Wearable sensor-based real-time gait detection: A systematic review

Hari Prasanth, Miroslav Caban, Urs Keller, Grégoire Courtine, Auke Ijspeert, Heike Vallery*, Joachim von Zitzewitz

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

34 Citations (Scopus)
6 Downloads (Pure)

Abstract

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.

Original languageEnglish
Article number2727
JournalSensors
Volume21
Issue number8
DOIs
Publication statusPublished - 13 Apr 2021

Bibliographical note

Funding: This research was partially funded by the European Union’s Horizon 2020 research and
innovation program Grant No. 779963 “EUROBENCH”.

Publisher Copyright: © 2021 by the authors. Lcensee MDPI, Basel, Switzerland.

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