Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review
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Analysis of lower limb exercises is traditionally completed with four distinct methods: (1) 3D motion capture; (2) depth-camera-based systems; (3) visual analysis from a qualified exercise professional; and (4) self-assessment. Each method is associated with a number of limitations.
The aim of this systematic review is to synthesise and evaluate studies which have investigated the capacity for inertial measurement unit (IMU) technologies to assess movement quality in lower limb exercises.
A systematic review of studies identified through the databases of PubMed, ScienceDirect and Scopus was conducted.
Study Eligibility Criteria
Articles written in English and published in the last 10 years which investigated an IMU system for the analysis of repetition-based targeted lower limb exercises were included.
Study Appraisal and Synthesis Methods
The quality of included studies was measured using an adapted version of the STROBE assessment criteria for cross-sectional studies. The studies were categorised into three groupings: exercise detection, movement classification or measurement validation. Each study was then qualitatively summarised.
From the 2452 articles that were identified with the search strategies, 47 papers are included in this review. Twenty-six of the 47 included studies were deemed as being of high quality.
Wearable inertial sensor systems for analysing lower limb exercises is a rapidly growing field of research. Research over the past 10 years has predominantly focused on validating measurements that the systems produce and classifying users’ exercise quality. There have been very few user evaluation studies and no clinical trials in this field to date.
Compliance with Ethical Standards
Martin O’Reilly was partially funded for this work by the Irish Research Council as part of a Postgraduate Enterprise Partnership Scheme with Shimmer (EPSPG/2013/574). Brian Caulfield, Tomas Ward, William Johnston and Cailbhe Doherty were funded and Martin O’Reilly was partially funded by Science Foundation Ireland under their grant for the Insight Centre for Data Analytics (SFI/12/RC/2289). These funding bodies had no influence on the data collection, data analysis, data interpretation or approval/disapproval of publication.
Conflict of interest
Martin O’Reilly, Brian Caulfield, Tomas Ward, William Johnston and Cailbhe Doherty declare that they have no conflicts of interest relevant to the content of this review.
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