Back close

Machine Learning based Hand Orthotic Device

School: School of Engineering

Project Incharge:Dr. R. Rajesh kannan
Machine Learning based Hand Orthotic Device

Robotic systems are rapidly emerging as easy-to-use rehabilitation tools that enhance several of health-related infirmities. Frequently, stroke and spinal cord -injuries are impair normal healthful living by paralysis, albeit the affected patient’s brains may remain functional. Such victims need long-term rehabilitation, by qualified therapists to regain motor and sensory controls, beyond the affordable reach of common citizens. This is where reasonably priced robotic exoskeleton systems come to their rescue. we are using a novel design for hand orthosis, which is a lightweight pliable prototype and is been calibrated to fit any average-sized hand. This device is controlled with a voice recognition module, IOT and Bluetooth. Our device can be used for picking up the objects and give an extra grasp to the patients. Empirical results validate the efficacy, reliability, and robustness of the orthotic device that can be used in the rehabilitation of stroke patients.

Related Projects

Gesture Controlled Automation For Physically Impaired
Gesture Controlled Automation For Physically Impaired
Integrative Health and Wellbeing – Strengthening Tribal Health with Preventative Care and Awareness
Integrative Health and Wellbeing – Strengthening Tribal Health with Preventative Care and Awareness
Self-E
Self-E
A Robust Approach for Improving the Accuracy of IMU based Indoor Mobile Robot Localization
A Robust Approach for Improving the Accuracy of IMU based Indoor Mobile Robot Localization
Development of a Real-time, Process Control Method Based on Neural Network Model Using Feedback of Weld Pool Geometric Parameters Measured by a Vision-based Technique and Experimental Verification for Automated Arc Welding Processes
Development of a Real-time, Process Control Method Based on Neural Network Model Using Feedback of Weld Pool Geometric Parameters Measured by a Vision-based Technique and Experimental Verification for Automated Arc Welding Processes
Admissions Apply Now