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Pulse rhythm

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Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

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Electrodeless Heart and Respiratory Rate Estimation during Sleep Using a Single Fabric Band and Event-Based Edge

Titus Jayarathna¹, Gaetano D Gargiulo^1,2,3,4, Gough Y Lui¹

¹The MARCS Institute, Western Sydney University, Westmead, NSW 2145, Australia.

Sensors (Basel, Switzerland)

|September 9, 2022

Summary

This study introduces a novel fabric sensor and event-based algorithms for comfortable, precise sleep monitoring. The system accurately measures heart rate (HR) and respiratory rate (RR), reducing data size and power consumption for better user experience.

Keywords:

edge computing textile sensors wearable sensors wireless sensors

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Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Area of Science:

Biomedical Engineering
Wearable Technology
Sensor Technology

Background:

Accurate heart rate (HR) and respiratory rate (RR) monitoring is crucial for diagnosing illnesses.
Non-contact HR/RR measurement is challenging due to imprecise readings.
Invisible wearables offer potential for comfortable and precise physiological monitoring during sleep studies.

Purpose of the Study:

To develop a novel, low-footprint method for sleep monitoring using integrated fabric sensors.
To introduce an event-based architecture and algorithms for real-time HR/RR calculation on edge devices.
To evaluate the accuracy and efficiency of the proposed system compared to gold standards.

Main Methods:

A single silver-coated nylon fabric band was integrated into a cotton/nylon garment as a resistive elastomer sensor.
An event-based architecture processed data on ARM Cortex-M3 and Cortex-M4F microcontrollers.
Two algorithms were developed for real-time HR/RR calculation, with data compression via event conversion.

Main Results:

Respiratory rate (RR) estimations achieved 99.03% sensitivity and 99.03% precision.
Heart rate (HR) calculation showed a mean absolute error of 0.81 ± 0.97 and 0.86 ± 0.61 beats/min compared to ECG.
The event-based algorithm reduced data size by 40-140 times and decreased power consumption by 33% while retaining waveform information.

Conclusions:

Fabric sensors combined with event-based algorithms offer a promising solution for reduced-footprint sleep monitoring.
The system provides accurate HR/RR estimations with improved performance and user comfort for sleep studies.
This approach can significantly enhance the usability and efficiency of wearable physiological monitoring devices.