21 results about "Muscle activity" patented technology

A system and method for visualizing a position of a needle inside a patient's body for use in providing regional anesthesia and a system and method for providing electro anesthesia during surgery, post surgery and during rehabilitation. The method and system use EMG / AMG information regarding activity of a muscle associated with a target nerve to provide a visualization of a position of a needle or other device relative to the target nerve and to determine efficacy of the electro anesthesia. EMG / AMG information and feedback information such as EEG information may be used to provide automatic adjustment of a waveform provided for electro anesthesia to maintain a suitable pain level.

A conducting composition includes a topological polymer (e.g., a polyrotaxane polymer) and poly(3,4-ethylene-dioxy thiophene):polystyrene sulfonate (PEDOT:PSS). Devices may include a conducting layer including the conducting compositions, e.g., a device comprising a conducting layer, wherein the conducting layer includes a polyrotaxane polymer and a PEDOT:PSS array. The devices are useful in bioelectronics, including high-resolution electrophysiological monitoring of deformable tissues and localized neuromodulation for high-precision control of individual muscle activities.

This invention discloses a method and system for constructing a disaster resilience classification training program for clinical nurses, belonging to the fields of disaster nursing management and computer image recognition technology. It involves acquiring facial micro-expression image sequences of nurses in standardized disaster simulation scenarios; extracting the activation frequency, activation duration, and left-right facial activation symmetry features of the zygomaticus major and orbicularis oculi muscles and inputting them into a preset resilience classifier; outputting subtype labels based on a fixed mapping relationship between the spatiotemporal features of muscle activity and resilience subtypes; retrieving the corresponding basic training framework based on the labels; comparing the activation duration features with a threshold; and generating reinforcement training labels on the framework using image overlay technology to form a personalized classification training program. This invention achieves objective and real-time subtype identification of clinical nurses' disaster resilience and can automatically generate differentiated training content targeting resource deficiencies in different subtypes, significantly improving the accuracy and automation level of disaster resilience training.

This invention discloses a wavelet-enhanced co-activation method for multi-target temporal modulation of functional electrical stimulation (FES), relating to the field of biomedical engineering technology. The model constructs a time-frequency energy salient feature matrix by analyzing multi-channel surface electromyography (SEMG) signals using an overlapping sliding window combined with energy proportions. Simultaneously, it constructs an intermuscular co-activation matrix within the multi-channel SEMG sliding window. Each single-channel time-frequency energy salient feature matrix and the multi-channel intermuscular co-activation matrix are then combined to form a multi-channel high-dimensional matrix. This high-dimensional matrix is ​​then dimensionality-reduced to a multi-channel, multi-dimensional time-frequency energy-intermuscular co-activation matrix. An adaptive threshold logic combination rule is used to filter muscle activation temporal vectors, and finally, the activation temporal sequence is applied to FES experiments. This invention, by analyzing multi-channel SEMG signals and multi-target muscle co-activation characteristics, accurately predicts hand movements in the sagittal plane and dynamically adjusts FES parameters, achieving precise control over muscle activity phases.

This invention provides a device for acquiring electromyographic (EMG) signal data in gait research, primarily improving the durability, anti-interference capability, comfort, adaptability, and stability of the sensor fixation device. The invention includes a sensor fixation base, a first support arm, and a second support arm; the sensor is disposed within the sensor fixation base; the sensor fixation base is hinged to both the first and second support arms; after hinged connection, the first support arm, the sensor fixation base, and the second support arm together form an arc shape. This device must effectively prevent sensor displacement, minimize interference and pressure on muscle activity, and be highly adaptable to different individual muscle morphologies and skin thicknesses, thereby ensuring continuous, stable, and accurate acquisition of high-quality EMG signals in gait research.

The application discloses a real-time analysis system and method for muscle activity based on electromyographic signals, and relates to the technical field of electromyographic signals, and comprises the following steps: calling signal acquisition records of user project training, extracting corresponding sampling frequencies, and obtaining target sampling frequencies; collecting electromyographic data of muscle contraction completed by the user; decomposing the training project into a plurality of training actions, obtaining a reference vector set of each limb, and obtaining an effective time period of each training action; dividing the effective time period into a plurality of time windows, establishing a time domain function corresponding to the effective time period, and obtaining a target time length of each training action; acquiring a training action at the current training time, evaluating abnormal conditions of the training action, and giving early warning and prompt to relevant instructors. The application analyzes historical signal acquisition records in combination with multi-dimensional characteristics, monitors abnormal behaviors in the project training process in real time, gives early warning and prompt to instructors in a timely manner, and is helpful to reduce the risk of muscle injury of the user.

Disclosed herein are new dosage regimens for deuterium-substituted benzoquinoline compounds, and methods for the treatment of abnormal muscular activity, movement disorders, and related conditions.

This invention relates to the field of lower limb monitoring technology, and more particularly to a device for monitoring the muscle activity of the human lower limbs. The device includes a wearable cylinder containing a monitoring component. A waist elastic band is located at the top of the wearable cylinder. The monitoring component includes a leg-length fixing band fixedly connected within the wearable cylinder. Through the coordinated use of the wearable cylinder, leg-length fixing band, exhaust pipe, and sealing plug, the wearable cylinder and leg-length fixing band conform to the curves of the human body, providing comfortable wear. Furthermore, air cushions are evenly distributed on the inner side of the leg-length fixing band, corresponding to the patient's muscle groups. Inflation and deflation adjust the fullness of the air cushions, ensuring that the inertial sensor fits tightly against the patient's skin, thereby monitoring the activity of the lower limb muscles. A connecting tube connects the air cushions to the inlet and outlet pipes. The sealing plug facilitates sealing of the inlet and outlet pipes. The operator can inflate and deflate the air cushions according to actual needs.

Peripheral nerve field stimulation (PNFS) may be controlled based on detected physiological effects of the PNFS, which may be an efferent response to the PNFS. In some examples, a closed-loop therapy system may include a sensing module that senses a physiological parameter of the patient, which may be indicative of the patient's response to the PNFS. Based on a signal generated by the sensing module, the PNFS may be activated, deactivated or modified. Example physiological parameters of the patient include heart rate, respiratory rate, electrodermal activity, muscle activity, blood flow rate, sweat gland activity, pilomotor reflex, or thermal activity of the patient's body. In some examples, a patient pain state may be detected based on a signal generated by the sensing module, and therapy may be controlled based on the detection of the pain state.

Disclosed is a method for restoring movement to a spastic muscle group. The method includes measuring one or more spinal activity values in association with a movement and decoding the measured spinal activity values to determine a time-varying neural activity pattern. The method further includes performing spinal cord stimulation (SCS) of one or more afferent fibres associated with the muscle group to enable muscular activity assisting the movement, wherein the SCS is modulated according to the neural activity pattern.

The application discloses a portable integrated limb bandaging life monitoring device and relates to the technical field of biomedicine. The device comprises two upper protective plates and two lower protective plates, soft connecting plates are connected between the two upper protective plates and the two lower protective plates, air bags are arranged on the inner sides of the two upper protective plates, the two soft connecting plates and the two lower protective plates, and the number of the air bags is three groups; the device further comprises a feedback module, wires are electrically connected to the feedback module, a life monitoring module is electrically connected to the feedback module, and three groups of muscle activity sensors are fixedly connected to the inner sides of the air bags. The muscle activity sensors are divided into three groups, each group of the muscle activity sensors is arranged on the inner side of the upper protective plate, the soft connecting plate and the lower protective plate, the muscle activity of each part of the bandaged limb is monitored, the activity state data of the human muscle, the limb and the whole body are collected in real time, and the activity intensity, the action mode and the physiological load are judged according to the data.

This application discloses a method and related equipment for long-term monitoring of electromyographic activity. The method includes: segmenting a first surface electromyographic signal, a first muscle electromyographic signal, and a first muscle sound signal acquired within a first preset time period by a preset time length to obtain multiple time segments of the first surface electromyographic signal, the first muscle electromyographic signal, and the first muscle sound signal; determining the mapping relationship between the first surface electromyographic signal and the first muscle electromyographic signal based on the first surface electromyographic signal, the first muscle electromyographic signal, and the first muscle sound signal within each time segment; acquiring the second surface electromyographic signal and the second muscle sound signal of the target muscle within a second preset time period; and predicting the second muscle electromyographic signal of the target muscle within the second preset time period based on the mapping relationship. This application can achieve long-term and accurate monitoring of specific muscle activity while maintaining low invasiveness, and can be widely applied in the field of electromyographic signal processing technology.

This invention discloses a post-orthopedic rehabilitation training monitoring device and system based on intelligent sensing, belonging to the technical field of rehabilitation training monitoring systems. By setting up a data acquisition unit and a data processing unit, it enables the continuous acquisition and unified recording of various types of motion data generated during post-orthopedic rehabilitation training, such as joint angles, muscle activity states, and movement speeds. Real-time monitoring of the patient's training movements is achieved using accelerometers, gyroscopes, and electromyography (EMG) sensors, allowing for the synchronous acquisition of joint movement trajectories, muscle contraction states, and movement rhythms. This enables the system to continuously monitor changes in movement during patient training, providing a complete data foundation for subsequent movement evaluation. Noise removal and outlier processing of the raw data filter out data that may be affected by environmental interference or transient fluctuations during sensor acquisition, thereby reducing the impact of data fluctuations on subsequent analysis.

The objective is to provide an exercise assistance device, exercise assistance method, and exercise assistance program that can induce voluntary movement from users by generating muscle activity equivalent to that of the target exercise, even when users perform alternative exercises instead of the target exercise. [Solution] This device provides positive or negative assistive force to a user performing an alternative exercise instead of a target exercise, thereby generating muscle activity equivalent to that of the target exercise. It comprises a simulation unit 11, an electromyography data input unit 12, a muscle synergy analysis unit 13, an output assistive pattern generation unit 14, an output unit 15, a storage unit 16, and an exercise device 30. The simulation unit 11 inputs assistive patterns, which combine phase and assistive force in a time series, into a musculoskeletal model to generate electromyography data sets for each assistive pattern. It includes a body movement data measurement unit 11a, a muscle strength data input unit 11b, a musculoskeletal model generation unit 11c, and an assistive force adjustment unit 11d.

This invention relates to the field of intelligent medical monitoring technology, specifically a pain assessment model based on facial expressions and AI. The model includes: starting from acquired continuous facial image sequences, dynamically detecting and labeling abnormal muscle regions whose activity intensity exceeds an individual's baseline level based on facial muscle anatomy. These abnormal regions are then time-series tracked, their activity intensity evolution trajectory is quantified, and the correlation coefficients of activity changes between different regions are analyzed to construct a multi-region collaborative relationship matrix. The regional activity intensity evolution trajectory data and the collaborative relationship matrix are fused, and processed by a pre-trained pain feature classifier to output a pain probability distribution. This invention improves the accuracy and reliability of pain expression recognition, especially when individual differences and complex expression confusion exist, by performing abnormal detection and dynamic collaborative analysis at the physiological muscle activity level.

This invention discloses a muscle electrical stimulation device and method. The device includes: an electrical stimulation component, which is a flexible structure with electrode contacts on its surface, and can be flexibly fitted onto the muscle to be stimulated; a control module, wiredly connected to the electrical stimulation component, which has multiple preset electrical stimulation programs and executes the corresponding programs according to different muscle sites, controlling the electrical stimulation component to apply electrical stimulation to the muscle; a power supply unit, connected to the control module, providing power to the muscle electrical stimulation device; and fixing components, disposed on both sides of the electrical stimulation component. The device provided by this invention allows for passive muscle movement through electrical stimulation without requiring active human effort, effectively maintaining muscle activity and blood circulation. It is suitable for muscle function maintenance scenarios in confined spaces, offering convenience and strong applicability.

To improve the decoding accuracy of motor output. [Solution] A biosignal processing device according to one aspect of this invention comprises a brain activity sensor, a muscle activity sensor, a muscle strength sensor, a brain activity signal processing unit, an electromyography signal processing unit, a muscle strength signal processing unit, and a learning unit. The brain activity signal processing unit calculates brain activity signal features from brain activity time-series data from the brain activity sensor. The electromyography signal processing unit calculates electromyography signal features from electromyography time-series data from the muscle activity sensor. The muscle strength signal processing unit calculates motor output signal features from motor output time-series data from the muscle strength sensor. The learning unit constructs a trained model by training a state transition model, which models time-series motor output and time-series muscle activity as state variables and time-series brain activity as an observed variable, using brain activity signal features, electromyography signals, and motor output signal features.

The embodiment of the application provides a kind of temporomandibular joint information processing method, by obtaining sensor information, according to sensor information, determine the six degrees of freedom motion information of temporomandibular joint, according to six degrees of freedom motion information, generate the three-dimensional motion trajectory curve of temporomandibular joint, obtain pain grade information, according to pain grade information and three-dimensional motion trajectory curve, determine pathological joint, obtain joint click audio, according to joint click audio and three-dimensional motion trajectory curve, determine click type, obtain muscle activity information, according to muscle activity information and three-dimensional motion trajectory curve, determine muscle abnormal mode, based on three-dimensional motion trajectory information, pathological joint, click type and muscle abnormal mode, determine the diagnosis result of temporomandibular joint.Based on this, the multiple information fusion of patient temporomandibular joint is carried out in the application, and the accuracy of temporomandibular joint disorder cause determination is improved through motion-sound-muscle combined features.

This project proposes an innovative exoskeleton designed to assist paraplegic individuals, incorporating advanced technologies to optimize its effectiveness. The major innovation lies in the intelligent control system based on predicting the biomechanical movements desired by the user. This system uses machine learning and artificial neural networks to analyze and predict joint movements based on intentions detected in real time. Using a rich database, it synchronizes the device's movements with the user's biomechanical needs. The system adapts dynamically thanks to a fuzzy logic module, which adjusts the torque of the joints according to the forces required to perform each movement. This adaptation takes into account several parameters such as the user's age, weight, and muscle activity, thus guaranteeing personalized and optimal assistance. The exoskeleton also features EMG sensors and measurement systems such as accelerometers and gyroscopes, ensuring precise movement control. All of these technologies allow for the reproduction of natural human body movements while providing fluid and autonomous mobility, thus improving users' quality of life. The project presents a comprehensive, adaptable, and highly personalized solution to overcome the limitations of existing devices.

Disclosed herein are new dosage regimens for deuterium-substituted benzoquinoline compounds, and methods for the treatment of abnormal muscular activity, movement disorders, and related conditions.

The application discloses a kind of motion intention recognition method and system based on multi-modal data, it is related to human motion intention recognition technical field, including: by fusing sEMG and accelerometer, gyroscope, electrocardiogram, muscle sound chart and so on multidimensional physiological signal, muscle activity, motion posture, physiological state and so on information in the process of human motion are comprehensively captured, make up the limitation of single signal;Adopt principal component analysis to the multi-modal feature is fused dimension reduction, while reducing data redundancy, generate the comprehensive feature representation that can accurately represent the evolution of motion state;Key parameters of time attention mechanism Transformer are optimized using whale optimization algorithm, avoid the problem of insufficient fitting degree and low recognition accuracy caused by experience parameter selection;Through dynamic updating comprehensive feature representation, realize the real-time iterative identification of motion intention, improve the timeliness and accuracy of identification result.