Ambulatory Human Gait Phase Detection Using Wearable Inertial Sensors and Hidden Markov Model.

Long Liu,Huihui Wang,Hongyu Zhao,Sen Qiu,Jiayi Liu,Haorui Li,Xiangyang Guo

doi:10.3390/s21041347

Abstract

Gait analysis, as a common inspection method for human gait, can provide a series of kinematics, dynamics and other parameters through instrumental measurement. In recent years, gait analysis has been gradually applied to the diagnosis of diseases, the evaluation of orthopedic surgery and rehabilitation progress, especially, gait phase abnormality can be used as a clinical diagnostic indicator of Alzheimer Disease and Parkinson Disease, which usually show varying degrees of gait phase abnormality. This research proposed an inertial sensor based gait analysis method. Smoothed and filtered angular velocity signal was chosen as the input data of the 15-dimensional temporal characteristic feature. Hidden Markov Model and parameter adaptive model are used to segment gait phases. Experimental results show that the proposed model based on HMM and parameter adaptation achieves good recognition rate in gait phases segmentation compared to other classification models, and the recognition results of gait phase are consistent with ground truth. The proposed wearable device used for data collection can be embedded on the shoe, which can not only collect patients’ gait data stably and reliably, ensuring the integrity and objectivity of gait data, but also collect data in daily scene and ambulatory outdoor environment.

Highlights

Accepted: 8 February 2021Walking is one of the most common physical activities for humans and plays an important role in our daily activities
Researches about Micro-Electro-Mechanical Systems (MEMS) have developed rapidly over the past decade, enabling the development of computer communication devices, high-performance physical sensors, and especially inertial sensors. These sensors are characterized by their large memory capacity, small size and low cost, and it is due to these characteristics that they are widely used in various areas [9–22]
The data source and data acquisition scheme are introduced; secondly, the indexes and curves for evaluating the algorithm are introduced; the overall recognition rate of single dimensional and multi-dimensional features before and after the model parameters are adapted and the recognition rate when the output is subject to different distribution are analyzed; compared with the recognition rate of the model before and after the parameter adjustment, the model parameters result in better perforamance

Summary

Introduction

Walking is one of the most common physical activities for humans and plays an important role in our daily activities It can be performed in a variety of ways and directions, and is an energy efficient method of mobility. Researches about Micro-Electro-Mechanical Systems (MEMS) have developed rapidly over the past decade, enabling the development of computer communication devices, high-performance physical sensors, and especially inertial sensors. These sensors are characterized by their large memory capacity, small size and low cost, and it is due to these characteristics that they are widely used in various areas [9–22]

Methods

Results

Conclusion