Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

Area of Science:

• Biomedical Engineering
• Rehabilitation Robotics
• Health Informatics

Background:

• Robotic devices are increasingly used for stroke survivor rehabilitation, with successful clinical transitions.
• Home-based robotic rehabilitation remains a challenge due to safety, monitoring, and data transmission concerns.

Purpose of the Study:

• To advance current robot-assisted in-home rehabilitation by presenting a novel framework.
• To enable safe and effective upper limb training for stroke survivors in a home environment.

Main Methods:

• Introduction of a cost-effective, wearable upper limb robotic orthosis for home use.
• Implementation of an Internet of Robotics Things (IoRT) framework for remote supervision and data management.
• Utilization of a machine learning-based protocol for sensor data analysis and decision-making.

Main Results:

• Experimental results from a proof-of-concept study demonstrate the feasibility of the proposed home-based training paradigm.
• Low mean absolute errors in predicting wrist, elbow, and shoulder angles indicate system accuracy.
• The framework addresses technological barriers for IT experts in health-related domains.

Conclusions:

• The proposed novel framework facilitates home-based robotic rehabilitation for stroke survivors.
• It overcomes technological barriers, paving the way for telerehabilitation systems.
• This approach increases the implementation of home-based robotic rehabilitation, improving patient outcomes.