216 results about "Muscle activity" patented technology

This invention is an article of clothing with electromyographic (EMG) sensors which measures body motion and / or muscle activity. This clothing can be a short-sleeve shirt or a pair of shorts, wherein the electromyographic (EMG) sensors are on the cuffs. The electromyographic (EMG) sensors can be modular; they can be removably attached to different locations in order to create a customized article of electromyographic clothing which optimally measures the muscle activity of a particular person or muscle activity during a particular sport. This clothing can also include bending-based motion sensors.

The invention relates to the technical field of pelvic floor muscle repair, in particular to a pelvic floor muscle repair method and related equipment, and the method comprises the following steps: acquiring a surface electromyogram signal collected by a surface electrode; based on bispectrum analysis, effective feature extraction is carried out on the abdominal electromyographic signals and the pelvic floor electromyographic signals, the extracted features are input into a deep learning model, and the muscle activity state is analyzed; the muscle activity state is fed back to the patient, and the patient is prompted to adjust contraction and relaxation of pelvic floor muscles through voice; through contraction and relaxation of pelvic floor muscles, the pelvic floor function condition is evaluated based on a Glazer evaluation method; and according to the pelvic floor function condition, selecting a corresponding treatment mode for pelvic floor muscle repair. The pelvic floor muscle repairing effect can be improved.

Wearable sensors and uses thereof are disclosed. Preferred sensors include surface EMG electrodes for measuring electrical muscle activity, by which muscle contraction is measured. Such muscle contraction can be used to determine a person's posture, especially when the contraction being measured is that of core muscles. An alert preferably is provided to a person in real time when an incorrect posture is detected. By providing such biofeedback, a person is trained in maintaining a good posture. Such posture training is believed to heal alleviate or even present lower back pain. Wearable sensors of the invention also can be used to measure contraction in muscles during exercise, including running and strength training, to gain information for use in improving such activities.

The invention relates to the technical field of rehabilitation medicine, in particular to a body carving slimming method for fat metabolism based on bioelectricity feedback and acupoint stimulation. The invention provides a body carving slimming method based on bioelectricity feedback and acupoint stimulation. The body carving slimming method comprises the four steps of obtaining, processing, acupoint stimulation and outputting. Bioelectricity signals and weight data of body parts of a user are collected through a bioelectricity and weight detection device. The muscle activity state and fat distribution are analyzed, a posture improvement value is calculated, and a personalized body carving training scheme is generated. In an acupoint stimulation link, electrical stimulation is performed on a specific acupoint according to the scheme, bioelectricity feedback is monitored in real time, and stimulation parameters are dynamically adjusted. And generating a personalized report containing the posture change and the training effect evaluation, and providing suggestions to the user. According to the method, the effect, safety and individuation are remarkably improved, and a new direction is opened up for development of the body carving and slimming field.

The invention relates to the technical field of brain power assessment, in particular to an operator brain power assessment and monitoring system based on manned-unmanned flight cooperative tasks, which comprises the following steps: acquiring brain wave signals, voice fluctuation and finger muscle activity data of an operator, and generating operator brain power load data; setting a personalized load reference value and a threshold range of an operator; based on multi-dimensional regression analysis and a self-adaptive deep learning model, a nonlinear relation model between task characteristics and brain load is constructed, the load state is dynamically evaluated, and a real-time load change prediction value is output; predicting a future high-load risk period according to the fluctuation mode of the mental load fluctuation curve; dynamically adjusting task parameters in a multi-task execution process; according to the invention, the dynamic reference setting of the mental load of the operator is realized, so that the individuation and accuracy of load evaluation are ensured, the influence of key task characteristics on the load can be quickly reduced when the load of the operator is increased, and an efficient and accurate load reduction effect is realized.

The invention discloses a multi-link collaborative evaluation and feedback system for upper limb rehabilitation training, and belongs to the technical field of medical rehabilitation, and the system comprises a data collection module which is used for obtaining the related data of the upper limb of a patient in the rehabilitation training process, and the related data comprises the motion posture, the muscle activity condition and the joint activity degree; the data processing module responds to the data acquisition module and is used for preprocessing the acquired related data, including filtering and denoising the related data, acquiring feature information in the preprocessed related data and sending the feature information to the data acquisition module; the feature information comprises movement tracks of joints, contraction intensity and time of muscles and coordination of upper limb movement. Multi-dimensional data such as action postures, muscle movement conditions and joint movement ranges in upper limb rehabilitation training are collected, preprocessing and feature extraction are carried out through the data processing module, the upper limb rehabilitation condition of a patient can be comprehensively and accurately evaluated, and a basis is provided for formulating a personalized rehabilitation scheme.

The invention relates to a motion capture optimization method of a tactical AR helmet. Based on a motion model and dynamic changes of muscle activities and biological signals; collecting data in real time through a sensor, and establishing a time-dependent model of motion data by using a time sequence analysis method; in combination with an LSTM (Long Short Term Memory) model in deep learning, capturing head motion and time dynamic change of motion, and predicting and correcting an error in motion capture; intelligent processing is carried out based on continuous time sequence data, and short-term errors of capturing data drift are avoided; designing a multi-level sensor correction algorithm, and performing time alignment and error correction on IMU data and visual data; drift phenomena including position errors and rotation errors in motion capture are eliminated; optimizing the helmet capture system using a ground sensor including a gait monitoring device and external vision calibration data; a target-oriented motion trail optimization algorithm is designed by combining a motion target and a tactical task of a user, and a capture strategy is dynamically adjusted according to the change of the task.

The invention discloses a wavelet enhancement cooperative activation method for FES multi-target time sequence regulation and control, and relates to the technical field of biomedical engineering. According to the model, an overlapped sliding window is analyzed through a multi-channel surface electromyographic signal, a time-frequency energy significant feature matrix is constructed by combining an energy ratio, and meanwhile, an inter-muscle co-activation matrix is constructed in the multi-channel electromyographic signal sliding window; constructing a multi-channel high-dimension matrix by using each single-channel time-frequency energy significant feature matrix and the multi-channel inter-muscle co-activation matrix, and performing data dimension reduction on the multi-channel high-dimension matrix to obtain a multi-channel multi-dimension time-frequency energy-inter-muscle co-activation matrix; and a muscle activation time sequence vector is screened by using a self-adaptive threshold logic combination rule, and finally an activation time sequence is applied to an FES experiment. According to the method, the movement of the hand on the sagittal plane is accurately predicted by analyzing the multi-channel surface electromyogram signals and the multi-target muscle co-activation characteristics, functional electrical stimulation FES parameters are dynamically adjusted, and accurate control over the muscle activity stage is achieved.

The invention discloses an exoskeleton attitude prediction system for building construction, particularly relates to the field of attitude prediction, and comprises a multi-dimensional sensing module, a data preprocessing module, an attitude prediction model module and a control decision module. A multi-dimensional sensing module collects joint movement, muscle movement and human-computer interaction force data through a nine-axis IMU, a surface myoelectricity sensor and a miniature strain type force sensor; the preprocessing module obtains standardized feature data through filtering, standardization and exception elimination; the attitude prediction model uses a CNN-LSTM hybrid architecture, and outputs a target attitude of 0.5-2 seconds; and the control decision module compares a prediction result with a safety threshold value, generates control instructions in three levels to drive exoskeleton correction, synchronous feedback and recording, solves the problem of response delay of a traditional exoskeleton, and improves the construction safety and efficiency.

The invention relates to the field of mechanical power assisting devices, in particular to a power assisting regulation and control method and system of a walker, a barrier-free walking process under the condition that a user wears the walker is detected, walking gait characteristics and muscle activity characteristics of the user are obtained, and a leg muscle activity state model of the user is constructed; detecting an obstacle avoidance walking process when the user wears the walker, and obtaining leg pressure characteristics of the user, so as to predict leg action information about to occur by the user; and based on the leg muscle activity state model and the leg action information, determining assistance demand information of the legs of the user so as to adjust the assistance effect provided by the walker to the legs of the user. According to the walking device, the leg assisting requirement can be accurately determined, multi-directional assisting support is provided for leg muscles, walking rehabilitation training is strengthened, and the assisting accuracy and reliability of the walking device are improved.

The invention provides a system for controlling a manipulator in real time through hand electromyographic signals, which comprises a data acquisition unit, a terminal equipment unit and a tail end execution unit which are in communication connection through serial ports. The data acquisition unit comprises a data acquisition device and a signal acquisition module, and is configured to acquire a multi-channel electromyographic signal of a hand of a user in real time and perform signal conditioning and digitization so as to obtain a digital signal sequence representing a muscle activity intention of the user; the terminal equipment unit comprises a data preprocessing module, a model training module, a gesture recognition module and an instruction generation module; the tail end execution unit comprises a manipulator control unit which is in communication connection with the control output module and is configured to be used for receiving the manipulator control instruction and driving a manipulator to execute corresponding actions; in combination with a control method, the problems of insufficient real-time performance, poor hand action recognition precision and robustness, unstable control instruction, poor user adaptability and the like in the prior art are solved, and the man-machine interaction performance is improved.

The invention provides a multi-source information fusion intelligent rehabilitation glove system. A main control unit of the system is used for coordinating and controlling all parts of rehabilitation gloves; the charging interface charges the battery; the control panel provides an interactive interface between a user and the system; the servo linear motor is used for controlling movement and position feedback of the rehabilitation glove; the battery supplies power to the system; the LED lamp is used for visual stimulation; the buzzer is used for audio stimulation; the low-frequency electrical stimulation interface is used for electrical stimulation; the multi-source fusion control module is used for monitoring a pressure signal and a muscle activity potential signal of the glove and controlling the movement and strength of the glove in real time; and wireless communication is used for data transmission and remote monitoring. By integrating multi-source information sensory stimulation, the rehabilitation process is enhanced, and particularly, the rehabilitation of damaged nerves is facilitated. Secondly, the multi-source information fusion control module monitors and adjusts hand movement and muscle movement of the patient in real time through a pressure sensor and a myoelectricity sensor, and personalized rehabilitation support is provided.

A method and system for facilitating stimulating of muscles of users are provided. Further, the method may include a step of receiving muscle data from sensors, analyzing the muscle data, generating first muscle activity data of the muscles of first users based on the analyzing of the muscle data, receiving first biological metrics of the first users from first devices, receiving second biological metrics of second users from second devices, analyzing the first muscle activity data, the first biological metrics, and the second biological metrics, transforming the first muscle activities into second muscle activities of the muscles of the second users, generating muscle activation commands for the muscle activation of the muscles of the second users, transmitting the muscle activation commands to muscle stimulators disposable on body parts of the second users, and storing the first muscle activity data and the muscle activation commands.

The invention relates to the technical field of nursing monitoring, in particular to a nursing monitoring system for collecting postoperative data of a patient based on myoelectricity sensing. The system comprises a frequency calculation unit, a power quality unit and a monitoring scheme unit. The active degree of the muscle signals around the postoperative wound of the patient is monitored through the broken line and the horizontal reference line in the postoperative wound electromyogram of the patient, when the root mean square point of the corresponding electromyographic signal in the broken line is higher than the horizontal reference line, it is monitored that the active degree of the muscles around the postoperative wound of the patient is increased, and the postoperative wound recovery state of the patient is good; the postoperative wound recovery condition of the patient is monitored in real time through the electromyogram, unnecessary examination and intervention can be reduced, a nurse can accurately judge the muscle activity condition of the patient according to the result of the electromyogram, and pain and burden brought to the patient by excessive examination are avoided. Meanwhile, unnecessary measures can be avoided, the medical cost is reduced, and the nursing efficiency is improved.

The invention relates to a multi-mode stimulation dysphagia training optimization method. According to the technical scheme, the method comprises the following steps that basic physiological data including age, gender and medical history of a patient and real-time swallowing data including throat muscle activity, electrophysiological signals and swallowing pressure of the patient are collected; performing fusion processing on the real-time swallowing data S1.1 by using a multi-modal data fusion technology to generate a personalized swallowing function evaluation report; on the basis of an evaluation result, a personalized dysphagia training scheme is generated through an intelligent algorithm, and the personalized dysphagia training scheme comprises a combination of multiple stimulation means such as tactile sensation, electrical stimulation, light stimulation and sound stimulation; constructing a real-time data acquisition and feedback system, and monitoring multi-modal data including muscle activity, sound signals, facial expressions and respiratory rate of a patient in a training process in real time by using a sensor; through a dynamic time warping (DTW) algorithm, real-time monitoring data is compared with an expected index in a training scheme, and the stimulation intensity and mode are adjusted in real time.

The invention discloses a multi-modal fusion muscular tension dynamic assessment method and system, relates to the technical field of biological dynamic assessment, and is used for solving the problem that muscle activity grade classification errors are increased due to dependence on single surface myoelectricity or joint angle sensor data. Joint angle variation, resistance variation and force feedback intensity are extracted, an evaluation mechanism is constructed in combination with region division and historical threshold values, and muscle activity evaluation levels are calculated; and comparing a preset threshold value to identify a flexibility level, entering a resistance evaluation mechanism when a condition is met, analyzing a muscular tension change trend in resistance data, completing dynamic refined judgment of a flexible state, realizing staged, regionalized and trended fusion evaluation, and improving muscular tension state identification and flexibility level judgment precision.

The invention discloses a rehabilitation training evaluation method based on dream guidance in combination with myoelectricity monitoring. The method comprises the following steps: collecting and preprocessing electroencephalogram data; analyzing the sleep state and judging whether the sleep state is a deep sleep state; performing stimulation guidance of a deep sleep state; repairing the deep sleep dream; performing real-time myoelectricity monitoring by using myoelectricity monitoring equipment after the patient enters the deep sleep state, and performing motor imagery marking in combination with the electroencephalogram signals; and performing rehabilitation training effect evaluation in combination with the myoelectricity data and the electroencephalogram data. According to the invention, a patient is guided to perform nerve rehabilitation treatment in a mode of monitoring dream by electroencephalogram, so that the sleep time is used for training to improve the training duration, and environmental interference factors can be reduced through deep sleep; the muscle activity effect of the dream guiding part can be judged in an auxiliary mode through the myoelectricity monitoring means, and whether the patient is correctly guided or not is judged in combination with monitoring of the brain electricity on the muscle part.

The invention relates to the technical field of electrical stimulation pulse control, in particular to an electrical stimulation pulse type physiotherapy instrument and an electrical stimulation pulse self-adaptive regulation and control method thereof. According to the method, in a neighborhood range of each relaxation and contraction interval, an accumulated deviation index and a deviation contribution index of each dimension myoelectricity characteristic of each relaxation and contraction interval are obtained according to a change trend of each dimension myoelectricity characteristic of different relaxation and contraction intervals; according to the weights of the myoelectricity characteristics of different dimensions of each relaxation and contraction interval and the difference fluctuation characteristics of the myoelectricity characteristics of each dimension of each relaxation and contraction interval between the adjacent positions, the propagation characteristic change degree in each relaxation and contraction interval is obtained; in combination with the fluctuation degree of the moment difference between the adjacent positions, obtaining a muscle activity index of each relaxation and contraction interval; and adjusting the electrical stimulation pulse. According to the method, different stimulation responses at different positions are considered, the active effect of muscles is analyzed, and the regulation and control accuracy is improved.

The invention discloses a lower limb exoskeleton control weight distribution prediction method based on artificial intelligence. Comprising the following steps that a, biomechanical data, including but not limited to joint angles, muscle activities and ground counter-acting force, of the lower limbs of a wearer are collected through a plurality of sensors; b, a deep learning model is utilized, and the model is trained through a large amount of lower limb movement data in advance to recognize weight distribution characteristics in different movement modes; c, according to the collected biomechanical data, the model predicts the weight distribution borne by each part of the lower limb in real time; and d, according to a prediction result, intelligently adjusting the supporting force of each joint of the exoskeleton so as to optimize the gait of the wearer and reduce energy consumption. Energy consumption and exercise efficiency are optimized, specifically, according to the predicted weight distribution, the supporting strategy of the exoskeleton is intelligently optimized, the energy consumption of the wearer in the exercise process is reduced, and the exercise efficiency is improved. And by reducing unnecessary energy waste, the service life of the battery is prolonged, and the practicability of the exoskeleton is enhanced.

An apparatus (1) comprising a garment (2), the garment comprising: at least one muscle activity sensor (4, 34) for detecting activity of a muscle at least partially covered by the garment; at least one electrical visual indicator device (14); and a controller (11) configured to: receive an activity signal indicative of detected activity of the muscle from the at least one muscle activity sensor; and cause the at least one electrical visual indicator device to output a visual indication of the detected activity of the muscle in dependence on the received activity signal.

The invention relates to the technical field of cervical vertebra rehabilitation systems, and discloses an intelligent self-adaptive cervical vertebra rehabilitation system. The system comprises a multi-mode sensing unit which synchronously collects form data and soft tissue data of the cervical vertebra of a user through an optical mark point array and an ultrasonic probe array to generate a multi-source sensing data set; the intelligent analysis unit constructs a cervical vertebra dynamic mechanical model by using a graph convolutional neural network based on the multi-source sensing data set, analyzes a cervical vertebra joint coupling relation and stress distribution, and generates an analysis parameter set; the strategy generation unit generates a personalized rehabilitation strategy according to the analysis parameter set, the personalized rehabilitation strategy comprises an action sequence and an impedance adjustment parameter, and a strategy instruction set is formed; the dynamic control unit responds to the strategy instruction set and combines muscle activity data fed back by the electromyographic signal sensor in real time to adjust an execution strategy and generate a control command set; and the flexible driving unit drives the soft robot outer skeleton to perform multi-axis linkage rehabilitation training based on the control command set.

The invention belongs to the technical field of shoulder joint detection, and discloses a shoulder joint stability evaluation system based on surface electromyogram signals, which comprises a data acquisition module, a data processing module and an evaluation analysis module. According to the invention, by synchronously acquiring the angle signal and the surface electromyogram signal, the muscle activity dynamic change of the shoulder joint in the whole movement process can be accurately captured in real time, the evaluation accuracy is remarkably improved, and a foundation is built for accurate diagnosis; in a signal processing link, a plurality of filtering methods are comprehensively applied to carry out deep purification on original surface electromyogram signals, various interference factors such as 50Hz power frequency interference, low-frequency motion artifacts and high-frequency noise are effectively eliminated, the purity and reliability of the signals are guaranteed, and high-quality data are provided for subsequent analysis; and meanwhile, a characterization index of an amplitude absolute value mean value is introduced, so that the muscle activity intensity is reflected more visually and accurately, and the internal correlation between the shoulder joint stability and the muscle activity is deeply excavated.

An electromyography (EMG) measurement device includes a garment configured to be worn on an anatomical region of an associated wearer, a plurality of electrodes arranged on the garment to contact skin of the anatomical region when the garment is worn on the anatomical region of the associated wearer, electronics operatively connected with the plurality of electrodes and configured to measure EMG data emanating from the anatomical region, and an electronic processor programmed to derive a contribution of spatial muscle activity of a target muscle or muscle group to the measured EMG data.

The invention provides a tibial nerve stimulation device and system for treating overactive bladder. The tibial nerve stimulation device comprises a program controller, a pulse generator and a stimulation electrode. The program controller is programmed to be capable of sending out signal parameter setting instructions with the frequency ranging from 500 Hz to 100 kHz, the amplitude ranging from 0.1 mA to 10 mA and the pulse width ranging from 1 us to 1 ms. And the pulse generator is in communication connection with the program controller and is used for generating a specific pulse electric signal according to the signal parameter setting instruction. One end of the stimulating electrode can be implanted into the tibial nerve of a tested object, the other end of the stimulating electrode is electrically connected with the pulse generator, and the stimulating electrode is used for receiving the specific pulse electric signal and transmitting the specific pulse electric signal to the tibial nerve for electrical stimulation. The tibial nerve stimulation device provided by the invention can emit a pulse electric signal with a higher frequency to the tibial nerve, not only can be used for treating overactive bladder, but also can effectively relieve untoward effects such as abnormal feeling or muscle activity of a stimulated part in the treatment process.

The embodiment of the invention provides an information processing method for a temporomandibular joint, which comprises the following steps: acquiring sensor information, determining six-degree-of-freedom motion information of the temporomandibular joint according to the sensor information, generating a three-dimensional motion track curve of the temporomandibular joint according to the six-degree-of-freedom motion information, and acquiring pain level information. According to the pain level information and the three-dimensional motion track curve, determining a lesion joint and obtaining a joint bouncing audio, according to the joint bouncing audio and the three-dimensional motion track curve, determining a bouncing type and obtaining muscle activity information, and according to the muscle activity information and the three-dimensional motion track curve, determining a muscle abnormal mode, and determining a diagnosis result of the temporomandibular joint based on the three-dimensional motion track information, the diseased joint, the bouncing type and the muscle abnormal mode. On the basis, the method and the device are used for fusing various information of the temporomandibular joint of the patient, and the accuracy of determining the cause of the temporomandibular joint disorder is improved through the motion-sound-myoelectricity combined characteristics.

The invention relates to a ventilation detection device for a myasthenia gravis patient, and the device comprises a muscle strength monitoring module which is configured to monitor the swallowing muscle of the myasthenia gravis patient and the muscle activity of the near end of the limb, and obtains the muscle strength data of the myasthenia gravis patient; the respiration monitoring module is configured to detect the respiration frequency, the tidal volume of each respiration cycle and the inspiration time of each respiration cycle under the condition that mechanical assisted ventilation is carried out on the myasthenia gravis patient, and the change trend along with time is obtained; the control module is in signal connection with the muscle strength monitoring module and the respiration monitoring module, and is configured to control the respiration monitoring module when the muscle strength data of the serious myasthenia patient is lower than a preset muscle strength threshold value and the change trend of the respiration frequency, the tidal volume of each respiration cycle and the inspiration time along with the time is inconsistent with the change trend of pre-stored corresponding parameters; and determining that man-machine resistance exists in the mechanical assisted ventilation process of the myasthenia gravis patient.

Typically, the human joints and muscles are complex regions of the body that have to satisfy several demands such as mobility, stability, and strength. Therefore, capturing these subtleties requires advanced techniques. Accordingly, a biometric device, comprising; an adhesive patch; a power supply; an electrode array with one or more sensor elements configured to measure electrical signals generated by one or more muscles; and a machine controller communicatively coupled to the electrode array configured to receive signals from the electrode array based on the electrical signals generated by one or more muscles measured by the sensor elements, process the signals to assess the patient's health conditions, trigger a response based on a set criteria including whether the patient is not using a muscle, over-using a muscle, or not complying with recommended muscle activity, or generating health or disease-related signals, and provide related feedback based on the response is disclosed.

The present invention relates to the technical field of electric stimulation pulse control, and specifically to an electric stimulation pulse physiotherapy instrument and its electric stimulation pulse adaptive control method. The present invention obtains the cumulative deviation index and deviation contribution index of each dimensional electromyographic feature of each relaxation and contraction interval according to the change trend of each dimensional electromyographic feature of different relaxation and contraction intervals within the neighborhood range of each relaxation and contraction interval; obtains the degree of change of the propagation characteristics in each relaxation and contraction interval according to the weight of the electromyographic features of different dimensions of each relaxation and contraction interval, and the difference fluctuation characteristics of each dimensional electromyographic feature of each relaxation and contraction interval between adjacent positions; obtains the muscle activity index of each relaxation and contraction interval in combination with the fluctuation degree of the moment difference between adjacent positions; and adjusts the electric stimulation pulse. The present invention analyzes the activation effect of the muscle and improves the accuracy of regulation by considering the different stimulation responses of different positions.

The invention provides a self-generating electricity stimulation scoliosis orthosis, and belongs to the technical field of medical instruments. The device comprises a support, protection plates installed at the two ends of the support, a correction belt adjuster installed on the support, a correction belt connected to the correction belt adjuster, a pressure sensor arranged on the correction belt, piezoelectric material modules arranged on a chest belt, a waist belt and the correction belt respectively, and electrode slices used for being connected with the piezoelectric material modules. The correction force is monitored in real time through the pressure sensor and transmitted to the terminal, the piezoelectric material module is utilized to generate electricity for stimulation under the action of mechanical pressure, collaborative treatment of muscle atrophy and the correction force is achieved, the dual functions of accurate regulation and control of the correction force and maintenance of muscle activity are achieved, and the correction effect is improved.