10 results about "Repetitive movements" patented technology

The application discloses a sports movement counting and rule-breaking action judging method based on human body posture recognition, and comprises the following steps: step A, human body recognition; step B, personnel screening; step C, repeated action detection based on human body key point detection technology; and step D, rule-breaking action judgment. The application uses human body key point detection technology of deep learning, calculates the relative position and relative angle of each limb part according to the position information, thereby judging what action the test personnel is doing, whether the action is in place, and whether there is a rule-breaking situation. Finally, in combination with time domain information, the preceding and subsequent multiple frames of actions are associated, and whether the human body accurately completes each standard action is judged. When each action is sequentially and normally completed, the system automatically counts. The application automatically adapts to different human body features, and compared with infrared radio frequency technology, the counting accuracy is high.

Systems, devices, and methods described herein may involve receiving movement data associated with a subject, the movement data collected during repetitive movement of the subject; generating a phase portrait based on the movement data; calculating a phase portrait metric of a characteristic of the phase portrait; and assigning the subject to a movement phenotype based on the phase portrait metric.

This application discloses a U-Q-Learning-based mobile robot path planning method in unknown environments, comprising: designing a composite reward function including a repetitive movement penalty, a distance reward, and a state transition heuristic reward; initializing a Q-table, an action selection count table, and a state visit count table, as well as an exploration rate, a learning rate, and a discount factor; calculating the upper confidence bound of actions based on the Q-value of the state, the number of action selections, and the number of state visits, and dynamically adjusting the exploration rate; if the current exploration rate is large, randomly selecting an action from the action space; otherwise, selecting the action with the largest upper confidence bound; the robot executing the selected action, transitioning to the next state, and calculating the immediate reward; adaptively adjusting the learning rate, updating the current Q-value by combining the immediate reward, the discount factor, and the maximum Q-value of the next state, updating the state to the next state, repeating multiple iterations until the algorithm converges, and outputting the optimal path based on the optimized final Q-table. This application can improve the efficiency and stability of path planning.

Disclosed herein is an apparatus and method for counting repetitive movements based on Artificial Intelligence. The method may include generating standard movement information that includes a key pose extracted from a demonstration movement image stream based on human skeleton information, which is a set of pieces of positional information of human joints, and major joint information in the key pose and counting repetitive movements depending on whether a user movement matches the standard movement information based on human skeleton information of a user movement image stream.

Methods, apparatuses, and systems for wireless monitoring systems, wireless vital monitoring, and wireless proximity sensing are described.SOLUTION: A system includes a transmitter configured to transmit a first wireless signal over a wireless channel of a venue, a receiver configured to receive a second wireless signal over the wireless channel, and a processor. The second wireless signal comprises a reflection of the first wireless signal by at least one living being having at least one repetitive motion at the location. The processor is configured to obtain a time series (TSCI) of channel information of the wireless channel based on the second wireless signal, to generate, for each living being of the at least one living being, a vital signal representative of all repetitive movements of the living being based on the TSCI, to extract a heart rate signal from the vital signal of each living being, and to monitor heart rate variability based on the heart rate signal for each living being at the location.SELECTED DRAWING: Figure 1A

When comparing and analyzing multiple recordings of repetitive movements, there is no means to dynamically switch the reference point for synchronized playback. Therefore, it was difficult to recognize the discrepancies between movements that appeared different depending on the selection of the reference point, and to identify their causes. [Solution] The system accepts multiple time-series media data, each recording multiple repetitive actions, with multiple reference point candidates assigned to each recording. It displays these data in parallel or superimposed, and presents the reference point candidates on a timeline. For each type of reference point candidate, the system calculates the median time position across multiple recordings as the representative position, and sets intervals on the timeline using the midpoint between adjacent representative positions as boundaries. The system designates the reference point candidate corresponding to the interval to which the user's input location belongs as the reference point, and realigns all recordings to the time position of that reference point to update synchronized playback.

The invention relates to method for calibrating a head-mounted device (1), the head-mounted device (1) comprising a frame (2) and at least one sensor (4) mounted on the frame (2), for measuring position and / or motion, the method being implemented while the head-mounted device (1) is worn on a wearer's head, the method comprising steps of: —detection (DET) of a known repetitive movement pattern of the wearer's head by the sensor (4), —acquisition (ACQ) of three-dimensional motion data by the sensor (4), —selection (SEL) of data relative to the movement pattern from the motion data, —numerical treatment (NUM) of the selected data to obtain a reference base relative to the wearer's head, and —calibrating (CAL) the sensor (4) based on the obtained reference base.

Active wrist brace for guiding and relieving musculotendinous pain at the elbow (epicondylar and epitrochlear) and wrist. The invention relates to a new type of wrist brace that relieves pressure on the forearm muscles and their tendon insertions at the elbow and wrist. Due to repetitive movements, the tendons of the elbow and wrist become inflamed, resulting in tendinitis. The wrist is responsible for this elbow condition. This brace will preferably consist of a flexible sleeve (1) covering the wrist and perforated for the thumb (2). One or more dorsal tunnels (3) (3') will be arranged on this flexible sleeve (1). One or more tunnels (3) (3') will be located on the palmar side of the sleeve (1). One or more flexible elastic strips (5) will be placed in these palmar and dorsal tunnels (3) or tunnels (3').

A method and system for augmented neurorehabilitation (ANR) for patients are disclosed. The ANR system generates rhythmic auditory stimuli (RAS) and visual augmented reality (AR) scenes, which are synchronized according to a common beat speed and output to the patient during a treatment session. Sensors worn by the patient capture biomechanical data related to repetitive movements performed by the patient in sync with the AR visual content and RAS. Critical thinking algorithms analyze the sensor data to determine the spatial and temporal relationships of the patient's movements relative to the visual and audio elements, and to determine the patient's entrainment level and progress toward clinical / therapeutic goals. Furthermore, a 3DAR modeling module configures the processor to dynamically adjust the augmented reality visual and audio content output to the patient based on the determined entrainment level and whether training goals have been achieved.

This invention belongs to the field of pet toy control technology and discloses a control method and system for pet toys. The method includes: acquiring pet location information; determining the escape direction of the pet toy based on the pet location information and the location coordinates of the pet toy; predicting the pet's movement trend based on the pet's current center coordinates and the center coordinates of the previous moment; obtaining a predictive offset direction based on the environmental distance information of the pet toy in a preset direction; superimposing the escape direction and the predictive offset direction to obtain the escape direction vector of the pet toy; determining the distance weight of each direction based on the distance to obstacles; weighting and summing the unit direction vectors of each direction based on the distance weight of each direction to obtain the obstacle avoidance direction vector; and fusing the escape direction weight, obstacle avoidance weight, escape direction vector, and obstacle avoidance direction vector to output control commands, thereby avoiding repetitive movement patterns being perceived by the pet and improving the pet's playtime persistence and fun.