Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System.

Area of Science:

• Robotics
• Biomedical Engineering
• Rehabilitation Science

Background:

• Rehabilitation robotics has a long history but faces slow adoption due to complexity and regulatory hurdles.
• Integrating industrial robots into healthcare requires adaptable architectures and standardized applications.

Purpose of the Study:

• To describe an architecture enabling industrial robot application in healthcare ecosystems.
• To evaluate the feasibility of using small industrial robots for patient rehabilitation.

Main Methods:

• Utilized an industrial robot (UR3) controlled by SmartHealth software with 2 Degrees of Autonomy (DOA).
• Collected data using electromyography (EMG) and force sensors at 45 Hz.
• Performed four shoulder rehabilitation exercises: passive, restricted active-assisted, free active-assisted, and Activities of Daily Living (ADL).

Main Results:

• Confirmed the viability of the proposed architecture for rehabilitation.
• Demonstrated the capability of small robots like the UR3 in performing various rehabilitation exercises and ADLs.
• Identified limitations including restricted Range of Motion (ROM) and cost.

Conclusions:

• The proposed architecture facilitates the integration of industrial robots into rehabilitation settings.
• Small robots offer a promising, albeit limited, solution for enhancing patient rehabilitation and daily living activities.