Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation

Files in This Item:
File Description SizeFormat 
insight_publication.pdf1.71 MBAdobe PDFDownload
Title: Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation
Authors: O'Reilly, Martin
Duffin, Joe
Ward, Tomás
Caulfield, Brian
Permanent link: http://hdl.handle.net/10197/9356
Date: 21-Aug-2017
Abstract: Background: Biofeedback systems that use inertial measurement units (IMUs) have been shown recently to have the ability toobjectively assess exercise technique. However, there are a number of challenges in developing such systems; vast amounts ofIMU exercise datasets must be collected and manually labeled for each exercise variation, and naturally occurring techniquedeviations may not be well detected. One method of combatting these issues is through the development of personalized exercisetechnique classifiers.Objective: We aimed to create a tablet app for physiotherapists and personal trainers that would automate the development ofpersonalized multiple and single IMU-based exercise biofeedback systems for their clients. We also sought to complete apreliminary investigation of the accuracy of such individualized systems in a real-world evaluation.Methods: A tablet app was developed that automates the key steps in exercise technique classifier creation through synchronizingvideo and IMU data collection, automatic signal processing, data segmentation, data labeling of segmented videos by an exerciseprofessional, automatic feature computation, and classifier creation. Using a personalized single IMU-based classification system,15 volunteers (12 males, 3 females, age: 23.8 [standard deviation, SD 1.8] years, height: 1.79 [SD 0.07] m, body mass: 78.4 [SD9.6] kg) then completed 4 lower limb compound exercises. The real-world accuracy of the systems was evaluated.Results: The tablet app successfully automated the process of creating individualized exercise biofeedback systems. Thepersonalized systems achieved 89.50% (1074/1200) accuracy, with 90.00% (540/600) sensitivity and 89.00% (534/600) specificityfor assessing aberrant and acceptable technique with a single IMU positioned on the left thigh.Conclusions: A tablet app was developed that automates the process required to create a personalized exercise techniqueclassification system. This tool can be applied to any cyclical, repetitive exercise. The personalized classification model displayedexcellent system accuracy even when assessing acute deviations in compound exercises with a single IMU.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: JMIR
Copyright (published version): 2017 the Authors
Keywords: Personal Sensing;Exercise therapy;Biomedical technology;Lower extremity;Physical therapy speciality
DOI: 10.2196/rehab.7259
Language: en
Status of Item: Peer reviewed
Appears in Collections:Public Health, Physiotherapy and Sports Science Research Collection
Insight Research Collection

Show full item record

Google ScholarTM

Check

Altmetric


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.