Body-Machine Interface Enables People With Cervical Spinal Cord Injury to Control Devices With Available Body Movements: Proof of Concept

Abstract

This study tested the use of a customized body-machine interface (BoMI) for enhancing functional capabilities in persons with cervical spinal cord injury (cSCI). The interface allows people with cSCI to operate external devices by reorganizing their residual movements. This was a proof-of-concept phase 0 interventional nonrandomized clinical trial. Eight cSCI participants wore a custom-made garment with motion sensors placed on the shoulders. Signals derived from the sensors controlled a computer cursor. A standard algorithm extracted the combinations of sensor signals that best captured each participant's capacity for controlling a computer cursor. Participants practiced with the BoMI for 24 sessions over 12 weeks performing 3 tasks: reaching, typing, and game playing. Learning and performance were evaluated by the evolution of movement time, errors, smoothness, and performance metrics specific to each task. Through practice, participants were able to reduce the movement time and the distance from the target at the 1-second mark in the reaching task. They also made straighter and smoother movements while reaching to different targets. All participants became faster in the typing task and more skilled in game playing, as the pong hit rate increased significantly with practice. The results provide proof-of-concept for the customized BoMI as a means for people with absent or severely impaired hand movements to control assistive devices that otherwise would be manually operated.

Keywords: body-machine interface; cervical spinal cord injury; proportional control.

Figures

Fig. 1

Study setup (A). Participants sat in front of a computer monitor wearing four inertial measurement units on the shoulders. Signals from the sensors were mapped into the 2D position of a computer cursor used to perform the different tasks; Sample reaching movement from one participant (B) first session versus (C) last session. The red circles illustrate the center and peripheral targets, and each black line represents the cursor’s path during one center–out reach

Fig. 2

Learning curves for reaching movement measures throughout the 24 sessions of practice; the black dots are individual session averages across all participants and the error bars are the 95% confidence intervals

Fig. 3

Increase in pong hit rate showed by sample pong trajectories from the very first 2-minute pong block in session 3 (top) compared to the very last 2-minute pong block from session 24 (bottom) played by a typical participant; (A) and (C) show the vertical paddle trajectory (thick line), ball vertical trajectory (dotted line) and the hits (red dots) vs. time; (B) and (D) show the paddle paths (blue lines) and the location of hits (red dots), the black rectangle marks the game area

Fig. 4

Increase in typing rate displayed by sample typing trajectories from the very first typing session (top) compared to the very last typing session (bottom) for a typical participant; (A) and (C) show the vertical (black line) and horizontal (grey line) cursor movements and the typed characters (red dots); (B) and (D) show the same information in the first minute