Role of machine learning in gait analysis: a review

Abstract

Human biomechanics and gait form an integral part of life. The gait analysis involves a large number of interdependent parameters that were difficult to interpret due to a vast amount of data and their inter-relations. To simplify evaluation, the integration of machine learning (ML) with biomechanics is a promising solution. The purpose of this review is to familiarise the readers with key directions of implementation of ML techniques for gait analysis and gait rehabilitation. An extensive literature survey was based on research articles from nine databases published from 1980 to 2019. With over 943 studies identified, finally, 43 studies met the inclusion criteria. The outcome reported illustrates that supervised ML techniques showed accuracies above 90% in the identified gait analysis domain. The statistical results revealed support vector machine (SVM) as the best classifier (mean-score = 0.87 ± 0.07) with remarkable generalisation capability even on small to medium datasets. It has also been analysed that the control strategies for gait rehabilitation are benefitted from reinforcement learning and (deep) neural-networks due to their ability to capture participants’ variability. This review paper shows the success of ML techniques in detecting disorders, predicting rehabilitation length, and control of rehabilitation devices which make them suitable for clinical diagnosis.

Keywords:

• Artificial intelligence (AI)
• machine learning (ML)
• gait analysis
• gait rehabilitation
• pathology detection

Acknowledgements

The authors would like to present their sincere gratitude for the support and contribution provided by Director, CSIR-Central Scientific Instrument Organisation, and researchers in Biomedical Instrumentation Unit, CSIR-CSIO, Chandigarh, India. PK acknowledges UGC, India for supporting her Ph.D. through its national fellowship programme. A wholehearted thanks to independent reviewer Ms. Vibhuti (VI) for her contribution.

Disclosure statement

The authors declare no conflict of interest.