A Multi-Functional Lower- and Upper-Limb Stroke Rehabilitation Robot
Chloe O. Nicholson-Smith, Vahid Mehrabi, S. Farokh Atashzar, Rajni V. Patel
- 发表年份
- 2020
- 引用次数
- 47
摘要
It is estimated that about 15 million people a year suffer from stroke worldwide, with 5 million stroke survivors experiencing permanent motor disability requiring therapeutic services. It has been shown that early involvement in rehabilitation therapies has a desirable effect on the long-term recovery of patients. There are, however, several challenges with the current state of delivering rehabilitation services, including limitations on the number of clinics, financial needs, and human resources. Robotic systems have been proposed in the literature to help with these challenges. However, most of the existing robotic systems are expensive, not-portable, and cannot be used for both upper- and lower-limb rehabilitation. This article presents a 3-DOF robotic device that has been designed to deliver both upper- and lower-limb therapy and incorporates a novel mechanical safety mechanism. The device is capable of teleoperation which makes it particularly suitable for telerehabilitation in the current COVID-19 environment. The rehabilitation robot can deliver therapy in assistive and resistive modes to aid patients at all stages of recovery. In the assistive mode, the robot's motion provides input to help the patient in completing the therapy task, while in the resistive mode, the robot opposes the motions generated by the patient thereby requiring additional muscle actuation. The robot has been tested by physiotherapists to assess its validity in a clinical setting, and by healthy participants to assess its functionality, safety, and engineering design. The study found that 80% of the physiotherapists agreed the platform has the potential to improve patient outcomes.
关键词
相关论文
Statistical Learning Theory
Yuhai Wu, Vladimir Vapnik
1999
Artificial intelligence: a modern approach
1995
Applied Nonlinear Control
Jean-Jacques Slotine, Weiping Li
1991
A new optimizer using particle swarm theory
R.C. Eberhart, James Kennedy
2002