Next Article in Journal
A Meta-Analysis of Intracortical Device Stiffness and Its Correlation with Histological Outcomes
Next Article in Special Issue
A ΣΔ Closed-Loop Interface for a MEMS Accelerometer with Digital Built-In Self-Test Function
Previous Article in Journal
Luminescent Properties of Eu3+-Doped Hybrid SiO2-PMMA Material for Photonic Applications
Previous Article in Special Issue
Design of Ensemble Stacked Auto-Encoder for Classification of Horse Gaits with MEMS Inertial Sensor Technology
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Micromachines 2018, 9(9), 442;

MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion

School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, China
Dalian Neusoft University of Information, Dalian 116023, China
Dalian Medical University, Dalian 116027, China
Author to whom correspondence should be addressed.
Received: 31 July 2018 / Revised: 30 August 2018 / Accepted: 30 August 2018 / Published: 3 September 2018
(This article belongs to the Special Issue MEMS Accelerometers)
Full-Text   |   PDF [2432 KB, uploaded 6 September 2018]   |  


Gait and posture are regular activities which are fully controlled by the sensorimotor cortex. In this study, fluctuations of joint angle and asymmetry of foot elevation in human walking stride records are analyzed to assess gait in healthy adults and patients affected with gait disorders. This paper aims to build a low-cost, intelligent and lightweight wearable gait analysis platform based on the emerging body sensor networks, which can be used for rehabilitation assessment of patients with gait impairments. A calibration method for accelerometer and magnetometer was proposed to deal with ubiquitous orthoronal error and magnetic disturbance. Proportional integral controller based complementary filter and error correction of gait parameters have been defined with a multi-sensor data fusion algorithm. The purpose of the current work is to investigate the effectiveness of obtained gait data in differentiating healthy subjects and patients with gait impairments. Preliminary clinical gait experiments results showed that the proposed system can be effective in auxiliary diagnosis and rehabilitation plan formulation compared to existing methods, which indicated that the proposed method has great potential as an auxiliary for medical rehabilitation assessment. View Full-Text
Keywords: MEMS sensors; gait analysis; rehabilitation assessment; body sensor network MEMS sensors; gait analysis; rehabilitation assessment; body sensor network

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Qiu, S.; Liu, L.; Zhao, H.; Wang, Z.; Jiang, Y. MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion. Micromachines 2018, 9, 442.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Micromachines EISSN 2072-666X Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top