43 results about "Motor skill" patented technology

A smart busy board is provided utilizing a technology driven timer which can be set to predetermined time intervals for each of a plurality of interactive components disposed on the smart busy board. The timer mechanism and some of the interactive components are connected to electrical elements hidden within the interior housing of the smart busy board. Each of the interactive components engage the infant or toddler with some type of sensory output, such as movement, sound, light, and touch for a predetermined time interval. The busy board is designed to stimulate frontal lobe and cognitive development and improve motor skills.

A programmable display cube device is provided with display screens on two or more rotatable outer faces of the display cube device to enhance a user's cognitive and motor skills. The programmable display cube device is configured to display and dynamically update multimedia content on each display screen according to a sequence of rotational inputs by the user. The multimedia content is prescribed by one or more programs (i.e., educational lesson plans) encompassing STEM, language arts, history, and the like. Educational programs include matching puzzles, memory games, and timed assessments in various subjects. The programmable display cube device may be communicatively connected to a client device (i.e., a mobile device operated by an education professional) to receive new educational programs and generate data-driven insights tracking the user's educational progress.

The utility model belongs to the technical field of rehabilitation training, and particularly relates to a rehabilitation training instrument for the neurology department. The device comprises a horizontally-arranged bottom plate, a seat is installed on the top of the bottom plate, a fixing plate is obliquely installed on the right side of the seat through a supporting rod, a supporting plate is vertically fixed to the right side of the fixing plate, a sliding groove is formed in the fixing plate, a toothed plate is independently installed in the sliding groove in a penetrating mode, and fixing rods are fixed to the front side and the rear side of the upper end of the toothed plate; a fixing block is vertically fixed to the upper end of the supporting plate, freely-rotating turntables are installed on the front side and the rear side of the fixing block, a linkage assembly which drives the turntables to rotate through movement of the toothed plate is arranged in the supporting plate, and an adjusting assembly used for adjusting the training intensity is arranged on the side wall of the fixing block. According to the rehabilitation training device, under the action of the adjusting assembly, the training intensity of a patient is adjusted, in the training process, the patient can drive the rotating disc to rotate through the palm, four limbs of the patient are trained in a linkage mode, the training effect is further improved, and the coordination and sports skills of the patient are enhanced.

The invention discloses a method for predicting a motor skill level based on an electroencephalogram signal deep learning algorithm, and relates to the technical field of motor skill level prediction, and the method specifically comprises the following steps: detecting whether neural activities generated by a subject during a multi-step complex motor skill task have an overlapping phenomenon in time and frequency or not in real time; the following steps are continued under the condition that the phenomenon is detected to exist; the method comprises the following steps: acquiring multi-dimensional motion performance information of a subject when the subject performs a multi-step complex motion skill task, performing feature extraction after acquisition, performing analysis after extraction, and evaluating whether the extracted features can distinguish neural activities of different steps in the multi-step task or not; and when it is evaluated that the extracted features cannot distinguish neural activities of different steps in the multi-step task, constructing a self-adaptive regulation mechanism for optimizing a model structure and parameters. According to the method, the problem of electroencephalogram signal overlapping in a complex task is solved, and accurate motor skill level evaluation and model adaptive optimization are realized.

To provide an information processing device capable of appropriately evaluating the level of motor skill proficiency. [Solution] The control unit 12 (information processing device) according to this disclosure includes: a subject brainwave acquisition unit 121 that acquires subject brainwaves showing the brainwaves of a subject when the subject performs a predetermined movement; a subject motion acquisition unit 122 that acquires subject motions showing the actions of a subject when the subject performs a predetermined movement; and a proficiency evaluation unit 123 that, using a reference motion as the reference motion when performing a predetermined movement, evaluates the proficiency of the subject motion higher the smaller the amplitude of the subject brainwaves when the absolute value of the difference between the amount of movement of the reference motion and the amount of movement of the subject motion is less than a first threshold.

The application discloses a kind of humanoid robot motor skills training methods based on body-object-environment relationship modeling.First, the reference motion data of target motor skills and interactive object, environment state data are obtained;During the training process, the body-object, body-environment, object-environment relationship characteristics and contact state are calculated based on the robot ontology, object and environment state, and the relationship enhanced state representation is constructed;Input it into the adversarial discriminant network to generate the adversarial reward representing the naturalness of action and the consistency of interaction;Finally, the adversarial reward, task, stability and safety reward are fused, and the control strategy is trained based on reinforcement learning.Compared with existing methods, the application explicitly learns the interaction relationship, reduces the complexity of reward engineering, improves the naturalness of movement, the rationality of interaction and the environmental adaptability, and is suitable for whole-body interaction tasks such as carrying, supporting and obstacle crossing.

Estimation of human vision-motor skills using a steer-by-wire system is provided. In one embodiment, a method includes presenting a visual tracking application on a display of a vehicle, the visual tracking application including a moving target user interface (UI) element and a tracking UI element, when the vehicle is restricted to a parked state; detecting movement of the steering wheel by a vehicle operator using a steering-by-wire (SbW) steering system of the vehicle; moving the tracking UI element in a visual tracking application based on the detected movement; and determining a corresponding tracking error between the position of the moving target UI element in the visual tracking application and the position of the tracking UI element in the visual tracking application multiple times. The method also includes processing the corresponding tracking error to estimate a visual-motor skill of the operator.

The invention discloses a humanoid robot controller training method, a humanoid robot controller control method and a humanoid robot controller training device. A two-stage training framework is adopted, and in the first stage, a reinforcement learning algorithm is adopted to independently train a plurality of single-behavior controllers which are specially used for different sports skills (such as walking and standing); then, the capabilities of the single-behavior controllers are integrated into a unified multi-behavior controller through a behavior distillation method, so that the unified multi-behavior controller has the capability of autonomously switching skills according to states. And in the second stage, in order to improve the robustness of the multi-behavior controller in a complex environment, a task environment for simulating various use terrains is constructed, and reinforcement learning fine tuning is performed on the controller in parallel in the environment, so that a controller capable of adapting to the complex terrains and realizing multi-skill smooth switching is finally obtained. The adaptive capacity of the humanoid robot controller and the reliability of practical application can be improved. The robot can be widely applied to the technical field of robots.

The invention discloses an autistic child fusion education social intervention fusion media interaction system and method, and relates to the technical field of medical rehabilitation, and the system comprises a scene design module which constructs a social intervention scene library based on the evaluation indexes of a Wenlang adaptive behavior scale VABS, the scene library comprises virtual scenes corresponding to four fields of communication, daily life skills, socialization and sports skills, and each scene is configured with an interaction task matched with the VABS sub-domain; and the interactive content generation module is integrated with a VR panoramic video acquisition unit and an unreal engine development unit. According to the social intervention fusion media interaction system and method for fusion education of children with autism provided by the invention, a personalized social intervention scene is constructed on the basis of a Wenlang adaptive behavior scale, the accuracy and pertinence of intervention content are ensured, a VR panoramic video and an unreal engine are integrated, a 3D social environment with high immersion and high interactivity is created, and the social intervention effect of children with autism is improved. The participation sense and experience sense of children are enhanced, and the fusion of digitization and traditional teaching is enhanced.

This invention discloses a method for constructing an AI-assisted assessment system for physical education teaching scenarios, belonging to the field of education. The method includes the following steps: First, a standard movement sample database is constructed based on physical education teaching standards, containing movement samples and feature annotations from multiple angles and difficulty levels; second, an AI assessment model is designed, using computer vision technology to extract key points of human posture, and combining temporal analysis algorithms to achieve automatic recognition and quantitative scoring of movements; then, a recording module, a feedback module, and a report generation module are integrated to provide video acquisition, real-time analysis, and personalized guidance functions; finally, the system is continuously improved through an iterative optimization path of data acquisition, model training, system integration, and experimental verification. This invention realizes the transformation of physical education teaching assessment from a subjective experience-driven model to a data-driven model, supports multi-dimensional movement scoring and deviation localization, provides objective and unified evaluation results, and offers immediate and accurate feedback, effectively improving teaching guidance efficiency and students' movement skill levels.

The application discloses a motion skill action posture intelligent evaluation method and system, adopts a multi-view camera array to synchronously collect a motion process video, extracts a human body skeleton two-dimensional key point through an improved algorithm, combines multi-view geometric constraints and Kalman filtering to reconstruct a three-dimensional human body posture sequence, constructs a standard action posture template library, realizes posture alignment of an evaluation object and a standard template through body proportion normalization processing, completes action stage division and phased posture evaluation through a dynamic time warping algorithm, calculates posture deviation and generates a graded deviation diagnosis report, and finally realizes superimposed visual feedback of evaluation postures and standard postures through augmented reality technology. The application can realize high-precision, quantifiable and phased intelligent evaluation of motion postures, eliminate the interference of individual morphological differences on evaluation results, accurately locate action deviation nodes, and provide intuitive interactive correction guidance.

1. Name of the product in this design: Rotating Chair (Hippopotamus). 2. Purpose of this design: A toy for developing children's motor skills, combining the functions of a toy and a sports equipment. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: three-dimensional picture.

The application discloses a basic motor skill quantitative test method and an intelligent evaluation device for child development evaluation, and relates to the technical field of child development evaluation. The specific steps of the method are as follows: first, collecting multi-source data of child movement through a device and standardizing and integrating the data; then, dividing sequence stages and constructing a standard action feature library; then, dynamically matching to determine specific stages and collecting exclusive parameters; then, performing multi-modal fusion evaluation, generating a report and early warning information; finally, generating a personalized intervention scheme according to the evaluation and early warning, and optimizing the model according to the effect to form a closed loop system. The application breaks the limitation of single data evaluation, collects and integrates data in multiple dimensions, divides sequence stages, constructs a standard feature library, and makes up for the deficiency of traditional evaluation. In addition, the application accurately judges and early warns risks through collaborative matching and fusion evaluation, generates differentiated intervention content, records data optimization models, forms a closed loop, solves the disconnection problem of traditional evaluation and intervention, and helps to improve child development.

A device that trains a person's motor skills, including the adaptation to instability, strength and mobility, includes a dome having preset characteristics of elastic deformability and a wall of preset thickness. One or more separating walls are arranged in an inner recess of the dome and form ribs that provide a support to the dome at its base, defining one or more compartments within the recess.

1. The name of the design product: toy (stone set for crossing the river and sensory board set). 2. The use of the design product: the design product is used for toys for exercising children's motor skills, which has the function of toys and the function of sports equipment. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: reference drawing in use. 5. Other circumstances that need to be explained: set 1 is a stone for crossing the river, and set 2 is a sensory board.

The present invention is an electric motor capable of contracting itself to transmit its mechanical force in the form of traction to another attached object. This type of motor behaves similarly to a muscle and can be manufactured to its exact size and shape to replace it, thus enabling mechanical limb prostheses equipped with it to restore to the user almost all the motor skills of their lost limb, unlike other types of electric motors which only restore a portion of them. This motor consists of an assembly of contraction units, each comprising a solenoid attached to the outer fibrous sheath by central fibers, and a permanent magnet facing it, also attached in the same way.When an electric current passes through the solenoid, it becomes magnetic. The two attract each other, and their attractive force is transmitted via the fibers to the coating, causing it to contract. This force can then be transmitted to another object attached to the coating. A special damping system is also present. Everything relies on a clever interplay of mechanical tension coupled with a driving force source derived from the properties of electromagnetism to successfully reproduce the subtleties of muscle movement. This device is useful in improving the capabilities of active limb prostheses, in robotics for enhancing the capabilities of android robots, and is also likely to find applications in other fields.

The present invention relates to an electroencephalogram (EEG) adaptive classification method and application based on a dynamic joint domain adaptation network. The method first collects and preprocesses EEG signals before transmitting them to a host computer. After receiving the preprocessed EEG signals, the host computer extracts and classifies the signals based on a dynamic joint domain adaptation network (DJDAN) model, which includes a feature extractor, a classifier, a global domain discriminator, and a local domain discriminator. The host computer converts the movement intention labels output by the classifier into control signals required by rehabilitation equipment. The rehabilitation equipment generates feedback signals based on the patient's training movements and transmits them to the host computer, which records and evaluates the patient's training progress. The present invention can realize movement intention recognition based on EEG signals and is applied to intelligent rehabilitation training systems for stroke patients, improving rehabilitation effects and the patient's motor skills.

The system and method combines virtual reality (VR), eye-tracking (ET), and motion sensors on extremities such as hands and feet to assess cognitive and motor skills in both healthy and unhealthy individuals using well-defined exercises. Applying VR and ET to motion sensors in cognitive exercises can improve the effectiveness of interventions and the ability to quantify cognitive and motor skills, thereby enhancing the effectiveness of training for individuals.

The application discloses a kind of based on multi-motion skill prior art human-machine shared autonomous teleoperation method and system, comprising the following steps: based on the method of dynamic motion primitive, by teaching learning to different task trajectory, construct motion skill library;Optimal classification in running skill library is obtained in real-time trajectory;Human and robot share control's admittance control model is constructed;Based on the confidence of human and robot, share control weight is obtained, and share control rate arbitration is carried out.The planning control of person and robot is unified under admittance control framework, and arbitration function is designed through the confidence of two, share control weight is distributed, and robot autonomous level is adjusted online, to realize shared control teleoperation;Compared with the off-line trajectory generation method of robot dominance, the flexibility of human intervention operation is increased in the present method, can realize obstacle avoidance, trajectory trimming and other functions, improve the reproduction accuracy, reduce human power consumption and task completion time.

The present disclosure provides a manual dexterity training device that addresses cognitive and motor skill decline through progressive therapeutic exercises. The device comprises an object configured to be manually moved, and a container with an open face defining side-A and side-B. A central vertical divider separates the sides and includes a protruding section with holes extending through face-A and face-B. Cover-A and cover-B are hingedly coupled to move between horizontal and vertical positions, each including holes for object passage. The device enables objects to move from side-A to side-B through aligned holes, providing graduated difficulty levels that help maintain independence and cognitive function in aging populations and individuals with neurological conditions.

This invention provides a method for generating multi-contact point motion in humanoid robots, comprising: describing the motion state of the end effector of a multi-contact point legged robot; constructing a mathematical description of the motion mode of the robot's end effector; converting the mathematical description into a reward function for reinforcement learning to train motor skills; and finally transferring the method to a physical robot. The advantages of this method are that it provides a comprehensive and accurate mathematical description of the end effector motion of a multi-contact point robot, constructs a continuous reward function to improve the continuity of the parameter space, and effectively guides the algorithm to converge quickly to the desired state, making it particularly suitable for generating motor skills based on reinforcement learning.

This application relates to an educational toy, specifically in the field of children's toy technology. The toy includes a fixed frame, a sliding assembly, a ball bearing, a control component, and a receiving component. At least two sliding assemblies are rotatably mounted on the fixed frame, and these assemblies are arranged alternately from top to bottom. The ball bearing is disposed within the sliding assembly. The control component is mounted on the sliding assembly. The receiving component is located below the sliding assembly. This application is simple to install, enhances children's motor skills, and improves sensory coordination. The receiving component allows the ball bearing to be retrieved, preventing it from falling directly from the bottom sliding assembly onto a table or floor. This facilitates ball bearing retrieval, avoids damage to the ball bearing, and prevents difficulties in retrieving the ball bearing due to it falling anywhere, thus solving the problem of lost or misplaced balls in existing educational toys.

The invention relates to an athletic skill recovery interface device (1) comprising: one or more cortical sensors (3; 31, 32,..., 3n), each cortical sensor being capable of detecting a respective cortical signal (SE1, SE2,..., SEn) from a respective portion of the cerebral cortex (C) of the patient (P); a decoding unit (2) associated with the one or more cortical sensors (3; 31, 32,..., 3n) arranged to be operatively connected to the one or more cortical sensors (3; -each of said cortical signals (SE1, SE2,..., SEn) transmitted by each of said cortical signals (SE1, SE2,..., SEn) transmitted by each of said cortical signals (SE1, SE2,..., SEn); one or more muscle electrical stimulators (4) each adapted to a muscle (M) to be stimulated; wherein the decoding unit (2) is configured to process, based on cortical signals received by the one or more cortical sensors (31, 32,..., 3n), the one or more muscle electrical stimulators (4; 41, 42,..., 4n), and one or more actuation signals (SM1, SM2,..., SMn) of the one or more signals (SM1, SM2,..., SMn) of the one or more signals (SM1, SM2,..., SMn). The invention also relates to a method for operating the interface device (1).

1. Name of the designed product: toy (sensory training product - foot pedal rotating disc). 2. Use of the designed product: mainly as a toy for exercising children's motor skills, sports equipment and "sensory training" products. 3. Design points of the designed product: in shape. 4. Picture or photo best indicating the design points: perspective view.

A system and method which integrate cognitive science, kinesiology, auditory neuroscience, developmental psychology, and occupational therapy to build a program of progressively complex rhythmic-coordinative cognitive-motor movements designed to engage the foundational motor and cognitive skills that precede learning. Multiple levels of progressively complex motor movements that introduce motor skills are presented in a specific developmental order. The levels progress through systematic cognitive and motor patterns with increasing complexity. Each level progresses to higher complexity that requires mastery of the previous levels. The complexity grows through increasing the cognitive processing requirements, increasing the sensory-motor processing demands and increasing the complexity of the movements.

This invention relates to the fields of virtual reality, digital twins, and artificial intelligence, and provides a method for optimizing fall prevention VR training strategies using digital twin-driven two-layer reinforcement learning. To address the safety concerns in existing fall prevention VR training, this method constructs a pre-trained digital twin environment and a user behavior simulation model, combining real-time multimodal data to generate user state vectors. A two-layer reinforcement learning architecture is then employed, where a strategy optimization generator produces a VR training strategy parameter set based on simulated performance characteristics, and safety verification and optimization are performed through simulation prediction. The optimized VR training strategy parameter set is deployed to a VR training terminal for training, using training feedback data to update the corresponding model, forming a dynamic adaptive optimization closed loop. By achieving personalized strategy generation and pre-verification of safety, this method significantly improves the safety and dynamic adaptability of fall prevention VR training, and can be applied to scenarios such as fall prevention for the elderly and motor skills training.

A system adapted to augment movement behavior of participants in an open motor task or activity includes one or more movement sensors configured to generate output characterizing movements of participants, including relevant interactions with elements and features of the environment and task or activity objects within the environment. A processor is configured to extract and segment a sequence of movement behavior elements from the output, register the movement elements with respect to operating environment, including the task or activity objects, recognize activity state, and determine cues to enhance performance and / or learning. Augmentations include verbal, visual, or haptic or audible signal-based cues that are designed to target critical aspects of movement skills in open motor tasks, including planning sequence of movements toward task goals; coordinating and executing movement elements in the sequence in relationship to relevant task activity events and elements.

To effectively improve the user's athletic ability. [Solution] An information processing system is provided that includes a request unit that requests the user to perform a body movement that causes an object or part of the user's body that moves in conjunction with the user's movements to overlap with a target object, and an evaluation unit that calculates a disorientation evaluation value indicating the degree of swaying of the user's body during the body movement. By having the user use such an information processing system, it becomes possible to effectively improve the user's motor skills.

A playful and therapeutic device for the rehabilitation of fine manual motor skills; specifically for the rehabilitation of patients with rheumatic, traumatic and neurological pathologies who have fine motor sequelae in the wrist, palm and fingers of both hands; characterized by having a cultural theme related to the Guelaguetza of Oaxaca and by comprising a container with a lid and handle that will serve to store and transport other components such as a puzzle, cylindrical pieces with and without slots, cards and tweezers.The device facilitates playful and therapeutic activities for mental, sensory, and physical stimulation; such as placing and removing the lid of the container to perform pronation and supination movements and palmar grasp; assembling a puzzle to perform pronation and supination movements, palmar grasp, four-finger pinch, and visuo-manual coordination; fitting cylinders into holes to perform two-finger pinch, three-finger pinch, and visuo-manual coordination movements; inserting cards into slots to perform pronation and supination movements, two-finger pinch, and visuo-manual coordination; and hooking tweezers onto a surface to perform pronation and supination movements, two-finger pinch, and visuo-manual coordination.

This invention discloses a bio-inspired learning system based on human-machine collaborative training and its usage method. The bio-inspired learning system includes a robot learning unit and a bio-inspired teaching unit. Embodiments of this invention enable the robot to acquire human motion data in real time, analyze and extract key features through deep learning algorithms, thereby quickly adapting to different movement patterns. During machine learning, dynamic interaction between humans and machines is achieved. The robot can not only observe human actions but also adjust its own movement strategies based on feedback, achieving more natural and fluid collaboration. Through human-machine collaborative learning, the robot can perform more accurate imitation and learning, significantly improving its motor skill learning efficiency and reducing sample dependence and sample quality problems in traditional learning methods.