Smart post-stroke virtual assessment using AI-driven analytics

According to the World Bank report, 15% of the world population suffers from a disability such as a stroke, myopathy, neuropathy, spinal cord injuries, bone atrophy, etc. Stroke annually occurs for 15 million people worldwide, and 50,000 are in Canada. For stroke survivors, assessment rehabilitation is often required to assist the affected body parts in regaining control over their mobility. With a substantial portion of the global population experiencing disabilities, including stroke survivors, remote patient monitoring and assessment using sensor technology and IoT devices has become feasible. Identifying and assessing the impaired body part is essential to providing proper treatment and rehabilitation plans. Therefore, monitoring Activities of Daily Living (ADLs) and complex body movements of post-stroke survivors could deliver a wealth of clinically applicable information. Specifically, evaluating stroke ADLs in a clinical setting is constrained to information provided by a healthcare professional and subjective rating scales based on their judgment. Thus, the assessment score and tests for stroke patients are done manually with a physiotherapist's opinion, which is subjective. To address the above-mentioned challenges for stroke survivors and utilize artificial intelligence (AI) in the rehabilitation assessment area, this thesis investigated the problem of iii automatically identifying affected body parts and the severity level of the affected hand. Furthermore, incorporating diverse sensor technologies, encompassing open-source wearable sensor-based (Xsens) and camera-based (Vicon) datasets, enriches the scope and depth of this thesis. Additionally, addressing the scalability of the developed AI model and maintaining the privacy of the patients' dataset is investigated. A novel Multi-Level Meta Learner (MLML) diagnosis model was developed using ensemble learning to accurately classify stroke patients' affected hands from non-affected hands virtually. Moreover, provides an intelligent post stroke severity assessment using a consensus clustering algorithm called Post-Stroke Assessment-Midified Nonnegative Matrix Factorization (PSA-MNMF) inspired by the advances in consensus learning that combine various clustering methods into one united clustering to produce more stable and robust results than individual clustering. The method is the first to investigate the severity levels using unsupervised learning and trunk displacement features in the frequency domain for post-stroke smart assessment. To ensure scalability and patient privacy, a federated learning approach called the Post-Stroke Federated Learning Consensus-Driven Model (PSA-FL-CDM) was proposed to harness the vast datasets, enhance the model's performance, and reduce computational time compared to the centralized model. From a clinical standpoint, there is an increasing recognition of the shift from traditional clinical assessments to a more technologically advanced approach. This shift involves utilizing AI-powered sensors to comprehensively assess neurological deficits, functional capabilities, and activities of daily living and even potentially estimate the quality of life. The primary objective of this thesis was to satisfy the needs of clinically assessing post-stroke, assess and compare the functional capabilities of the affected hand in post-stroke patients and a control group.

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