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Recognising Bone Loading Exercises In Older Adults Using Machine Learning

Enshaeifar, Shirin, Farajidavar, N, Ahrabian, Alireza, Barnaghi, Payam, Hannam, K, Deere, K, Tobias, JH and Allison, Sarah (2017) Recognising Bone Loading Exercises In Older Adults Using Machine Learning Medicine & Science in Sports & Exercise, 49 (5S).

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Abstract

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.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Enshaeifar, Shirins.enshaeifar@surrey.ac.ukUNSPECIFIED
Farajidavar, NUNSPECIFIEDUNSPECIFIED
Ahrabian, Alirezaa.ahrabian@surrey.ac.ukUNSPECIFIED
Barnaghi, PayamP.Barnaghi@surrey.ac.ukUNSPECIFIED
Hannam, KUNSPECIFIEDUNSPECIFIED
Deere, KUNSPECIFIEDUNSPECIFIED
Tobias, JHUNSPECIFIEDUNSPECIFIED
Allison, Sarahs.allison@surrey.ac.ukUNSPECIFIED
Date : May 2017
Identification Number : 10.1249/01.mss.0000518712.49164.ed
Copyright Disclaimer : © 2017 American College of Sports Medicine
Depositing User : Melanie Hughes
Date Deposited : 22 Sep 2017 13:27
Last Modified : 22 Sep 2017 14:30
URI: http://epubs.surrey.ac.uk/id/eprint/842384

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