# Improving Photoplethysmographic Measurements under Motion Artifacts using Artificial Neural Network for Personal Healthcare

arXiv: https://arxiv.org/abs/1807.05331 · HTML: https://mlsanity.com/paper/1807.05331/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN](https://mlsanity.com/paper/2106.11512/) (2021)
- [Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices](https://mlsanity.com/paper/2208.07981/) (2022)
- [Motion Artifact Reduction In Photoplethysmography For Reliable Signal Selection](https://mlsanity.com/paper/2109.02755/) (2021)
- [Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysis](https://mlsanity.com/paper/2504.09132/) (2025)

## Abstract (arXiv)

Photoplethysmographic (PPG) measurements are susceptible to motion artifacts (MA) due to movement of the peripheral body parts. In this paper, we present a new approach to identify the MA corrupted PPG beats and then rectify the beat morphology using artificial neural network (ANN). Initially, beat quality assessment was done to identify the clean PPG beats by a pre-trained feedback ANN to generate a reference beat template for each person. The PPG data was decomposed using principal component analysis (PCA) and reconstructed using fixed energy retention. A weight coefficient was assigned for each PPG samples in such a way that when they are multiplied , the modified beat morphology matches the reference template. A particle swarm optimization (PSO) based technique was utilized to select the best weight weight vector coefficients to tune another feedback ANN, fed with a set of significant features generated by an auto encoder from PCA reconstructed data. For real time implementation, this pre-trained ANN was operated in feed-forward mode to directly generate the weight vectors for any subsequent measurements of PPG. The method was validated with PPG data collected from 55 human subjects. An average RMSE of 0.28 and SNR improvement of 14.54 dB was obtained, with an average improvement of 36% and 47% measurement accuracy on crest time and systolic to diastolic peak height ratio respectively. With IEEE Signal Processing Cup 2015 Challenge database, Pearson's correlation coefficient between PPG estimated and ECG derived heart rate was 0.990. The proposed method can be useful for personal health monitoring applications.