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At the moment, RIOT mainly focuses on hand training, specifically hand opposition and hand strengthening exercises, which are important in stroke rehabilitation.However, RIOT is designed in a modular way, so the system can be expanded to include other limb training such as the arm, wrist, or leg.

For government hospitals, we plan to sell RIOT in modules, where the government can purchase a license to use the system centrally in public hospitals. This allows patients to receive treatment at a low cost or even free, especially for the B40 income group and senior citizens. For private hospitals, we plan to charge RM90 per 1-hour session. This rate is significantly cheaper than conventional manual physiotherapy sessions, which can reach RM180 or more.

RIOT is currently at TRL 4. At this level, the system has been developed into a functional prototype and has gone through some initial tests in a lab or controlled environment, including user simulations and basic gesture functionality evaluations.Although the system shows good potential, it still requires more extensive effectiveness testing, particularly in actual clinical environments with real patients, before it can advance to the next TRL.

We are aiming to complete RIOT as a stable version, ready for commercialization by next year.However, there are several key factors we need to consider before the official launch especially on the system's effectiveness in various usage scenarios and its ability to consistently provide accurate feedback for every gesture exercise.

RIoT can give patients exercise data in percentage & count. These data will be stored in online report of this system automatically and can be monitored by the therapist. Therapist can monitor patient progress and plan the recovery journey for the patients.

Patient only can see the percentage feedback. For the feedback from the therapist, we are trying to make it visible for patients. But for now, therapist need to contact them personally or patient need to do an appointment for the consultation about their recovery journey plan.

As the system is still new and in the development phase, we focus on hand first. But after this, we try to extend it to other parts like arms, legs and full body. The feature that will be added is known as Imagy. Maybe in these parts we need to include sensors to get more accurate reading when the patient use it.

Our hand is the important part of our body. It helps us do our daily tasks like eating, writing and even some of us use it for communication. That's why we focus on hand first. Further more, if patient does not do hand exercises within 6 months, the hand will be totally stroke or fully numb. So, this system will help them to do exercise regularly. Hand can make exercise, but face can not.

In our study, we tested the system with real stroke patients to evaluate the accuracy and effectiveness of the method that we used, ensuring it delivers reliable results and is practical for various stroke rehabilitations situations.

The first copyright which is calibration of hand gesture recognition for stroke rehabilitation internet-of-things (RIOT) using mediapipe in smart healthcare systems is to protect the source code and the method and calibration that are implemented in this study. The second copyright which is development of a web-based medical information system rehabilitation internet-of-things (RIOT), it protects the whole website and system that has been developed.

Currently, we promote our study by publishing research papers and participating in various competitions that are related to this field and for now we received financial support from Ministry of Higher Education, Malaysia.