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Related Experiment Video

Updated: Aug 8, 2025

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
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Photoplethysmography-Based Distance Estimation for True Wireless Stereo.

Youngwoo Jeong1, Joungmin Park1, Sun Beom Kwon1

  • 1Department of Electronic Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea.

Micromachines
|February 25, 2023
PubMed
Summary

This study introduces a novel algorithm for estimating distance using Photoplethysmography (PPG) signals in resource-limited True Wireless Stereo (TWS) devices. The method effectively reduces noise, achieving high accuracy for wearable health monitoring.

Keywords:
distance estimationembedded systemnoise reductionphotoplethysmography (PPG)true wireless stereo (TWS)waveform adjustment (WA)

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Wearable Technology

Background:

  • Wearable devices are increasingly used for healthcare, but True Wireless Stereo (TWS) devices have limitations in space and power.
  • Photoplethysmography (PPG) sensors are vital for health monitoring but susceptible to noise from motion and individual characteristics.
  • Effective noise reduction is crucial for accurate PPG signal analysis in TWS devices.

Purpose of the Study:

  • To propose a distance estimation algorithm for PPG signals specifically designed for TWS devices.
  • To address the challenges of noise reduction and simultaneous multi-function implementation in resource-constrained TWS.

Main Methods:

  • Development of a waveform adjustment (WA) filter to minimize noise while preserving signal integrity.
  • Implementation of MobileNet, a lightweight deep learning model, for efficient PPG signal processing.
  • Establishment of a PPG monitoring testbed for algorithm verification with three distance estimation criteria.

Main Results:

  • The proposed algorithm achieved high performance metrics: 92.5% accuracy, 92.6% precision, 92.8% recall, and 0.927 F1 score with a signal length of 15.
  • While other algorithms showed superior metrics in specific cases, the proposed model demonstrated the fastest inference time.
  • The algorithm effectively reduces noise in PPG signals for TWS applications.

Conclusions:

  • The developed distance estimation algorithm offers a viable solution for PPG signal processing in TWS devices.
  • The combination of the WA filter and MobileNet provides an efficient and accurate method for wearable health monitoring.
  • The proposed approach balances performance with the computational constraints of TWS, enabling advanced healthcare functionalities.