Smart RIoT Chatbot

Rehabilitation Internet Of Things (RIOT)

Revolutionize your rehabilitation experience with RIOT

About Us

How can RIOT help in your rehabilitation journey?

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.

Easy Hand Exercise Tracking
Support for Recovery
Easy to Use and Accesible
Detect Open Hand
Detect Close Hand

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:

Hand Grip
Finger Opposition

96%

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

Stroke Survivors: Your Story Matters!

Experienced a mild stroke? Watch this video to see how sharing your journey can help others. Take our quick survey to contribute to better stroke recovery support!

Join the Survey!

Evaluation of RIoT Accuracy vs. Manual Measurements

The following graphs evaluate how closely the RIoT system matches manual clinical measurements for two key rehabilitation tasks: Hand Strengthening (HS) and Hand Opposition (HO).

What does tolerance mean? – Tolerance (±20, ±25, ±30, ±35) represents how much variation is allowed between RIoT’s measurement and the manual measurement. For example, ±20 means the RIoT reading is counted as correct only if it falls within 20 units of the manual value. Higher tolerance gives a wider acceptable range, so accuracy naturally increases.

How to read the graphs? – The bars show the percentage of RIoT readings that are within the chosen tolerance. A higher percentage means the system is more reliable compared to manual measurement. For instance, RIoT achieves 69% accuracy at ±20 in Hand Strengthening, but improves to 92% at ±35. This shows that even at stricter tolerances the system is reasonably accurate, and it becomes highly reliable with wider thresholds.

Hand Strengthening (HS) Accuracy

Accuracy improves from 69% at ±20 to 92% at ±35, showing that RIoT’s hand-strengthening readings align closely with manual data.

Hand Opposition (HO) Accuracy

HO measurements are highly consistent: starting at 83% (±20) and reaching 94% (±30 and ±35). This demonstrates strong reliability even at stricter tolerances.

Overall Accuracy (HS + HO Combined)

When both exercises are considered together, accuracy rises from 76% at ±20 to 93% at ±35, confirming that RIoT provides reliable results comparable to manual evaluation.

Our Journals

Discover our journal on Rehabilitation and the Internet of Things, exploring innovative ways technology supports recovery.

Calibrating Hand Gesture Recognition for Stroke RIOT Using MediaPipe in Smart Healthcare

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.

Web-Based Medical Information System for Stroke RIOT Patients: A Prototype

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.

Design and Implementation of a Deep Learning Based Hand Gesture Recognition System for RIOT Environments using MediaPipe

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.

Ts. Dr. Ahmad Anwar Zainuddin

Manager anwarzain@iium.edu.my

Dr. Mohd Khairul Azmi Bin Hassan

Web Developer mkazmi@iium.edu.my

Dr. Amir Aatieff Amir Hussin

AI Specialist amiraatieff@iium.edu.my

Dr.Rizal Bin Mohd. Nor

Cyber Security rizalmohdnor@iium.edu.my

Ts. Dr. Dini Oktarina Dwi Handayani

AI Specialist dinihandayani@iium.edu.my

Dr. Asmarani Ahmad Puzi

Rehab Specialist asmarani@iium.edu.my

Md Sariful Islam

IOT Trainer sarif2435@gmail.com

Nik Nor Muhammad Saifudin Nik Mohd Kamal

Assistant web developer saifudinkamalmcst@gmaill.com

Frequently Asked Questions

1. What is the Virtual Hand Gesture-Driven Stroke Patient Database Management System?

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.

2. How does hand gesture detection work in RIOT website?

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.

3. Who can use RIOT website?

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.

4. What technology does RIOT website use?

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.

5. What are the main benefits of using RIOT website?

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.

Contact

If you have any questions or need assistance with using RIOT website, please do not hesitate to reach out.

Address

Excellence of Cyber Security

Level 3, Kulliyah of Information Communication and Technology, International Islamic University Malaysia,Jln Gombak, 53100 Kuala Lumpur, Selangor

Call Us

+03-64215646

Email Us

anwarzain@iium.edu.my

Open Hours:

Mon-Fri: 9:00AM - 5:00PM

Rehabilitation Internet-of-Things (RIOT)