LAUSR

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.