Hands-free control of an assistive robotic arm for high-level paralysis

 Can your competent? doctor figure out how to repurpose this for upper limb hemiparesis?

Do you prefer your doctor, hospital and board of director's incompetence NOT KNOWING? OR NOT DOING? Your choice; let them be incompetent or demand action!

My doctor knew nothing and did nothing to get my left arm recovered; starting with complete failure to cure spasticity! 20 years later, left arm/hand are still completely worthless.

Hands-free control of an assistive robotic arm for high-level paralysis

• Brady A. Hasse,
• Mario A. Ibarra,
• Jehad A. Alfaleh,
• Derek Gin,
• Drew E. G. Sheets,
• Aaliyah M. P. A. Thompson-Mazzeo &
• Andrew J. Fuglevand 

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Abstract

Background

Recent advancements in assistive robotic arms have enabled many people with tetraplegia to perform activities of daily living more independently. Because these systems typically require hand use, they are not a ready option for many individuals with high-level (C4 and above) tetraplegia. Such individuals, however, might be able to use signals that arise from the head and neck to control assistive devices. Therefore, the goal of the study was to evaluate the utility of several signals arising from the head and neck to control a robotic arm during 3D center-out reaching to multiple targets ~ 25–50 cm from the start location.

Methods

Ten non-disabled human subjects were tested using five non-invasive, hands-free modalities (head position, head velocity, facial electromyography, tongue, and voice) to control a robot arm. For comparison, subjects also used joystick position and joystick velocity methods to control reaching movements of the robotic arm. A one-way repeated measures ANOVA was carried out on key performance indicators including movement time, path efficiency, throughput, and perceived workload.

Results

The hands-free control modalities of head position, facial EMG, tongue, and voice had average (± SD) movement times (5.8 ± 1.6, 8.2 ± 3.7, 6.3 ± 2.0, and 10.0 ± 3.7 s, respectively). With the exception of voice, none of these times were significantly different than that of the benchmark hand position control of a joystick (6.3 ± 2.3 s). Furthermore, no significant differences were revealed in perceived workload across control modalities.

Conclusions

These results indicate, therefore, that various non-invasive, hands-free methods could be used effectively by people with high-level tetraplegia to operate assistive robotic arms.