Wearable Sensor-Based Real-Time Gait Detection: A Systematic Review

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 8; p. 2727
• Main Authors: Prasanth, Hari, Caban, Miroslav, Keller, Urs, Courtine, Grégoire, Ijspeert, Auke, Vallery, Heike, von Zitzewitz, Joachim
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.04.2021; MDPI
• Subjects: Activities of Daily Living; Artificial intelligence; Biomechanical Phenomena; Citation indexes; Digital libraries; Gait; Gait Analysis; Humans; inertial measurement unit; insole pressure sensors; Keywords; Laboratories; pathological gait; Prostheses; real-time gait detection; Rehabilitation; Science; Search engines; Semantics; Sensors; Spinal cord; Systematic Review; Wearable Electronic Devices; wearable sensor; assistive device; inertial measurement unit; insole pressure sensors; pathological gait; wearable sensor; gait rehabilitation; real-time gait detection; gait analysis
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21082727

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.