A Wearable Sensor-Based Exercise Biofeedback System: Mixed Methods Evaluation of Formulift

Martin Aidan O'Reilly; Patrick Slevin; Tomas Ward; Brian Caulfield

doi:10.2196/mhealth.8115

A Wearable Sensor-Based Exercise Biofeedback System: Mixed Methods Evaluation of Formulift

JMIR Mhealth Uhealth. 2018 Jan 31;6(1):e33. doi: 10.2196/mhealth.8115.

Authors

Martin Aidan O'Reilly^{1

2}, Patrick Slevin^{1

2}, Tomas Ward^{3

4}, Brian Caulfield^{1

2}

Affiliations

¹ Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.
² School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.
³ Insight Centre for Data Analytics, Dublin City University, Dublin, Ireland.
⁴ School of Computing, Dublin City University, Dublin, Ireland.

Abstract

Background: Formulift is a newly developed mobile health (mHealth) app that connects to a single inertial measurement unit (IMU) worn on the left thigh. The IMU captures users' movements as they exercise, and the app analyzes the data to count repetitions in real time and classify users' exercise technique. The app also offers feedback and guidance to users on exercising safely and effectively.

Objective: The aim of this study was to assess the Formulift system with three different and realistic types of potential users (beginner gym-goers, experienced gym-goers, and qualified strength and conditioning [S&C] coaches) under a number of categories: (1) usability, (2) functionality, (3) the perceived impact of the system, and (4) the subjective quality of the system. It was also desired to discover suggestions for future improvements to the system.

Methods: A total of 15 healthy volunteers participated (12 males; 3 females; age: 23.8 years [SD 1.80]; height: 1.79 m [SD 0.07], body mass: 78.4 kg [SD 9.6]). Five participants were beginner gym-goers, 5 were experienced gym-goers, and 5 were qualified and practicing S&C coaches. IMU data were first collected from each participant to create individualized exercise classifiers for them. They then completed a number of nonexercise-related tasks with the app. Following this, a workout was completed using the system, involving squats, deadlifts, lunges, and single-leg squats. Participants were then interviewed about their user experience and completed the System Usability Scale (SUS) and the user version of the Mobile Application Rating Scale (uMARS). Thematic analysis was completed on all interview transcripts, and survey results were analyzed.

Results: Qualitative and quantitative analysis found the system has "good" to "excellent" usability. The system achieved a mean (SD) SUS usability score of 79.2 (8.8). Functionality was also deemed to be good, with many users reporting positively on the systems repetition counting, technique classification, and feedback. A number of bugs were found, and other suggested changes to the system were also made. The overall subjective quality of the app was good, with a median star rating of 4 out of 5 (interquartile range, IQR: 3-5). Participants also reported that the system would aid their technique, provide motivation, reassure them, and help them avoid injury.

Conclusions: This study demonstrated an overall positive evaluation of Formulift in the categories of usability, functionality, perceived impact, and subjective quality. Users also suggested a number of changes for future iterations of the system. These findings are the first of their kind and show great promise for wearable sensor-based exercise biofeedback systems.

Keywords: biomedical technology; exercise therapy; feedback; lower extremity; mHealth; physical therapy specialty; posture.

©Martin Aidan O'Reilly, Patrick Slevin, Tomas Ward, Brian Caulfield. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 31.01.2018.