Data-driven Implementations for Enhanced Healthcare Internet-of-Things Systems
Time: Fri 2021-12-10 10.00
Video link: zoom link for online defense
Subject area: Information and Communication Technology
Doctoral student: Amleset Kelati , Elektronik och inbyggda system
Opponent: Professor Bengt Oelmann,
Supervisor: Hannu Tenhunen, Integrerade komponenter och kretsar; Juha Plosila, ; Nerey Mvungi, ; Ethiopia Nigussie,
Healthcare monitoring systems based on the Internet of Things (IoT) areemerging as a potential solution for reducing healthcare costs by impacting and improving the quality of health care delivery. The rising numberof elderly and chronic patient population in the world and the associatedhealthcare costs urges the application of IoT technology to improve andsupport the health care services. This thesis develops and integrates twoIoT-based healthcare systems aiming to support elderly independent livingat home. The first one involves using IoT-based remote monitoring for paindetection, while the second one detects behavioral changes caused by illnessvia profiling the appliances’ energy usage.In the first approach, an Electromyography (EMG )sensor node with aWireless Fidelity (Wi-Fi) radio module is designed for monitoring the painof patients living at home. An appropriate feature-extraction and classification algorithm is applied to the EMG signal. The classification algorithmachieves 98.5% accuracy for the experimental data collected from the developed EMG sensor node, while it achieves 99.4% classification accuracy forthe clinically approved pain intensity dataset. Moreover, the experimentalresults clearly show the relevance of the proposed approaches and provetheir suitability for real-life applications. The developed sensor node for thepain level classification method is beneficial for continuous pain assessmentto the smart home-care community.As a complement to the first approach, in the second approach, an IoTbased smart meter and a set of appliance-level load profiling methods aredeveloped to detect the electricity usage of users’ daily living at home, whichindirectly provides information about the subject’s health status. The thesishas formulated a novel methodology by integrating Non-intrusive ApplianceLoad Monitoring (NIALM) analysis with Machine Learning- (ML) basedclassification at the fog layer. The developed method allows the detectionof a single appliance with high accuracy by associating the user’s Activitiesof Daily Living (ADL). The appliances detection is performed by employinga k-Nearest Neighbors (k-NN) classification algorithm. It achieves 97.4% accuracy, demonstrating its high detection performance. Due to the low cost and reusability advantages of Field Programmable Gate Arrays (FPGA),the execution of k-NN for appliances classification model is performed onan FPGA. Its classification performance was comparable with other computing platforms, making it a cost-effective alternative for IoT-based healthcare assessment of daily living at home. The developed methods have haspractical application in assisting real-time e-health monitoring of any individual who can remain in the comfort of their normal living environment.