Continuous locomotion mode recognition and gait phase estimation based on a shank-mounted IMU with artificial neural networks

TL;DR

This paper presents a neural network-based approach for continuous recognition of gait modes, gait phase, and stair slope estimation using a single shank-mounted IMU, demonstrating high accuracy and robustness in real-world conditions.

Contribution

The study introduces a novel neural network method that utilizes time history data from a single IMU for accurate gait and environment recognition in wearable robotics.

Findings

Gait phase estimation error was 2.0-3.5%.

Locomotion mode classification accuracy was 98.51%-99.67%.

Method demonstrated robustness across different hardware and environments.

Abstract

To improve the control of wearable robotics for gait assistance, we present an approach for continuous locomotion mode recognition as well as gait phase and stair slope estimation based on artificial neural networks that include time history information. The input features consist exclusively of processed variables that can be measured with a single shank-mounted inertial measurement unit. We introduce a wearable device to acquire real-world environment test data to demonstrate the performance and the robustness of the approach. Mean absolute error (gait phase, stair slope) and accuracy (locomotion mode) were determined for steady level walking and steady stair ambulation. Robustness was assessed using test data from different sensor hardware, sensor fixations, ambulation environments and subjects. The mean absolute error from the steady gait test data for the gait phase was 2.0-3.5 % for gait phase estimation and 3.3-3.8{\deg} for stair slope estimation. The accuracy of classifying the correct locomotion mode on the test data with the utilization of time history information was in between 98.51 % and 99.67 %. Results show high performance and robustness for continuously predicting gait phase, stair slope and locomotion mode during steady gait. As hypothesized, time history information improves the locomotion mode recognition. However, while the gait phase estimation performed well for untrained transitions between locomotion modes, our qualitative analysis revealed that it may be beneficial to include transition data into the training of the neural network to improve the prediction of the slope and the locomotion mode. Our results suggest that artificial neural networks could be used for high level control of wearable lower limb robotics.