Gopinath, D. | bioRxiv Science

bioRxiv2023

An Exploratory Multi-Session Study of Learning High-Dimensional Body-Machine Interfacing for Assistive Robot Control

Individuals who suffer from severe paralysis often lose the capacity to perform fundamental body movements and everyday activities. Empowering these individuals with the ability to operate robotic arms, in high-dimensions, helps to maximize both functional utility and human agency. However, high-dimensional robot teleoperation currently lacks accessibility due to the challenge in capturing high-dimensional control signals from the human, especially in the face of motor impairments. Body-machine interfacing is a viable option that offers the necessary high-dimensional motion capture, and it moreover is noninvasive, affordable, and promotes movement and motor recovery. Nevertheless, to what extent body-machine interfacing is able to scale to high-dimensional robot control, and whether it is feasible for humans to learn, remains an open question. In this exploratory multi-session study, we demonstrate the feasibility of human learning to operate a body-machine interface to control a complex, assistive robotic arm in reaching and Activities of Daily Living tasks. Our results suggest the manner of control space mapping, from interface to robot, to play a critical role in the evolution of human learning.

bioengineering↗

bioRxiv2022

Diversity of Learning to Control Complex Rehabilitation Robots Using High-Dimensional Interfaces

Upper body function is lost when injuries are sustained to the cervical spinal cord. Assistive machines can support the loss in upper body motor function. To regain functionality at the level of performing activities of daily living (e.g., self-feeding), though, assistive machines need to be able to operate in high dimensions. This means there is a need for interfaces with the capability to match high-dimensional operation. The body-machine interface provides this capability and has shown to be a suitable interface even for individuals with limited mobility. This is because it can take advantage of peoples available residual body movements. Previous studies using this interface have only shown that the interface can control low-dimensional assistive machines. In this pilot study, we demonstrate the interface can scale to high-dimensional robots, can be learned to control a 7-dimensional assistive robotic arm, to perform complex reaching and functional tasks, by an uninjured population. We also share results from various analyses that hint at learning, even when performance is extremely low. Decoupling intrinsic correlations between robot control dimensions seem to be a factor in learning--that is, proficiency in activating each control dimension independently may contribute to learning and skill acquisition of high-dimensional robot control. In addition, we show that learning to control the robot and learning to perform complex movement tasks can occur simultaneously.

bioengineering↗