Making Sense of Shoulder Exercise: Measuring the Accuracy of an Artificial Intelligence Model to Classify Shoulder Exercise via Wearable Sensors Among People With and Without Rotator Cuff Tendinopathy.
This study aimed to compare the accuracy of machine learning classification for three commonly prescribed shoulder exercises in people with and without rotator cuff tendinopathy. Eighteen participants with rotator cuff tendinopathy (mean age 54.2, SD 13.2; 50% female), followed by eighteen matched c...
| Publicado en: | European Journal of Sport Science Vol. 26; no. 5; pp. 1 - 14 |
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| Autores principales: | , , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193491139&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193491139 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 17461391 IX3 jtl: European Journal of Sport Science issn: 17461391 maglogo: Y pubinfo: dt: May2026 vid: 26 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 193491139 193491139 193491139 10.1002/ejsc.70167 193491139 ppf: 1 ppct: 13 formats: tig: atl: Making Sense of Shoulder Exercise: Measuring the Accuracy of an Artificial Intelligence Model to Classify Shoulder Exercise via Wearable Sensors Among People With and Without Rotator Cuff Tendinopathy. aug: au: Naunton, Josh Jiang, Yanran Bini, Rodrigo Kidgell, Dawson Bennell, Kim Haines, Terry Kulić, Dana Malliaras, Peter affil: Department of Physiotherapy, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Science, Monash Musculoskeletal Research Unit (MMRU), Monash University, Melbourne, Australia sug: subj: Rotator Cuff Injuries Rehabilitation Tendinopathy Rehabilitation Shoulder Physiology Therapeutic Exercise Methods Wearable Sensors Machine Learning Algorithms Classification Artificial Intelligence Classification Models, Theoretical Sensitivity and Specificity Human Adult Middle Age Aged Male Female Prospective Studies Nonrandomized Trials Nonexperimental Studies Case Control Studies Descriptive Statistics Muscle Strength Accelerometry Random Forest Convolutional Neural Networks Concurrent Validity Comparative Studies Wrist Arm Torso T-Tests Parametric Statistics Mann-Whitney U Test Nonparametric Statistics Data Analysis Software Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Male Female ab: This study aimed to compare the accuracy of machine learning classification for three commonly prescribed shoulder exercises in people with and without rotator cuff tendinopathy. Eighteen participants with rotator cuff tendinopathy (mean age 54.2, SD 13.2; 50% female), followed by eighteen matched controls completed a laboratory‐based shoulder strength testing protocol. Three exercises were performed (shoulder press, lateral raise and bent over row) while wearing three inertial measurement (IMU) sensors (Axivity, Ax6 ‐ 3 axis accelerometry and gyroscope at 100 Hz and 1000°/sec respectively) positioned on the wrist arm and trunk. Data were analysed and accuracy was compared between common machine learning algorithms for those with rotator cuff tendinopathy and healthy matched controls in a subject‐dependent and subject‐independent model. The best accuracy scores for the subject‐dependent results were achieved by a random forest algorithm; 96.12% (3‐sensor combined system) for those with rotator cuff tendinopathy. For the subject‐independent results best accuracy scores were achieved by a convolutional neural network algorithm; 94.55% (3‐sensors) for the healthy controls without shoulder pain. K‐fold cross validation confusion matrix results by exercise type for the entire cohort show 97% accuracy (shoulder press), 95.5% (lateral raise) and 90.7% (bent over row) (3‐sensors, CNN subject‐independent analysis). Machine learning classification of 3 different shoulder exercises in people with rotator cuff tendinopathy and matched healthy controls demonstrate most accurate results using a CNN algorithm for subject‐independent analysis and a RF algorithm for subject‐dependent analysis. Results were similar for both those with rotator cuff tendinopathy and their matched healthy controls. Highlights: Shoulder exercise can be classified with good accuracy scores using an inertial measurement unit (IMU) accelerometer and gyroscope sensor placed on the wrist.Subject‐dependent and subject‐independent models demonstrate comparable results depending on the chosen machine learning algorithm.Exercise classification based on a wearable IMU shows similar results between those with rotator cuff tendinopathy and matched healthy controls without shoulder pain. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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