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Artificial Neural Network-Based Activities Classification, Gait Phase Estimation, and Prediction.

Shuangyue Yu¹, Jianfu Yang¹, Tzu-Hao Huang¹

¹Lab of Biomechatronics and Intelligent Robotics (BIRO), Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, 27695, USA.

Annals of Biomedical Engineering

|January 21, 2023

Summary

This summary is machine-generated.

This study introduces an advanced artificial neural network algorithm for real-time gait phase estimation and prediction. The system accurately detects unrhythmic gait patterns during various activities, enhancing wearable device control and health monitoring.

Keywords:

Activities classification Artificial neural networks Exoskeleton Gait phase detection

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Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Area of Science:

Biomechanics
Artificial Intelligence
Wearable Technology

Background:

Gait pattern analysis is crucial for health monitoring, assessing gait impairments, and controlling wearable devices.
Detecting unrhythmic gait patterns in community settings presents a significant challenge.
Existing methods often struggle with complex, real-world movement variations.

Purpose of the Study:

To develop a high-accuracy algorithm for real-time gait phase estimation and prediction.
To enable gait analysis in community settings using minimal wearable sensors.
To improve the performance of wearable device controllers through gait phase prediction.

Main Methods:

Utilized a two-stage artificial neural network architecture.
Employed two Inertial Measurement Unit (IMU) sensors, one on each thigh.
Trained and validated the algorithm on datasets from able-bodied subjects performing various activities.

Main Results:

Achieved 99.55% accuracy in identifying multiple activities under unrhythmic conditions.
Demonstrated real-time gait phase estimation with a mean error of 6.3%.
Showcased prediction of gait phase 200 ms ahead with an 8.6% error, significantly outperforming an event-based method.

Conclusions:

The developed algorithm accurately estimates and predicts gait status for diverse unrhythmic activities.
This technology offers a viable solution for enhancing wearable robot controllers and health monitoring systems.
The system's real-time capabilities and high accuracy pave the way for advanced human-computer interaction in mobility assistance.