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246 results about "Muscle functions" patented technology

Multiple-freedom degree wearing type rehabilitation training robot for function of hand and control system thereof

The invention discloses a multi-freedom wearable robot for hand function recovery. The robot comprises mechanical arms and mechanical fingers; the mechanical fingers consist of a mechanical thumb, a forefinger, a middle finger, a ring finger and a little finger, wherein the forefinger, the middle finger, the ring finger and the little finger have the same structure as that of the thumb; the mechanical forefinger mainly comprises air muscle, a finger end bracket, a first middle connecting piece, a finger front end bracket and a second middle connecting piece which are connected in turn through a connecting rod; the air muscle drives the second middle connecting piece to move through a rigid string so that the finger of a patient makes lituate and adduction exercises; the inside of each connecting piece is provided with a pressure spring; and inside walls of the two connecting pieces are distributed with rolling beads to reduce friction between the connecting rod and the connecting pieces. The invention also provides a control system and an integrated electricity stimulation system of the robot to assist a patient to rebuild muscle function. The robot provides an assisted exercise mechanism for the fingers, has multiple freedom degrees and dimension adjustable movement mechanism, and can effectively assist the patient to finish repeated training of composite exercise for fingers and complicated finger dividing exercise.
Owner:HUAZHONG UNIV OF SCI & TECH

Upper limb rehabilitation system and method based on myoelectric signal and virtual reality interaction technology

The invention provides an upper limb rehabilitation system and method based on a myoelectric signal and a virtual reality interaction technology. The system comprises a myoelectric signal acquiring and processing part, a virtual reality man-machine interaction part and a muscle function evaluation part, wherein the myoelectric signal acquiring and processing part is composed of a data acquiring module, a signal processing module and a model control module; the virtual reality man-machine interaction part is composed of an upper computer virtual environment module and a force feedback device module; the muscle function evaluation part is composed of a muscular tension quantitative evaluation module and a muscle cooperativeness quantitative evaluation module. According to the rehabilitation method, myoelectric control is used so that a patient subjective intention can be reflected better; a patient keeps initiative in a rehabilitation process by using a virtual reality technology, and the portability, the safety and the effectiveness of rehabilitation trainings are improved. According to the upper limb rehabilitation system and method, an existing clinical rehabilitation evaluation manner can be combined and a muscle function state of the patient is objectively evaluated, so that rehabilitation training standards are provided for the patient and evidences for formulating a therapeutic scheme are provided for rehabilitation doctors.
Owner:YANSHAN UNIV

Self-adaptive recommendation method and system for rehabilitation training prescription based on deep reinforcement learning

The invention provides a self-adaptive recommendation method and system for a rehabilitation training prescription based on deep reinforcement learning. The method comprises the following steps: 1) collecting basic information and medical record information of a patient; 2) acquiring cerebral cortex blood oxygen data of different brain regions of the patient and movement and myoelectricity data ofaffected limbs of the patient; 3) calculating brain function evaluation indexes during exercise and rehabilitation training of the patient by utilizing the brain blood oxygen data, and calculating exercise function evaluation indexes and muscle function evaluation indexes during exercise and rehabilitation training of the patient by utilizing the exercise data and the myoelectricity data so as todynamically evaluate the brain function, the exercise function and the muscle function of the patient; 4) inputting the brain function, motion function and muscle function evaluation indexes obtainedin the step 3) into a pre-established deep reinforcement learning model to train the deep reinforcement learning model and automatically generate a rehabilitation training prescription; and 5) feeding back the rehabilitation training prescription generated in the step 4) to a doctor and the patient for rehabilitation training. By means of the method and system, self-adaptive adjustment of the training prescription can be achieved.
Owner:DANYANG HUICHUANG MEDICAL EQUIP CO LTD
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