection using Adversarial Oversampling](https://mlsanity.com/paper/1901.09972/) (2019)
- [Signal-to-noise ratio is more important than sampling rate in beat-to-beat interval estimation from optical sensors](https://mlsanity.com/paper/2202.13651/) (2022)

## Built on by (most cited first, 1 of 1)

- [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)

## Abstract (arXiv)

The focus of this paper is a proof of concept, machine learning (ML) pipeline that extracts heart rate from pressure sensor data acquired on low-power edge devices. The ML pipeline consists an upsampler neural network, a signal quality classifier, and a 1D-convolutional neural network optimized for efficient and accurate heart rate estimation. The models were designed so the pipeline was less than 40 kB. Further, a hybrid pipeline consisting of the upsampler and classifier, followed by a peak detection algorithm was developed. The pipelines were deployed on ESP32 edge device and benchmarked against signal processing to determine the energy usage, and inference times. The results indicate that the proposed ML and hybrid pipeline reduces energy and time per inference by 82% and 28% compared to traditional algorithms. The main trade-off for ML pipeline was accuracy, with a mean absolute error (MAE) of 3.28, compared to 2.39 and 1.17 for the hybrid and signal processing pipelines. The ML models thus show promise for deployment in energy and computationally constrained devices. Further, the lower sampling rate and computational requirements for the ML pipeline could enable custom hardware solutions to reduce the cost and energy needs of wearable devices.