This PhD dissertation focuses on developing improved methods for assessing and reducing fall risk in stroke survivors. Falls are a major concern in this population as they occur more frequently and can lead to catastrophic injuries due to factors like hemiparesis, spasticity, impaired balance and cognitive deficits. The dissertation reviews fall risk factors (FRFs) identified in prior studies and finds balance, gait and mobility metrics to be most significant. It notes current assessment methods rely on subjective scales and lack detailed motion analysis. Dual-task testing is also overlooked despite revealing subtle deficits. To address these gaps, wearable sensors and machine learning are proposed to objectively quantify fall risk. The dissertation first compares outcomes of clinical tests like Timed Up and Go (TUG), Sit-to-Stand (STS), balance and 10 Meter Walk Test (10MWT) between stroke survivors and controls under single and dual-task conditions. Stroke survivors show impaired performance exacerbated by dual-tasking, indicating utility of this approach. The next study instruments the tests with inertial sensors to enable precise motion analysis. Key deficits in turning, transitions and gait are identified in stroke survivors using features like trunk sway and velocities. Dual-tasking disproportionately worsens their performance, stressing limited cognitive-motor resources. A machine learning model using a test battery of instrumented clinical tasks under single and dual-task conditions is then developed to classify fall risk. Random forest model with two features related to medio-lateral sway during dual-task balance and gait speed achieves 91% accuracy. Analysis of sensor configurations finds a single thorax sensor effectively captures fall risk biomechanics. The model could be applied to assess rehabilitation exercises, demonstrating feasibility of tracking risk over time. In conclusion, the dissertation presents wearable sensor-based movement analysis and machine learning models as a quantitative framework for objective fall risk assessment in stroke survivors. Dual-task testing enhances sensitivity. A single thorax sensor capturing key metrics during clinical tests can efficiently evaluate risk. This could assist clinicians in prescribing interventions and tracking rehabilitation progress. With further validation, the approach may be translated to point-of-care screening and remote monitoring to improve prevention and quality of life after stroke.
Mechanical and Industrial Engineering (Ph.D)
Department, Program, or Center
Kate Gleason College of Engineering
Abdollahi, Masoud, "A Comprehensive Machine Learning Approach to Assess Fall Risk in Stroke Survivors: Integrating Common Clinical Tests, Motor-Cognitive Dual-Tasks, Wearable Motion Sensors, and Detailed Motion Analysis" (2023). Thesis. Rochester Institute of Technology. Accessed from
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