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Recognising Bone Loading Exercises In Older Adults Using Machine Learning: 2305 Board #318 June 1 2: 00 PM - 3: 30 PM

Enshaeifar, Shirin1; Farajidavar, Nazli1; Ahrabian, Alireza1; Barnaghi, Payam1; Hannam, Kimberly2; Deere, Kevin2; Tobias, Jon H.2; Allison, Sarah J.1

Medicine & Science in Sports & Exercise: May 2017 - Volume 49 - Issue 5S - p 650
doi: 10.1249/01.mss.0000518712.49164.ed
D-74 Free Communication/Poster - Physical Activity Assessment in Adults Thursday, June 1, 2017, 1:00 PM - 6:00 PM Room: Hall F

1University of Surrey, Guildford, United Kingdom. 2Bristol University, Bristol, United Kingdom.

(No relationships reported)

Machine learning has been used to accurately recognise physical activity patterns; however, classifiers for recognising targeted bone loading exercises have not been developed.

PURPOSE: The purpose of this study was to determine the accuracy of machine learning models for classifying the intensity of exercises necessary for bone adaption in older adults.

METHODS: Triaxial accelerometer data was collected from forty-four older participants (60-70 yrs) wearing a GCDC X16-1C accelerometer on their hip during three aerobics classes consisting of impact aerobic exercises performed at high and low intensities. Multi-class support vector machine (M-SVM) classifiers were trained in parallel for activity type detections where one classifier trained with low intensity activity samples and the other with high intensity samples. In a multi-view scoring manner, the classification confidence of these two learners was utilised for predicting the activity intensity. The leave-one-out cross-validation technique was used for assessment purpose.

RESULTS: Overall recognition accuracy of the M-SVM classifier for detecting exercise intensity was 73%. For each aerobics class, the M-SVM classifier accurately recognised exercise intensity by 82%, 73% and 65%.

CONCLUSIONS: Machine learning techniques such as M-SVM accurately recognised the intensity of bone promoting exercises from triaxial accelerometer data in community-dwelling older adults. First results of the developed classifier demonstrate significant potential of machine learning models for the evaluation of exercise adherence and performance in older adults.

© 2017 American College of Sports Medicine