Dr Alireza Ahrabian
Dr Alireza Ahrabian is a Research Fellow in the Department of Electronic Engineering at the University of Surrey. He completed his PhD degree in signal processing and her MEng in Electrical Engineering at Imperial College London, in 2014 and 2010, respectively.
My main research interests are in detecting changes in time series data. Namely, my work focuses on developing both sequential and batch processing algorithms. Furthermore, I have interests in Bayesian approaches and statistical signal processing techniques.
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.