A Residual Movement Classification Based User Interface for Control of Assistive Devices by Persons With Complete Tetraplegia

Abstract

Complete tetraplegia can deprive a person of hand function. Assistive technologies may improve autonomy but needs for ergonomic interfaces for the user to pilot these devices still persist. Despite the paralysis of their arms, people with tetraplegia may retain residual shoulder movements. In this work we explored these movements as a mean to control assistive devices. Methods: We captured shoulder movement with a single inertial sensor and, by training a support vector machine based classifier, we decode such information into user intent. Results: The setup and training process take only a few minutes and so the classifiers can be user specific. We tested the algorithm with 10 able body and 2 spinal cord injury participants. The average classification accuracy was 80% and 84%, respectively. Conclusion: The proposed algorithm is easy to set up, its operation is fully automated, and achieved results are on par with state-of-the-art systems. Significance: Assistive devices for persons without hand function present limitations in their user interfaces. Our work present a novel method to overcome some of these limitations by classifying user movement and decoding it into user intent, all with simple setup and training and no need for manual tuning. We demonstrate its feasibility with experiments with end users, including persons with complete tetraplegia without hand function.

Activating effective functional hand movements in individuals with complete tetraplegia through neural stimulation

“…Patient control. 3 command modalities have been proposed to participants to control the triggering of hand opening and 2 different grasping: (1) they could perform different movements with their contralateral shoulder that were captured with inertial sensors (IMU) 44 ; (2) they could use two different muscles voluntary contractions, again from the contralateral side, that were captured by electromyography (EMG) sensors; or (3) they could push buttons attached to the wheelchair headrest with head movements. P1 chose to use small voluntary contractions of the supralesional platysma and upper trapezius muscles (of the contralateral side of the stimulated hand) detected by surface EMG (Trigno™ Delsys, Natick, MA).…”

“…Combined approaches with functional surgery may be also a solution, in particular for elbow flexion recovery 8 . Further researches are necessary to keep the solution simple with hidden complexity of the technology and richer interfaces 44,45 .…”

On the Decoding of Shoulder Joint Intent of Motion From Transient EMG: Feature Evaluation and Classification

“…Eventually, in this study we considered only EMG signal for intent detection of the shoulder but the use of load cell and inertial data, in conjunction with myoelectric signal, has been yet reported [12], [37], [38], [39] and thus fusing information coming from other sensors can represent a viable solution for improving the overall performances of the proposed architecture [40].…”