24 results about "Motor behaviour" patented technology

The application discloses a kind of based on intelligent wearing detection movement data carries out health analysis method, it is related to wearing detection movement data processing technical field, including the following steps: according to reference evolution sequence, the response trajectory of each feature relative to the change of movement behavior is matched and calculated, response offset identification sequence is generated, to judge whether the response of different features in wearing detection movement data to health analysis result exists hysteresis difference;In the case where there is hysteresis difference, according to the continuous change of each feature response offset of response offset identification sequence, generate the hysteresis description parameter group containing hysteresis amplitude parameter and hysteresis change gradient parameter, to determine the response hysteresis degree.The application solves the problem that the fusion weight distribution is unbalanced due to the hysteresis difference of multiple features, realizes the dynamic weight adjustment based on hysteresis degree, so as to improve the accuracy and stability of health analysis result.

The present application discloses a method for controlling headphones, headphones, and a computer-readable storage medium. The headphones include a sensor module, and the method includes: identifying motion behaviors of a user wearing the headphones within a preset time window, each motion behavior corresponding to a preset motion parameter calculation algorithm; obtaining, based on the motion parameter calculation algorithm, motion parameters corresponding to the motion behaviors from detection data collected by the sensor module while the user is performing the motion; and outputting a fatigue level of the user wearing the headphones within the time window based on the motion parameters corresponding to all motion behaviors within the time window and a preset fatigue detection model.

The application discloses a kind of simulation non-stable base's kinematics test system, applied to animal kinematics test technical field.The system includes console, non-stable base simulation part, raw force data acquisition part, motion behavior data acquisition part;Console includes host computer, synchronous trigger and other control equipment;Non-stable base simulation part includes programmable control's rotating motor, equivalent base plane and rigid connection fixture;Raw force data acquisition part includes six-dimensional force sensor, three-axis acceleration sensor and related acquisition equipment;Motion behavior data acquisition part includes high-speed camera, motion capture camera, and can complete the behavior data and kinematics data record of research object.The application can realize the equivalent simulation of various types, various states of leaf class non-stable base in nature, and can complete the synchronous acquisition of force data and motion behavior data in the process of kinematics experiment of animal on non-stable base.

This invention discloses an artificial intelligence-based dynamic monitoring method and system for motion behavior, belonging to the field of dynamic monitoring technology. After user authorization, it acquires the user's acceleration, angular velocity, and pressure data, and synchronizes them with timestamps. A timestamp set is constructed, and the position and angular posture of a single user at a single timestamp are calculated. The motion state index of a single user at a single timestamp is calculated, and combined with pressure data, the motion-force index value of a single user at a single timestamp is calculated. Based on the motion-force index value, a motion-force threshold Boolean matrix is ​​constructed, and the similarity between different users is calculated. The number of times a single user exhibits abnormal motion behavior across all timestamps is calculated, a preset threshold is established, and the analysis and output of warnings are performed. This invention achieves high-precision dynamic monitoring and anomaly detection of motion behavior, enabling timely identification of potential motion risks, improving motion safety, and optimizing motion behavior management.

An apparatus comprising: at least one processor; and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to: model a response of a motor to a drive signal using one or more filters, the one or more filters configured to define a range of expected motor behaviour associated with the drive signal; compare the modelled response with an actual response of the motor to the drive signal; and identify a motor error if the actual response of the motor is outside the range of expected motor behaviour defined by the modelled response.

The present invention generally relates to methods and systems for predicting one or more a kinetic biomarkers associated with one or more neurodevelopmental disorder, such as autism spectrum disorder, by monitoring and analysing various movement behaviours. Specifically, the present invention can be applied to monitor and record data on atypical movement behaviours, such as tiptoe behaviour and axis-spinning, when observed in subjects including children and adults. An aspect of the invention relates to a computer-implemented method for predicting a kinetic biomarker associated with a neurodevelopmental disorder; the method comprising: - receiving a first data flow indicative of a movement and orientation of the lower leg of the subject; - receiving a second data flow indicative of a movement and orientation of the foot of the subject; - determining using at least one machine learning model that the first data flow and second data flow correspond to an atypical movement event; - determining, based on a frequency of occurrence of the atypical movement event during a predetermined time window, that the first data flow and the second data flow correspond to at least one kinetic biomarker associated with the neurodevelopmental disorder; and, - generating a report comprising the kinetic biomarker.

The application discloses a Caenorhabditis elegans bionic multi-perception-motion behavior method and system, and belongs to the field of artificial pulse neural network control. A pulse neural network model for changing the motion behavior of Caenorhabditis elegans by sensing changes in salt concentration, temperature and tactile stimulation position in the environment is built, the pulse neural network model comprises a bionic forward loop, a backward loop, a salt-seeking loop, a temperature-seeking loop and a tactile loop, and the bionic multi-perception-motion behavior of Caenorhabditis elegans is simulated. The salt-seeking behavior, the temperature-seeking behavior and the touch-avoiding behavior of Caenorhabditis elegans can be better matched, and a complex multi-perception-motion behavior mode can be realized by using a small number of neurons.

A system (3) for providing motion assistance to a user, comprising at least one sensor (5) for measuring values of at least one motion parameter and / or at least one stress parameter, the system comprising at least one stimulus generating module (7), configured to generate at least one stimulus for the user. The system is configured for: - obtaining (OBT) through the sensor (5) a value of the at least one motion parameter and / or a value of the at least one stress parameter, - comparing (COMP) the value of the motion parameter to a motion reference value and / or the value of the stress parameter to a stress reference value and determining a motion behavior of the user, and - generating, according to the determined motion behavior, the at least one stimulus through the at least one stimulus generating module (7), to assist a motion of the user.

This disclosure relates to the field of biomimetic robotics, specifically providing a method and apparatus for generating robot motion trajectories. The method includes obtaining a corresponding behavior sequence based on the robot's motion behavior; for each time point in the behavior sequence, determining the optimal variable corresponding to each time point based on a target dynamics model; and determining the target trajectory corresponding to the motion behavior based on the optimal variable. In this disclosure, a target dynamics model for optimizing high-dynamic motion trajectories is constructed based on the robot's center-of-mass dynamics and whole-body kinematics. This simplifies the trajectory optimization algorithm and effectively reflects the whole-body motion information of the robot during high-dynamic motion. By encoding the corresponding behavior sequence for high-dynamic motion, the method accurately reflects the motion information during various high-dynamic motion processes, thereby improving trajectory planning accuracy.

The present invention discloses a joint angle prediction method based on motor units and neuromusculoskeletal models, including: 1. Acquisition and preprocessing of electromyography data and motion data during joint movement; 2. Extracting the activation positions of motor units and clustering them using the non-negative matrix factorization algorithm; 3. Estimating neural activation through a regression model and a twitch force model; 4. Completing the calculation from neural activation to joint angle based on a specific musculoskeletal model; 5. Optimizing the extended parameter vector in the above-mentioned musculoskeletal model based on a global heuristic search algorithm, and using the optimized neuromusculoskeletal model for joint angle prediction. The present invention can decode motion behaviors from underlying neural commands according to the laws of exercise physiology, thereby enabling more robust and accurate joint angle prediction.

The application relates to the technical field of exoskeletons, in particular to an exoskeleton assistance control method and device with power compensation, equipment and a medium. The method comprises the following steps: when detecting to-be-processed hip joint data of a user wearing an exoskeleton, determining a motion behavior mode and a speed mode of the user; based on power compensation amounts corresponding to the motion behavior mode and the speed mode, calculating a preset reference torque control curve to obtain a first assistance torque for adjusting the motion behavior mode and a second assistance torque for adjusting the speed mode; and performing assistance control according to the first assistance torque and the second assistance torque. The designed assistance curve is distinguished according to different motion modes and speeds, the poor assistance control effect caused by not considering power is avoided, and the purpose of improving the adaptability of assistance control to different modes and speeds is achieved.

The invention provides a mechanical arm man-machine interaction method and system. The method comprises the steps that force data, collected by a force sensor, of external force applied to the tail end of a mechanical arm are obtained; pre-processing the force data, wherein the pre-processing comprises the steps of extracting time domain features and carrying out linear fitting on the time domain features; inputting the preprocessed force data into a pre-trained random forest model, and outputting the type of human motion as a motion intention recognition result; the random forest model is obtained through training by taking force data corresponding to multiple types of human motions as input and taking the types of the human motions as output. According to the mechanical arm man-machine interaction method provided by the embodiment of the invention, the random forest method is adopted to construct the human body upper limb subjective motion intention recognition algorithm based on the force sensor, multiple human body upper limb motion behaviors are recognized, certain predictability is achieved, the human body upper limb motion intention can be recognized in advance, and the real-time performance of a man-machine interaction system is improved.

Methods of predicting mobility of dog movement, under the control of at least one processor, can include collecting movement sensor data from a wearable monitoring device positioned on a dog, wherein the wearable monitoring device includes an accelerometer and a gyroscope (each capable of collecting three axial signals), a processor, and a memory storing instructions that, when executed by the processor, accumulates the movement sensor data in time windows. The method further includes classifying movement behavior as a binary of walk or trot within at least a plurality of the time windows based on the movement sensor data collected from multiple axes of the accelerometer and multiple axes of the gyroscope, and predicting mobility of dog movement based on the movement behaviors as applied to a trained artificial intelligence mobility model. Predicting the mobility of the dog movement can include predicting a binary of healthy mobility or compromised mobility.

The application relates to a spring motion behavior simulation method and system. The spring motion behavior simulation method comprises the following steps: acquiring geometric defect feature data and assembly deviation feature data of a spring; constructing a spring digital model based on the geometric defect feature data and the assembly deviation feature data; predicting local coil contact and buckling deformation of the spring in the spring motion process based on the spring digital model, and quantifying nonlinear friction damping and dynamic effective stiffness variation of the spring. The application can more truly simulate the non-ideal behavior of the spring under the action of complex loads by integrating the actually existing geometric defects and assembly deviations into the digital model, improves the accuracy of design verification, and provides a reliable basis for performance optimization of a vehicle suspension system.

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.

A robot companion device 100 that dynamically adjusts a behaviour of a motor 102 across different surfaces and usage patterns of the motor. The robot companion device includes encoders 104A-N that are coupled to the motor that is configured to measure the speed and the velocity of the motor. The robot companion device includes a processor that is configured to (i) generate an interpolated speed and velocity of the motor and operating conditions of the robot companion device in the surfaces, (ii) detect a motor behaviour state, (iii) determine the motor behaviour as an abnormal behaviour state, (iv) increase the threshold motor speed and velocity of the motor to reduce state fluctuations, and (v) dynamically adjust, using the machine learning model, the behaviour of the motor in the surfaces and the usage patterns of the motor in real-time.

The invention relates to a method for controlling the movement behavior of an artificial joint having an upper part and a lower part which is mounted on the upper part so as to be pivotable about a pivot axis, between which a device for influencing the pivoting capacity or pivoting of the upper part relative to the lower part is arranged, the device is coupled to a control unit, a rule set is stored in the control unit, and the control unit activates, deactivates or modulates the device on the basis of input values for the rule set in order to influence the pivoting or pivoting capability, wherein sensor values of at least one sensor arranged on the upper part or the lower part, detected during use of the artificial joint, are provided to at least one machine learning-based estimation method, the estimation method continuously calculates an estimated value for a dynamic or kinematic parameter or for an expected dynamic or kinematic parameter from the sensor values and provides the estimated value as an input value to the rule set and serves as a criterion for activation, deactivation or modulation in the rule set.

Methods of predicting mobility of dog movement, under the control of at least one processor, can include collecting movement sensor data from a wearable monitoring device positioned on a dog, wherein the wearable monitoring device includes an accelerometer and a gyroscope (each capable of collecting three axial signals), a processor, and a memory storing instructions that, when executed by the processor, accumulates the movement sensor data in time windows. The method further includes classifying movement behavior as a binary of walk or trot within at least a plurality of the time windows based on the movement sensor data collected from multiple axes of the accelerometer and multiple axes of the gyroscope, and predicting mobility of dog movement based on the movement behaviors as applied to a trained artificial intelligence mobility model. Predicting the mobility of the dog movement can include predicting a binary of healthy mobility or compromised mobility.

This invention relates to the field of laboratory animal behavior monitoring technology, and discloses a method, system, and apparatus for monitoring the excretion and movement behavior of laboratory rodents. The method involves laying a filter paper support layer at the bottom of a transparent observation unit and simultaneously acquiring images using camera units positioned above and below the top of the transparent observation unit. This obtains information on the movement behavior and excretion traces of the laboratory rodents, and performs temporal and / or spatial correlation analysis to obtain the monitoring results of excretion and movement behavior. This invention also discloses a system and apparatus for implementing this method, which can be extended to the simultaneous monitoring of multiple transparent observation units, and is suitable for the development and commercial application of experimental devices. Abstract and Figure 4:

A method and a system for rhythmic motion control of a robot based on a neural oscillator, including: acquiring a current state of the robot, and a phase and a frequency generated by the neural oscillator; and obtaining a control instruction according to the acquired current state, phase and frequency and a preset reinforcement learning network so as to control the robot. The preset reinforcement learning network includes an action space, a pattern formation network and the neural oscillator. A control structure designed by the present disclosure, which is composed of the neural oscillator and the pattern formation network, can ensure formation of an expected rhythmic motion behavior; and meanwhile, a designed action space for joint position increment can effectively accelerate the training process of rhythmic motion reinforcement learning, and solve a problem that design of the reward function is time-consuming and difficult in learning with existing model-free reinforcement learning.

Techniques are described for controlling a robotized orthosis based on one or more machine learning models trained with input data produced by sensors during motion of test subjects. Examples of such techniques include collecting raw data and / or post-processed spatiotemporal and kinematics data of a test subject's limbs using sensors while the subject is performing motion activities. The prediction of kinematic behavior of a patient may allow a robotized orthosis to compensate for backlash and / or a slow response by anticipating the motion of the patient. A machine learning model(s) may quickly predict, based on the input data, how the patient will move and provide power to the robotized orthosis to support this movement. As a result, the robotized orthosis may be more comfortable for the patient to use.

The invention relates to the technical field of intelligent terminals, in particular to an intelligent motion behavior sensing terminal based on spatio-temporal context attention, comprising: an instrument body comprising an intelligent physical measurement instrument, the surface of which is provided with a support base; and the adjusting mechanism comprises an adjusting groove, and the adjusting groove is fixedly connected to the surface of the supporting base. Through connection of the adjusting groove and a driving motor, the driving motor can be driven to be started through an infrared sensor, the driving motor can be started to drive worms to rotate, the two sets of worms can synchronously rotate through a connecting rod, and a worm wheel can rotate on the surface of a fixing shaft along with rotation of the worms; the movable block can move in the adjusting groove along with rotation of the worm wheel through the connecting screw rod, and then the action height of the intelligent body measuring instrument can be changed through connection of the movable block and the intelligent body measuring instrument, so that the effect of adapting to people with different heights is achieved.

A method includes engaging Parkinson's Disease (PD) subjects in a continuous motor performance task that elicits natural motor variability, quantifying natural motor variability of each PD subject with an array of motor metrics at short timescales, applying a machine-learning classification or regression algorithm to determine weights for each of these metrics to maximally differentiate each patient's motor performance from that of controls performing the same task, and combining the weights to determine a scalar metric of motor performance for each short epoch of motor behavior.

Device (1, 1') for measuring the Motion Behavior Index (MBI) of a ball comprising a tunnel structure (2, 2') that includes at least one speed sensor for measuring the speed of a ball passing through said tunnel (2, 2'), a processing unit, and output means (3). Said device (1, 1') includes at least one additional sensor for measuring the environmental conditions in which the device is located, and the processing unit is configured to: - receive the measurement values obtained from said sensors; - combine said values using a calculation algorithm to derive the MBI; - use the output means to communicate the MBI to a user.