Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Fusion of Unobtrusive Sensing Solutions for Sprained Ankle Rehabilitation Exercises Monitoring in Home Environments

Version 1 : Received: 12 August 2021 / Approved: 13 August 2021 / Online: 13 August 2021 (15:12:24 CEST)

How to cite: Ekerete, I.; Garcia-Constantino, M.; Diaz, Y.; Nugent, C.; Mclaughlin, J. Fusion of Unobtrusive Sensing Solutions for Sprained Ankle Rehabilitation Exercises Monitoring in Home Environments. Preprints 2021, 2021080301 (doi: 10.20944/preprints202108.0301.v1). Ekerete, I.; Garcia-Constantino, M.; Diaz, Y.; Nugent, C.; Mclaughlin, J. Fusion of Unobtrusive Sensing Solutions for Sprained Ankle Rehabilitation Exercises Monitoring in Home Environments. Preprints 2021, 2021080301 (doi: 10.20944/preprints202108.0301.v1).

Abstract

The ability to monitor Sprained Ankle Rehabilitation Exercises (SPAREs) in home environments can help therapists to ascertain if exercises have been performed as prescribed. Whilst wearable devices have been shown to provide advantages such as high accuracy and precision during monitoring activities, disadvantages such as limited battery life, users' inability to remember to charge and wear the devices are often the challenges for their usage. Also, video cameras, which are notable for high frame rates and granularity, are not privacy-friendly. This paper, therefore, proposes the use and fusion of unobtrusive and privacy-friendly sensing solutions for data collection and processing during SPAREs in home environments. Two Infrared Thermopile Array (ITA-32) thermal sensors and two Frequency Modulated Continuous Wave (FMCW) Radar sensors were used to simultaneously monitor 15 healthy participants during SPAREs which involved twisting their ankle in 4-fundamental movement patterns namely (i) extension, (ii) flexion, (iii) eversion and (iv) inversion. Experimental results indicated the ability to identify thermal blobs of participants performing the 4 fundamental movement patterns of the human ankle. Cluster-based analysis of data gleaned from the ITA-32 sensors and the FMCW Radar sensors indicated average classification accuracy of 96.9% with K-Nearest Neighbours, Neural Network, AdaBoost, Decision Tree, Stochastic Gradient Descent and Support Vector Machine, amongst others.

Keywords

Unobtrusive Sensing; Data Fusion; Data Mining; Radar Sensing; Thermal Sensing; Sprained Ankle; Infrared Thermopile Array; Home Environment.

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