Literature DB >> 27782290

Technology in Rehabilitation: Evaluating the Single Leg Squat Exercise with Wearable Inertial Measurement Units.

Darragh F Whelan1, Martin A O'Reilly, Tomás E Ward, Eamonn Delahunt, Brian Caulfield.   

Abstract

BACKGROUND: The single leg squat (SLS) is a common lower limb rehabilitation exercise. It is also frequently used as an evaluative exercise to screen for an increased risk of lower limb injury. To date athlete / patient SLS technique has been assessed using expensive laboratory equipment or subjective clinical judgement; both of which are not without shortcomings. Inertial measurement units (IMUs) may offer a low cost solution for the objective evaluation of athlete / patient SLS technique.
OBJECTIVES: The aims of this study were to determine if in combination or in isolation IMUs positioned on the lumbar spine, thigh and shank are capable of: (a) distinguishing between acceptable and aberrant SLS technique; (b) identifying specific deviations from acceptable SLS technique.
METHODS: Eighty-three healthy volunteers participated (60 males, 23 females, age: 24.68 + / - 4.91 years, height: 1.75 + / - 0.09 m, body mass: 76.01 + / - 13.29 kg). All participants performed 10 SLSs on their left leg. IMUs were positioned on participants' lumbar spine, left shank and left thigh. These were utilized to record tri-axial accelerometer, gyroscope and magnetometer data during all repetitions of the SLS. SLS technique was labelled by a Chartered Physiotherapist using an evaluation framework. Features were extracted from the labelled sensor data. These features were used to train and evaluate a variety of random-forests classifiers that assessed SLS technique.
RESULTS: A three IMU system was moderately successful in detecting the overall quality of SLS performance (77 % accuracy, 77 % sensitivity and 78 % specificity). A single IMU worn on the shank can complete the same analysis with 76 % accuracy, 75 % sensitivity and 76 % specificity. Single sensors also produce competitive classification scores relative to multi-sensor systems in identifying specific deviations from acceptable SLS technique.
CONCLUSIONS: A single IMU positioned on the shank can differentiate between acceptable and aberrant SLS technique with moderate levels of accuracy. It can also capably identify specific deviations from optimal SLS performance. IMUs may offer a low cost solution for the objective evaluation of SLS performance. Additionally, the classifiers described may provide useful input to an exercise biofeedback application.

Entities:  

Keywords:  Exercise therapy; biomedical technology; inertial measurement units; lower extremity; physical therapy speciality

Mesh:

Year:  2016        PMID: 27782290     DOI: 10.3414/ME16-02-0002

Source DB:  PubMed          Journal:  Methods Inf Med        ISSN: 0026-1270            Impact factor:   2.176


  12 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.  Noninvasive Continuous Monitoring of Vital Signs With Wearables: Fit for Medical Use?

Authors:  Malte Jacobsen; Till A Dembek; Guido Kobbe; Peter W Gaidzik; Lutz Heinemann
Journal:  J Diabetes Sci Technol       Date:  2020-02-17

3.  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

4.  Mobile App to Streamline the Development of Wearable Sensor-Based Exercise Biofeedback Systems: System Development and Evaluation.

Authors:  Martin O'Reilly; Joe Duffin; Tomas Ward; Brian Caulfield
Journal:  JMIR Rehabil Assist Technol       Date:  2017-08-21

5.  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

6.  Validity of a New 3-D Motion Analysis Tool for the Assessment of Knee, Hip and Spine Joint Angles during the Single Leg Squat.

Authors:  Igor Tak; Willem-Paul Wiertz; Maarten Barendrecht; Rob Langhout
Journal:  Sensors (Basel)       Date:  2020-08-13       Impact factor: 3.576

7.  Automatic Classification of Squat Posture Using Inertial Sensors: Deep Learning Approach.

Authors:  Jaehyun Lee; Hyosung Joo; Junglyeon Lee; Youngjoon Chee
Journal:  Sensors (Basel)       Date:  2020-01-08       Impact factor: 3.576

8.  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

9.  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

Review 10.  Quantitative Modeling of Spasticity for Clinical Assessment, Treatment and Rehabilitation.

Authors:  Yesung Cha; Arash Arami
Journal:  Sensors (Basel)       Date:  2020-09-05       Impact factor: 3.576

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