Stanford University Hospital Publishes Paper Demonstrating Home Rehabilitation Effectiveness Using Neofect Smart Glove

[Asia Economy Reporter Hyunseok Yoo] Neofect announced on the 6th that a paper proving the effectiveness of home rehabilitation using the "Smart Glove" was published in the American Journal of Physical Medicine and Rehabilitation (PM&R).

The research team led by Dr. Maarten Lansberg at the Stanford Stroke Center, Stanford University Hospital, conducted a study on the effects and patient satisfaction of stroke patients performing home rehabilitation independently using Neofect's hand rehabilitation device, the Smart Glove.

This clinical trial involved 20 chronic stroke patients with upper limb functional impairments, who used the Smart Glove for 50 minutes a day, 5 days a week, over 8 weeks. Tools such as the Fugl-Meyer Assessment and the Jebsen-Taylor Hand Function Test were used to evaluate the recovery of motor and balance functions in stroke patients.

According to the paper published by Stanford University Hospital, 85% of the patients participating in the study were satisfied with the rehabilitation training using the Smart Glove, and 80% showed improvement in hand function.

The Stanford Stroke Center concluded through this study that most patients were satisfied with home rehabilitation using the Smart Glove, and that hand and arm functions could be improved through autonomous rehabilitation training performed at home without medical staff assistance. The paper is available on the PM&R website.

Ban Hoyoung, CEO of Neofect, said, "We are pleased to prove that many patients can perform more effective rehabilitation training, and it is especially meaningful that the results came from a chronic patient group," adding, "Based on the research results published by Stanford University Hospital, we will accelerate marketing in the U.S. market and also lay the foundation for future insurance registration possibilities."

The Neofect Smart Glove is a rehabilitation training device that allows stroke patients to perform rehabilitation exercises for finger, wrist, and forearm functions through various rehabilitation training games. It was developed as personalized software to enable patients to perform rehabilitation training independently at home. Using artificial intelligence algorithms, it provides various game training contents according to the patient's condition and adjusts the difficulty level in real time.