Recognition of Physiological Patterns during Activities of Daily Living Using Wearable Biosignal Sensors

TL;DR

This study analyzes physiological signals from wearable sensors during daily activities to identify patterns that could predict fall risk, aiding in fall prevention efforts.

Contribution

It provides a statistical overview of physiological patterns during daily activities using EMG, ECG, and respiration sensors, highlighting their potential in fall risk prediction.

Findings

Distinctive patterns between high- and low-intensity activities

Proportional trend between movement velocity and muscle activity

Potential for developing activity recognition and fall risk prediction frameworks

Abstract

A key aspect of developing fall prevention systems is the early prediction of a fall before it occurs. This paper presents a statistical overview of results obtained by analyzing 22 activities of daily living to recognize physiological patterns and estimate the risk of an imminent fall. The results demonstrate distinctive patterns between high-intensity and low-intensity activity using EMG, ECG, and respiration sensors, also indicating the presence of a proportional trend between movement velocity and muscle activity. These outcomes highlight the potential benefits of using these sensors in the future to direct the development of an activity recognition and risk prediction framework for physiological phenomena that can cause fall injuries.