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A Support Device to Increase Range of Motion for Shoulder Injuries
ME-E-47
Shmuel Mula; shmuel5005@gmail.com Tzuriel Gabay; murhtk33@gmail.com
Advisors: Naama Agassi1, Ms. Nurit Gofman1 1SCE - Shamoon College of Engineering Be’er Sheva
This project focuses on developing a wearable mechanical device designed to reduce strain on the elbow joint, to prevent fatigue, pain, and orthopedic injuries. Our system includes mechanical clamps attached to the forearm and upper arm, a connecting cable, a rail with a sliding pin, and a torsion spring that provides upward force. The integration of a motor, encoder, and sensor is also being considered for controlled movement assistance. The development process involves mechanical design, material selection, and functional prototype testing, with an emphasis on maintaining a lightweight and efficient solution. The end goal is to enhance comfort and reduce elbow load during arm movement.
Keywords: arm movement support, assistive technology, orthopedic assistance, wearable device
Synthetic Membrane Parameter Research Using Machine Learning
ME-E-48
Raz Soffer; razso@ac.sce.ac.il Asaf Mazuz; asafma2@ac.sce.ac.il
Advisors: Dr. Etan Fisher1, Eng. Nurit Gufman2 1SCE - Shamoon College of Engineering, Be’er-Sheva 2The Air Force Technical College, Be’er Sheva
The project’s purpose is to characterize a synthetic membrane using machine learning (ML) models, specifically for accurately extracting data on important parameters by analyzing video footage of the membrane in action.
The creation of our model is based on an existing lab setup and, thus, understanding all aspects of the experimental system is crucial for building and improving on the model. Our initial model is successfully identifying the relevant objects and current results show promise. The work on this project was done with minimal financial investment.
The final stage of our project will be dedicated to refining the experimental system and adding the relevant features for detecting the bubbles and the pressure reading in a time-dependent manner.
Keywords: machine learning models, pressure and bubble detection, synthetic membrane, video analysis