IoT based Automated Rehabilitation using 3D Printed Hand and Health Monitoring for Paralyzed Patients
Paralysis is a condition characterized by the loss of muscle function in part of the body, leading to impaired movement and control. It can significantly impact an individual's daily life, mobility, and overall well-being. Paralysis can result from various factors, including spinal cord injuries, strokes, traumatic injuries, neurological disorders, and certain medical conditions. According to the World Health Organization (WHO), millions of people worldwide live with some form of paralysis, with varying degrees of severity. Tracking the improvement of patients using these techniques might be difficult. Rehabilitation results inadequate monitoring of critical health factors including temperature and blood oxygen levels. The suggested approach is creating a 3D - printed hand incorporating Internet of Things sensors. Paralysis patients can benefit from this hand real-time sensory feedback and customized therapy routines. IoT sensors are incorporated in the hand to track the patient's hand movements, blood oxygen saturation (SpO2), heart rate, and muscle activity (EMG). Arduino UNO serves as the central controller that facilitates automation, while the Wi-Fi module and voice control enhance user interaction. Additionally, Peltier modules keep the affected extremities at their ideal temperature, improving patient comfort and promoting blood flow. Suggested approach provides a multi-dimensional approach to enhance their entire quality of life and health condition for people with this difficult medical condition by combining all those features.
Authors
- Shashank Govindaraj
- S. Maheswari
- Priyadarshini Loganathan (ORCID: https://orcid.org/0000-0003-4247-2412)
- M. Ponkarthika
- J. S. Sharanya Nivasini
Institutions
- Tabor College (AU)
Publication Details
- Published
- 2024-11-18
- DOI
- https://doi.org/10.1109/icdici62993.2024.10810963
- Citations
- 6
- Primary Topic
- Stroke Rehabilitation and Recovery
- Type
- article
- Field-Weighted Citation Impact
- 1.50