Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation
|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||Online since:||2018-05-04T11:48:05Z||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||Journal:||JMIR Rehabilitation and Assistive Technologies||Volume:||4||Issue:||2||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||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Public Health, Physiotherapy and Sports Science Research Collection|
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