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Related Experiment Video

Updated: Aug 7, 2025

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

The Design and Engineering of a Fall and Near-Fall Detection Electronic Textile.

Zahra Rahemtulla¹, Alexander Turner², Carlos Oliveira¹

¹Nottingham School of Art & Design, Nottingham Trent University, Bonington Building, Dryden Street, Nottingham NG1 4GG, UK.

Materials (Basel, Switzerland)

|March 11, 2023

Summary

Wearable electronic textiles in over-socks can detect falls and near-falls in older adults. A machine learning algorithm achieved over 94% accuracy in identifying these events, improving safety for those living alone.

Keywords:

E-textiles activities of daily living design electronic textiles electronic yarn fall detection machine learning near-fall detection older people smart textiles

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Area of Science:

Biomedical Engineering
Wearable Technology
Gerontology

Background:

Falls pose significant risks to the quality of life and independence of older adults.
Early detection of falls and near-falls is crucial for timely intervention and prevention.
Existing fall detection systems often lack comfort and user acceptance.

Purpose of the Study:

To design and engineer a comfortable, wearable electronic textile device for monitoring falls and near-falls.
To develop and apply a machine learning algorithm for accurate interpretation of sensor data.
To assess the feasibility of using a single motion-sensing electronic yarn in an over-sock design.

Main Methods:

Development of over-socks incorporating a single motion-sensing electronic yarn.
Data collection from 13 participants performing activities of daily living (ADLs), falls, and near-falls.
Analysis of trial data using a bidirectional long short-term memory (Bi-LSTM) network for classification.

Main Results:

The system accurately differentiated between ADLs and falls (99.4%) and ADLs, falls, and near-falls (94.2%).
High accuracy was achieved even with motion sensing in only one over-sock.
The bidirectional long short-term memory (Bi-LSTM) network effectively classified the sensor data.

Conclusions:

Wearable electronic textiles integrated into comfortable over-socks can reliably detect falls and near-falls.
The developed system, coupled with a Bi-LSTM network, offers a promising solution for fall prevention in older adults.
The findings suggest that a single sensing unit is sufficient for effective fall detection, enhancing device simplicity and user comfort.