IMU Sensors Measurements Towards the Development of Novel Prosthetic Arm Control Strategies

• Published in: IEEE International Conference on Rehabilitation Robotics Vol. 2023; pp. 1 - 6
• Main Authors: Galviati, Riccardo, Boccardo, Nicolo, Canepa, Michele, Di Domenico, Dario, Marinelli, Andrea, Frigo, Carlo Albino, Laffranchi, Matteo, de Michieli, Lorenzo
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.01.2023
• Subjects: Controllability; Electromyography; Health care; IMU; Manipulators; Mechatronics; Motion capture; Prosthesis; Reliability; Robotics; Sensors; Shadow Control
• ISSN: 1945-7901; 1945-7901
• DOI: 10.1109/ICORR58425.2023.10304730

The complexity of the human upper limb makes replicating it in a prosthetic device a significant challenge. With advancements in mechatronic developments involving the addition of a large number of degrees of freedom, novel control strategies are required. To accommodate this need, this study aims at developing an IMU-based control for the HannesARM upper-limb prosthetic device, as a proof-of-concept for new control strategies integrating data-fusion approaches. The natural human control of the upper-limb is based on different inputs that allow adaptive control. To mimic this in prostheses, the implementation of IMUs provides kinematic information of both the stump and the prosthesis to enrich the EMG control. The principle of operation is to decode upper limb movements by using a custom-made system and to replicate them in prosthetic arms improving the control algorithms. To evaluate the system's effectiveness, the custom algorithm's motion extraction was compared to a motion capture system using fifteen able-bodied subjects. The results showed that this system scored 0.16 ± 0.04 and 0.81 ± 0.12 in Root Mean Squared Error and Cross-Correlation compared to the motion capture system. Experimental results demonstrate how this work can extract valuable kinematic information necessary for new and improved control strategies, such as intention detection or pattern recognition, to allow users to perform a broader range of tasks and enhancing in turn their quality of life.