Real-Time Wearable Gait Phase Segmentation For Running And Walking

TL;DR

This paper introduces a real-time gait phase segmentation method using a CNN-based approach with a single IMU sensor, adaptable to walking and running at various speeds, achieving high accuracy and low latency.

Contribution

It proposes a novel segmentation-based gait phase detection method with gait phase aware receptive fields, suitable for high-speed and low-speed IMU data, enabling real-time mobile implementation.

Findings

Achieves 96.44% gait phase detection accuracy at 20Hz sampling rate.

Error of approximately 9 ms in swing and stance times.

Real-time processing on mobile takes only 36 ms per second of data.

Abstract

Previous gait phase detection as convolutional neural network (CNN) based classification task requires cumbersome manual setting of time delay or heavy overlapped sliding windows to accurately classify each phase under different test cases, which is not suitable for streaming Inertial-Measurement-Unit (IMU) sensor data and fails to adapt to different scenarios. This paper presents a segmentation based gait phase detection with only a single six-axis IMU sensor, which can easily adapt to both walking and running at various speeds. The proposed segmentation uses CNN with gait phase aware receptive field setting and IMU oriented processing order, which can fit to high sampling rate of IMU up to 1000Hz for high accuracy and low sampling rate down to 20Hz for real time calculation. The proposed model on the 20Hz sampling rate data can achieve average error of 8.86 ms in swing time, 9.12 ms in stance time and 96.44\% accuracy of gait phase detection and 99.97\% accuracy of stride detection. Its real-time implementation on mobile phone only takes 36 ms for 1 second length of sensor data.