Latest Research Trends in Gait Analysis Using Wearable Sensors and Machine Learning: A Systematic Review

Abstract

Gait is the locomotion attained through the movement of limbs and gait analysis examines the patterns (normal/abnormal) depending on the gait cycle. It contributes to the development of various applications in the medical, security, sports, and fitness domains to improve the overall outcome. Among many available technologies, two emerging technologies that play a central role in modern day gait analysis are: A) wearable sensors which provide a convenient, efficient, and inexpensive way to collect data and B) Machine Learning Methods (MLMs) which enable high accuracy gait feature extraction for analysis. Given their prominent roles, this paper presents a review of the latest trends in gait analysis using wearable sensors and Machine Learning (ML). It explores the recent papers along with the publication details and key parameters such as sampling rates, MLMs, wearable sensors, number of sensors, and their locations. Furthermore, the paper provides recommendations for selecting a MLM, wearable sensor and its location for a specific application. Finally, it suggests some future directions for gait analysis and its applications.

Recent Advances in Quantitative Gait Analysis Using Wearable Sensors: A Review

“…Hence, it was specific to motor skills instead of gait. Saboor et al [28] discussed the latest trend on gait analysis using wearable sensors combined with machine learning-based methods. However, the study selected 2 0 1 1 2 0 1 2 2 0 1 3 2 0 1 4 2 0 1 5 2 0 1 6 2 0 1 7 2 0 1 8 2 0 1 9 2 0 2 was constrained from 2015 onwards with limitations only to machine learning methods.…”

Wearable Devices for Gait Analysis in Intelligent Healthcare

“…It is friendly to know the patient's condition but unduly burden the clinicians. Computer-aided injuring detection will help clinicians analyze the complex relationships among the measures of gait kinetics, kinematics, and spatiotemporal features for pre-diagnosis (Saboor et al, 2020). Individuals can use their intelligent devices (i.e., smartphones) for auto-diagnosis at any time and anywhere.…”

Machine Learning Approach to Support the Detection of Parkinson’s Disease in IMU-Based Gait Analysis

“…The use of ML in gait analysis has already shown promising results [ 19 , 20 , 21 , 22 ]. Several studies applied ML classification for the detection, quantification, and classification of gait abnormalities in pwPD using gait data from several gait analysis systems [ 8 , 9 , 10 , 11 , 23 ].…”