Literature DB >> 28731981

Technology in Strength and Conditioning: Assessing Bodyweight Squat Technique With Wearable Sensors.

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

Abstract

O'Reilly, MA, Whelan, DF, Ward, TE, Delahunt, E, and Caulfield, BM. Technology in strength and conditioning: assessing bodyweight squat technique with wearable sensors. J Strength Cond Res 31(8): 2303-2312, 2017-Strength and conditioning (S&C) coaches offer expert guidance to help those they work with achieve their personal fitness goals. However, it is not always practical to operate under the direct supervision of an S&C coach and consequently individuals are often left training without expert oversight. 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 individuals record their workout performance. One aspect of such technologies is the ability to assess exercise technique and detect common deviations from acceptable exercise form. In this study, we investigate this ability in the context of a bodyweight (BW) squat exercise. Inertial measurement units were positioned on the lumbar spine, thighs, and shanks of 77 healthy participants. Participants completed repetitions of BW squats with acceptable form and 5 common deviations from acceptable BW squatting technique. Descriptive features were extracted from the IMU signals for each BW squat repetition, and these were used to train a technique classifier. Acceptable or aberrant BW squat technique can be detected with 98% accuracy, 96% sensitivity, and 99% specificity when using features derived from all 5 IMUs. A single IMU system can also distinguish between acceptable and aberrant BW squat biomechanics with excellent accuracy, sensitivity, and specificity. Detecting exact deviations from acceptable BW squatting technique can be achieved with 80% accuracy using a 5 IMU system and 72% accuracy when using a single IMU positioned on the right shank. These results suggest that IMU-based systems can distinguish between acceptable and aberrant BW squat technique with excellent accuracy with a single IMU system. Identification of exact deviations is also possible but multi-IMU systems outperform single IMU systems.

Entities:  

Mesh:

Year:  2017        PMID: 28731981     DOI: 10.1519/JSC.0000000000001957

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


  8 in total

Review 1.  Wearable Inertial Sensor Systems for Lower Limb Exercise Detection and Evaluation: A Systematic Review.

Authors:  Martin O'Reilly; Brian Caulfield; Tomas Ward; William Johnston; Cailbhe Doherty
Journal:  Sports Med       Date:  2018-05       Impact factor: 11.136

2.  Classification of Plank Techniques Using Wearable Sensors.

Authors:  Zong-Rong Chen; Wei-Chi Tsai; Shih-Feng Huang; Tzu-Yi Li; Chen-Yi Song
Journal:  Sensors (Basel)       Date:  2022-06-14       Impact factor: 3.847

3.  Wearable Motion Sensor Device to Facilitate Rehabilitation in Patients With Shoulder Adhesive Capsulitis: Pilot Study to Assess Feasibility.

Authors:  Yu-Pin Chen; Chung-Ying Lin; Ming-Jr Tsai; Tai-Yuan Chuang; Oscar Kuang-Sheng Lee
Journal:  J Med Internet Res       Date:  2020-07-23       Impact factor: 5.428

4.  Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial.

Authors:  Alessandro Luna; Lorenzo Casertano; Jean Timmerberg; Margaret O'Neil; Jason Machowsky; Cheng-Shiun Leu; Jianghui Lin; Zhiqian Fang; William Douglas; Sunil Agrawal
Journal:  Sci Rep       Date:  2021-09-13       Impact factor: 4.379

5.  Thoracolumbar And Lumbopelvic Spinal Alignment During The Deadlift Exercise: A Comparison Between Men And Women.

Authors:  Victor Bengtsson; Ulrika Aasa; Fredrik Öhberg; Lars Berglund
Journal:  Int J Sports Phys Ther       Date:  2022-10-02

6.  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

7.  Clinician perceptions of a prototype wearable exercise biofeedback system for orthopaedic rehabilitation: a qualitative exploration.

Authors:  Rob Argent; Patrick Slevin; Antonio Bevilacqua; Maurice Neligan; Ailish Daly; Brian Caulfield
Journal:  BMJ Open       Date:  2018-10-25       Impact factor: 2.692

8.  The Importance of Real-World Validation of Machine Learning Systems in Wearable Exercise Biofeedback Platforms: A Case Study.

Authors:  Rob Argent; Antonio Bevilacqua; Alison Keogh; Ailish Daly; Brian Caulfield
Journal:  Sensors (Basel)       Date:  2021-03-27       Impact factor: 3.576

  8 in total

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