Literature DB >> 28538326

Technology in Strength and Conditioning Tracking Lower-Limb Exercises With Wearable Sensors.

Martin A OʼReilly1, Darragh F Whelan, Tomas E Ward, Eamonn Delahunt, Brian Caulfield.   

Abstract

Strength and conditioning (S&C) coaches offer expert guidance to help those they work with achieve their personal fitness goals. However, because of cost and availability issues, individuals are often left training without expert supervision. Recent developments in inertial measurement units (IMUs) and mobile computing platforms have allowed for the possibility of unobtrusive motion tracking systems and the provision of real-time individualized feedback regarding exercise performance. These systems could enable S&C coaches to remotely monitor sessions and help gym users record workouts. One component of these IMU systems is the ability to identify the exercises completed. In this study, IMUs were positioned on the lumbar spine, thighs, and shanks on 82 healthy participants. Participants completed 10 repetitions of the squat, lunge, single-leg squat, deadlift, and tuck jump with acceptable form. Descriptive features were extracted from the IMU signals for each repetition of each exercise, and these were used to train an exercise classifier. The exercises were detected with 99% accuracy when using signals from all 5 IMUs, 99% when using signals from the thigh and lumbar IMUs and 98% with just a single IMU on the shank. These results indicate that a single IMU can accurately distinguish between 5 common multijoint exercises.

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Year:  2017        PMID: 28538326     DOI: 10.1519/JSC.0000000000001852

Source DB:  PubMed          Journal:  J Strength Cond Res        ISSN: 1064-8011            Impact factor:   3.775


  4 in total

1.  Feature-Free Activity Classification of Inertial Sensor Data With Machine Vision Techniques: Method, Development, and Evaluation.

Authors:  Jose Juan Dominguez Veiga; Martin O'Reilly; Darragh Whelan; Brian Caulfield; Tomas E Ward
Journal:  JMIR Mhealth Uhealth       Date:  2017-08-04       Impact factor: 4.773

2.  Using the VERT wearable device to monitor jumping loads in elite volleyball athletes.

Authors:  Faraz Damji; Kerry MacDonald; Michael A Hunt; Jack Taunton; Alex Scott
Journal:  PLoS One       Date:  2021-01-22       Impact factor: 3.240

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

Authors:  Martin Aidan O'Reilly; Patrick Slevin; Tomas Ward; Brian Caulfield
Journal:  JMIR Mhealth Uhealth       Date:  2018-01-31       Impact factor: 4.773

4.  Supervised Machine Learning Applied to Wearable Sensor Data Can Accurately Classify Functional Fitness Exercises Within a Continuous Workout.

Authors:  Ezio Preatoni; Stefano Nodari; Nicola Francesco Lopomo
Journal:  Front Bioeng Biotechnol       Date:  2020-07-07
  4 in total

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