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Design and Analysis for Fall Detection System Simplification

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Fast Wearable Sensor-Based Foot-Ground Contact Phase Classification Using a Convolutional Neural Network with

Haneul Jeon¹, Sang Lae Kim¹, Soyeon Kim¹

¹School of Mechanical Engineering, Soongsil University, Seoul 06978, Korea.

Sensors (Basel, Switzerland)

|September 9, 2020

Summary

This study introduces a novel method using sliding-window label overlapping with convolutional neural networks (CNNs) for accurate foot-ground contact phase detection in wearable motion data, achieving over 99% test accuracy.

Keywords:

CNN biomechanics sliding window time-series data walking gait wearable sensor

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Area of Science:

Biomechanics
Wearable technology
Machine learning

Background:

Accurate classification of foot-ground contact and swing phases is crucial for lower-limb motion analysis.
Applications include rehabilitation, gait analysis, and exoskeleton control.
Existing methods may lack precision in real-time detection.

Purpose of the Study:

To develop a real-time method for precise detection of foot-ground contact phases (including 3 sub-phases) and the swing phase.
To utilize time-series wearable motion data and a convolutional neural network (CNN) architecture.
To improve the accuracy of lower-limb motion analysis.

Main Methods:

Implementation of a sliding-window label overlapping technique for training data acquisition.
Utilizing a convolutional neural network (CNN) model for classification.
Processing wearable motion data at a frequency of 100 Hz.

Main Results:

Achieved a real-time CNN model for learning motion data.
Attained a test accuracy of 99.8% or higher for phase detection.
Observed a validation accuracy close to 85%.

Conclusions:

The proposed method accurately detects foot-ground contact and swing phases in real-time.
The CNN model demonstrates high efficacy for lower-limb motion analysis.
This approach has significant potential for applications in rehabilitation and gait improvement.