Rehabilitation Internet Of Things (RIOT)
Revolutionize your rehabilitation experience with RIOT
Revolutionize your rehabilitation experience with RIOT
Our platform uses hand gesture technology to help patients practice hand movements and track progress in a fun and interactive way. With just a camera, patients can perform exercises that are recorded and analyzed to support improvement over time.
Our goal is to provide stroke survivors with a simple tool to support their rehabilitation and keep everyone involved informed and connected.Our system uses hand gestures to help you practice exercises and tracks your progress over time with just a camera. Our service also includes:
.png)
of respondents prefer to use a system to monitor the progress in rehabilitation
of respondents believe that integrating internet of things(IoT) technology into rehabilitation can improve overall quality of life post-stroke
of respondents prefer doing rehabilitation exercises and be monitored at home.
of respondents have confidence that technology can be integrated for stroke rehabilitation purposes
We’re looking for individuals like you to share their journey and insights! By taking this brief survey, you can help us better understand the challenges and needs of people recovering from stroke. Take the survey today and let your voice be heard!
Take the Survey!Discover our journal on Rehabilitation and the Internet of Things, exploring innovative ways technology supports recovery.
The study outlines how MediaPipe can be integrated into a web-based system (the RIOT framework) to support stroke patients’ recovery by tracking hand gestures during exercises. It discusses the calibration of this technology to ensure accuracy in various conditions (lighting and camera angles) and emphasizes the importance of real-time feedback to enhance patient engagement and effectiveness in home-based therapy.
This paper presents a prototype of a web-based medical information system that integrates IoT and hand gesture recognition to assist stroke patients with remote rehabilitation. The system enables patients to perform exercises at home while allowing healthcare providers to monitor their progress remotely.
This study emphasizes how deep learning enables the system to offer personalized feedback to patients, ensuring that each exercise session is correctly monitored and recorded. This integration of deep learning with the RIOT environment demonstrates a promising step toward cost-effective, accessible rehabilitation solutions.
This system is designed to help stroke patients recover hand motor function using hand gesture detection technology and an online database. It allows patients to track their progress, while healthcare professionals can manage and analyze data to improve rehabilitation outcomes.
The system uses camera-based hand gesture recognition to monitor and track hand movements. This provides real-time feedback on the patient’s exercises, helping them improve coordination and strength in their hands.
This system is primarily intended for stroke patients, as well as healthcare providers like doctors and physical therapists who work with them. Researchers in rehabilitation can also benefit from the data for further studies.
The RIOT website combines Internet of Things (IoT) technology with hand gesture cameras. The IoT component helps gather data from hand movements, while the camera records and analyzes these movements in real-time, which is then stored in a secure database.
This system aims to provide efficient hand motor rehabilitation, which can be tracked and reviewed. It helps stroke patients become more independent, and the data collected can be used by healthcare providers to tailor patient care.
If you have any questions or need assistance with using RIOT website, please do not hesitate to reach out.
Excellence of Cyber Security
Level 3, Kulliyah of Information Communication and Technology, International Islamic University Malaysia,Jln Gombak, 53100 Kuala Lumpur, Selangor
+03-64215646
anwarzain@iium.edu.my
Mon-Fri: 9:00AM - 5:00PM
