Elias Malak
Mechanical Engineering Student at Purdue | Interested in Aerospace and Space Exploration
- Role
- Member of the Stroke Patient Rehabilitation Tracking Team at Vip At Purdue
- Location
- Indianapolis, IN, US
- LinkedIn followers
- 500 followers
About Elias Malak
I’m a mechanical engineering student at Purdue University with a strong passion for…
Experience
Member of the Stroke Patient Rehabilitation Tracking Team
Aug 2024 — Present · Indianapolis, IN, US
Approximately 80% of stroke survivors experience motor impairments in their upper limbs, impacting independence in daily activities. Traditional clinical assessments, such as the Action Research Arm Test (ARAT) and Modified Ashworth Scale (MAS), are limited by subjectivity and the inability to track detailed movement patterns continuously. To find a solution, the team worked on using an intelligent glove equipped with Inertial Measurement Units (IMUs) and a shape sensor.This project introduces an intelligent glove equipped with IMUs for real-time, objective monitoring of motor recovery. The glove integrates IMUs sewn into Neoprene fabric with 3D-printed components to ensure comfort, durability, accurate acceleration, angular velocity, and orientation tracking. Its modular design allows independent patient placement, facilitating consistent and continuous data collection. Using the data, the team analyzed and generated 3D visualizations of the movements. Machine Learning (ML) was used to detect deviations from healthy movement patterns.In addition, a shape sensor monitors elbow movements to measure the range of motion and smoothness. Polynomial models applied to shape sensor data generate R² values, quantifying how closely the patient’s movements match smooth trajectories. These values offer additional insight into rehabilitation progress by tracking improvements in motor control over time.The IMUs and shape sensors work together to evaluate upper-limb function. This cost-effective, wearable system provides objective feedback and supports at-home monitoring, reducing clinical visits while enabling personalized rehabilitation. Future work will focus on refining the ML models and sensor configurations to enhance accuracy and expand the system’s clinical applicability.
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