Wouldn't work on me, that control is now dead brain. So cherry picking higher functioning survivors to make the research look good.
New Artificial Intelligence-Integrated Electromyography-Driven Robot Hand for Upper Extremity Rehabilitation of Patients With Stroke: A Randomized, Controlled Trial
Abstract
Background
An
artificial intelligence (AI)-integrated electromyography (EMG)-driven
robot hand was devised for upper extremity (UE) rehabilitation. This
robot detects patients’ intentions to perform finger extension and
flexion based on the EMG activities of 3 forearm muscles.
Objective
This study aimed to assess the effect of this robot in patients with chronic stroke.
Methods
This
was a single-blinded, randomized, controlled trial with a 4-week
follow-up period. Twenty patients were assigned to the active (n = 11)
and control (n = 9) groups. Patients in the active group received 40
minutes of active finger training with this robot twice a week for 4
weeks. Patients in the control group received passive finger training
with the same robot. The Fugl-Meyer assessment of UE motor function
(FMA), motor activity log-14 amount of use score (MAL-14 AOU), modified
Ashworth scale (MAS), H reflex, and reciprocal inhibition were assessed before, post, and post-4 weeks (post-4w) of intervention.
Results
FMA was significantly improved at both post (P = .011) and post-4w (P
= .021) in the active group. The control group did not show significant
improvement in FMA at the post. MAL-14 AOU was improved at the post in
the active group (P = .03). In the active group, there were significant improvements in wrist MAS at post (P = .024) and post-4w (P = .026).
Conclusions
The
AI-integrated EMG-driven robot improved UE motor function and
spasticity, which persisted for 4 weeks. This robot hand might be useful
for UE rehabilitation of patients with stroke.
Clinical Trial Registry Name: The effect of robotic rehabilitation using XMM-HR2 for the paretic upper extremity among hemiparetic patients with stroke.
Clinical Trial Registration-URL: https://jrct.niph.go.jp/
Unique Identifier: jRCTs032200045.
No comments:
Post a Comment