32results about How to "Ensure training efficiency" patented technology

The invention relates to a rehabilitation robot, in particular to an exoskeleton-type upper limb rehabilitation robot, which realizes the upper limb rehabilitation by assisting shoulder blades of the body in rotation. The exoskeleton-type upper limb rehabilitation robot comprises an upper arm part, an upper arm forward bending assisting part, an upper arm external expansion assisting part, a shoulder blade rotation assisting part, a transmission part and a bracket part and is characterized in that the diameter of a forward bending driving wheel of the transmission part is twice that of a driving wheel; the diameter of an external expansion driving wheel is twice that of the driving wheel; and a weight wheel, the forward bending driving wheel and the external expansion driving wheel are all provided with positioning devices. The exoskeleton-type upper limb rehabilitation robot follows a 2-1 principle of driving shoulder blades by the upper limb in human body articular kinesiology, makes the mechanical upper arms assist the shoulder blades in rotation according to the 2-1 principle when assisting the upper arms of the user in bending forward or extending by assisting the shoulder blades of the human body and can more scientifically carry out rehabilitation training of the upper limb of a patient.

The invention relates to the technical field of table tennis training, and specially relates to the ping-pong ball collecting device for ping-pong ball training and the using method thereof. The device comprises a table, an adjusting device, a collecting device and a guiding device; an adjusting device is arranged at the bottom end of the table; the table is fixedly connected with the adjusting device; a collecting device is arranged at the top end of the adjusting device; the collecting device is fixedly connected with the adjusting device; a ball collecting hopper is arranged at the right end of the collecting device; in the present invention, the limiting cloth, the first fan and the second fan are arranged; the arrangement is matched with the fixed connection of the limiting cloth andthe support plate, the elastic force of the second spring to the extrusion plate, the fixed connection of the extrusion plate and the pressure sensor, the traction of the first fan to wind and the traction of the second fan to wind; when the device is used, the emitted table tennis balls can be automatically guided and collected, so that the phenomenon that the labor intensity of operators is increased due to manual collection is avoided, and the phenomenon that the table tennis balls are trampled during collection is also avoided.

The invention discloses a network traffic time series prediction method based on distributed clustering. The clustering-based time series prediction method of this method combines the distributed clustering algorithm and the autoregressive model, obtains the time slice tuples by slicing the time series data, and uses the distributed K-average algorithm (k ‑means) clustering algorithm for distributed clustering, and normal distribution fitting for each cluster in the clustering results to obtain a normal distribution N(μ,σ 2 ), μ is the preliminary predicted value. In the prediction stage, the time series data to be predicted is preprocessed to obtain the time slice prefix tuple to be predicted, and the preliminary prediction value corresponding to the nearest cluster is distributedly calculated, and the preliminary prediction value is calculated in combination with the autoregressive model. correction to obtain a more accurate final forecast value.

The invention discloses a pelvic correction rehabilitation training robot for rehabilitation training. Step 1: Before rehabilitation training, collect the maximum muscle strength value of the patient through a pressure sensor, and collect the maximum activity angle value of the patient's hip joint through a photoelectric encoder; Step 2 : Set the maximum muscle strength value and the maximum activity angle value of the hip joint, within the range smaller than the maximum muscle strength value and the maximum activity angle value of the hip joint, output the motor torque and speed multiple times, and measure the patient's muscle strength and hip joint The angle of activity gives the evaluation results of the patient's pelvic deformation and rehabilitation training data, and generates an evaluation report; Step 3: Set the rehabilitation training mode and parameters according to the evaluation report and generate control instructions, complete the setting of the motor torque and speed, and the motor drives the patient to complete corresponding action. Servo motors are used to drive the flexion and extension of the robot's hip joint, and the robot is used to replace the therapist to assist the patient in the rehabilitation training of pelvic correction.

The invention discloses a brain-computer interface decoding method based on an impulse neural network, comprising: (1) constructing a liquid state machine model based on an impulse neural network, and the liquid state machine model is composed of an input layer, an intermediate layer and an output layer; wherein, The connection weight from the input layer to the intermediate layer is W hx , the weight of the recurrent connection inside the middle layer is W hh , the read weight from the middle layer to the output layer is W yh ; (2) Input the pulse EEG signal, and train each weight using the following strategies: (2-1) Adopt STDP unsupervised training to connect the weight W hx ; (2-2) Set the intermediate layer cyclic connection weight W by means of distance model and random connection hh ; (2‑3) Using Ridge Regression to train the connection weights W with supervised supervision yh , and establish the mapping between the intermediate layer liquid information R(t) and the output motion information Y(t), and finally output the predicted motion trajectory. By using the present invention, the model can be quickly trained in a relatively short time, the movement trajectory of the arm can be predicted in real time, and the efficiency and accuracy can be improved.

The invention discloses a pelvic correction rehabilitation training robot, comprising a support body, a hip joint correction module and a leg support module, the hip joint correction module includes a motor, a reducer and a hip joint correction piece, the motor is installed on the support body, and the motor output shaft It is connected with the input end of the reducer, the output end of the reducer is connected with the hip joint correction part, and the hip joint correction part is connected with the leg support module. Using servo motors to drive the flexion and extension of the robot's hip joints, and using robots to replace therapists to assist patients in pelvic correction rehabilitation training can not only liberate therapists from the heavy training process, but also ensure training efficiency, which is of great significance.

The invention relates to a coarse-grained soil filler gradation recognition method and application system based on a convolutional neural network, constructing a first neural network and several second neural networks; collecting images of fillers on site, and using the first neural network to output the classification of particle size ranges Results; input the filler image and classification results into the second neural network corresponding to the particle size range to obtain the quality of coarse-grained soil in each single particle size range; count the mass distribution of coarse-grained soil in various particle size ranges to obtain gradation and grade matching curve. The present invention obtains the particle size range through the first neural network, and obtains the total mass of a single particle size group through the corresponding second neural network, thereby improving the accuracy of mass calculation, thereby ensuring the accuracy of gradation calculation. Multiple second neural networks are processed in parallel, which ensures the training efficiency of the second neural network and the on-site processing efficiency. High degree of automation, no need for complex image processing algorithms, no manual intervention, no dependence on operator experience, strong environmental adaptability, and high precision.