Wan Bin of Ruihan Medical: "Brain-computer interface + AI + robot" is the future of rehabilitation track

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In recent years, the development of brain-computer interface and robotics technology has entered the fast lane. The technology and market of the two are independent but complementary. In the field of rehabilitation, rehabilitation robots with brain-computer interfaces have become a new force in the rehabilitation track.


On November 22, 2024, at the "Third Nandujiang Smart Medical and Rehabilitation Industry Summit Forum", Wan Bin, general manager of Shenzhen Ruihan Medical Technology Co., Ltd., discussed the current status and future development of brain-computer interface technology and rehabilitation robots under the theme of "Integration and Prospects of Brain-Computer Interface and Rehabilitation Robots".


Wan Bin used "Avatar" as an analogy to define brain-computer interface. In the movie, the protagonist Jack's task is to control the Avatar through the brain-computer interface and enter the Na'vi tribe. Although everyone likes to use the image of "Avatar" to materialize brain-computer interface technology, in detail, brain-computer interface is much more complicated than in the movie.


In terms of brain-computer interface platform technology, Ruihan Medical mainly provides non-invasive brain-computer interface technology. However, it should be emphasized that as a subsidiary of Lepu Medical, its parent company Lepu Medical is also constantly promoting more brain-computer interface technologies.



Due to the increasingly severe aging of the population, the number of stroke patients in China has exceeded 12 million, and 70% to 80% of patients have varying degrees of motor and cognitive impairment, as well as a lack of rehabilitation medical resources. Rehabilitation robots have taken advantage of this trend, and the market size of rehabilitation medical devices has increased year by year, with a market turnover growth rate of 57.5%.


Ruihan Medical has made a breakthrough in combining brain-computer interfaces and rehabilitation robots and has independently developed many products.


For example, BCI+finger joint robot can complete BCI closed-loop finger training, which can cover the full cycle of hand function rehabilitation training from acute stage, subacute stage to chronic stage of stroke.



For example, the BCI+ upper and lower limb active and passive robot can execute brain-computer commands and complete BCI closed-loop upper and lower limb training. It has five training modes: passive, assisted, active and passive, active, and isokinetic, which can meet the rehabilitation needs of patients with different muscle strength levels.



In the future, Ruihan Medical will develop a "brain-computer interface rehabilitation robot system based on multimodal induction and feedback coupling strategy", which will efficiently integrate brain-computer interface (BCI) + virtual reality (VR) + rehabilitation robot, and use non-invasive and highly safe new technologies to allow the brain to interact directly with external devices to achieve active and passive collaborative rehabilitation training. While helping patients improve limb dysfunction, it directly trains damaged brain neural networks and promotes the reconstruction and functional reorganization of brain motor neurons. The products of this project are mainly used for patients with hand dysfunction after stroke, as well as other patients with hand dysfunction caused by the central nervous system, such as craniocerebral trauma, high spinal cord injury, etc., effectively filling the gap in domestic rehabilitation robot technology, helping the development of the rehabilitation and elderly care industry, and cultivating more high-level talents for the rehabilitation medical industry.


In addition, Ruihan Medical will also introduce AI deep learning to optimize brain-computer interfaces and form a product layout of "brain-computer interface + AI + robots".


Reference address:Wan Bin of Ruihan Medical: "Brain-computer interface + AI + robot" is the future of rehabilitation track

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