Rethinking Health Recommender Systems for Active Aging: An Autonomy-Based Ethical Analysis

Health Recommender Systems are promising Articial-Intelligence-based tools endowing healthy lifestyles and therapy adherence in healthcare and medicine. Among the most supported areas, it is worth mentioning active aging. However, current HRS supporting AA raise ethical challenges that still need to be properly formalized and explored. This study proposes to rethink HRS for AA through an autonomy-based ethical analysis. In particular, a brief overview of the HRS' technical aspects allows us to shed light on the ethical risks and challenges they might raise on individuals' well-being as they age. Moreover, the study proposes a categorization, understanding, and possible preventive/mitigation actions for the elicited risks and challenges through rethinking the AI ethics core principle of autonomy. Finally, elaborating on autonomy-related ethical theories, the paper proposes an autonomy-based ethical framework and how it can foster the development of autonomy-enabling HRS for AA

Data-driven Rehabilitation Development for Stroke Patients Using Machine Learning Techniques

A portable motion capture system that can guide a stroke patient’s rehabilitation session will help them regain motion in their arm quicker and save money on medical bills. The goal of this dissertation was to investigate stroke patient motions to lay the groundwork for such a system. Specifically, the dissertation focused on developing an automated system for stroke diagnostics and task-oriented rehabilitation. This form of telerehabilitation is new to the field since previous systems do not create skill-based instructions for the patient. The system created in this dissertation was demonstrated to classify a patient’s stroke severity using supervised machine learning techniques. It was shown how this information can be used to create a personalized rehabilitation protocol for that patient. For this study, inertial measurement units were used to collect joint kinematics and kinetics during reaching tasks for 10 healthy and 17 stroke subjects, and from that data machine learning features were defined for a support vector machine classification algorithm. These parameters were validated against a gold standard optical tracking motion capture system (Optitrack, NaturalPoint, Inc.) where the joint results were calculated using the Motion Monitor Biomechanics engine. The kinematic and kinetic features extracted from these patients for the machine learning model also served a dual purpose by creating a hierarchal rehabilitation session by categorizing reaching tasks by difficulty. The features used to determine a patient’s task hierarchy for rehabilitation were determined based on the support vector machine algorithm that classified stroke severity. Task rankings were developed for stroke individual stroke v subjects as well as for classes of stroke subjects. The rankings were unique for classes and for individuals, but their general orders coincided with intuitive task difficulty