School of Computing, Jordanstown Campus, Ulster University, Newtownabbey BT37 0QB, UK.
NetwellCASALA Advanced Research Centre, Dundalk Institute of Technology, A91 K584 Dundalk, Ireland.
Sensors (Basel). 2021 Nov 13;21(22):7560. doi: 10.3390/s21227560.
The ability to monitor Sprained Ankle Rehabilitation Exercises (SPAREs) in home environments can help therapists 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 and users' inability to remember to charge and wear the devices are often the challenges for their usage. In addition, video cameras, which are notable for high frame rates and granularity, are not privacy-friendly. Therefore, this paper proposes the use and fusion of privacy-friendly and Unobtrusive Sensing Solutions (USSs) for data collection and processing during SPAREs in home environments. The present work aims to monitor SPAREs such as dorsiflexion, plantarflexion, inversion, and eversion using radar and thermal sensors. The main contributions of this paper include (i) privacy-friendly monitoring of SPAREs in a home environment, (ii) fusion of SPAREs data from homogeneous and heterogeneous USSs, and (iii) analysis and comparison of results from single, homogeneous, and heterogeneous USSs. Experimental results indicated the advantages of using heterogeneous USSs and data fusion. Cluster-based analysis of data gleaned from the sensors indicated an average classification accuracy of 96.9% with Neural Network, AdaBoost, and Support Vector Machine, amongst others.
在家环境中监测踝关节扭伤康复运动（SPAREs）的能力可以帮助治疗师确定运动是否按照规定进行。虽然可穿戴设备在监测活动中已经显示出高精度和高准确性等优势，但缺点是电池寿命有限，用户无法记住充电和佩戴设备，这往往是其使用的挑战。此外，视频摄像机的帧率和粒度都很高，但不太隐私友好。因此，本文提出在家环境中使用和融合隐私友好和非侵入式感测解决方案（USSs）进行 SPAREs 数据采集和处理。本工作旨在使用雷达和热传感器监测背屈、跖屈、内翻和外翻等 SPAREs。本文的主要贡献包括：（i）在家环境中进行隐私友好的 SPARE 监测；（ii）融合来自同质和异质 USSs 的 SPAREs 数据；（iii）分析和比较来自单个、同质和异质 USSs 的结果。实验结果表明，使用异质 USSs 和数据融合具有优势。基于聚类的传感器数据分析表明，使用神经网络、自适应增强和支持向量机等算法的平均分类准确率为 96.9%。
