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Using Near-Infrared Spectroscopy Wearable Devices to Identify Central Versus Peripheral Limitations During Exercise
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A Wearable Sensor-Based Exercise Biofeedback System: Mixed Methods Evaluation of Formulift.

Martin Aidan O'Reilly1,2, Patrick Slevin1,2, Tomas Ward3,4

  • 1Insight Centre for Data Analytics, University College Dublin, Dublin, Ireland.

JMIR Mhealth and Uhealth
|February 2, 2018
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Summary
This summary is machine-generated.

The Formulift mobile health app, using an inertial measurement unit (IMU), effectively tracks exercise technique and repetitions. This wearable sensor-based system shows promise for improving workout safety and effectiveness across various user types.

Keywords:
biomedical technologyexercise therapyfeedbacklower extremitymHealthphysical therapy specialtyposture

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Area of Science:

  • Sports Science
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Formulift is a novel mobile health (mHealth) application integrated with an inertial measurement unit (IMU) sensor.
  • The system analyzes exercise movements in real-time for repetition counting and technique classification.
  • It provides users with feedback for safe and effective exercise performance.

Purpose of the Study:

  • To evaluate the usability, functionality, perceived impact, and subjective quality of the Formulift system.
  • To assess the system's performance with diverse user groups: beginner gym-goers, experienced gym-goers, and S&C coaches.
  • To gather user feedback for future system enhancements.

Main Methods:

  • 15 healthy volunteers (5 beginners, 5 experienced, 5 S&C coaches) participated.
  • Individualized exercise classifiers were created using IMU data.
  • Participants engaged in standardized exercises (squats, deadlifts, lunges) and were evaluated via interviews, System Usability Scale (SUS), and Mobile Application Rating Scale (uMARS).

Main Results:

  • The Formulift system demonstrated good to excellent usability, achieving a mean SUS score of 79.2.
  • Users reported positive experiences with repetition counting, technique classification, and real-time feedback.
  • The app received a median subjective quality rating of 4 out of 5 stars, with users anticipating benefits for technique, motivation, and injury prevention.

Conclusions:

  • The Formulift system received a positive overall evaluation for usability, functionality, perceived impact, and quality.
  • User feedback highlighted areas for improvement in future iterations.
  • This study indicates significant potential for wearable sensor-based exercise biofeedback systems.