102 results about "Muscle activity" patented technology

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 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.

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.

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 discloses a doctor-patient speech communication model training method and system based on multi-modal corpus analysis, and belongs to the crossing field of artificial intelligence and medical treatment, and the method comprises the steps: carrying out the extraction according to an OpenPose algorithm to obtain motion features, carrying out the muscle activity intensity detection to obtain expression features, carrying out the speech recognition to obtain text features, and carrying out the recognition of the text features; speech segmentation is carried out based on the time domain energy parameters, and a segmentation result is subjected to modal analysis to obtain acoustic features; performing feature fusion based on an attention mechanism to obtain fusion features, and inputting an obstacle recognition model to obtain an obstacle type; the method comprises the steps of obtaining an obstacle type, obtaining an intervention strategy and an intervention identity according to a solution mapping relation, taking the obstacle type, the intervention strategy and the intervention identity as multi-dimensional labels, carrying out time domain alignment and structured packaging to obtain target data, and training according to the target data to obtain a communication model used for providing dialogue prompt information. Obstacle recognition accuracy can be improved, and the intelligent agent application effect can be improved.

The invention belongs to the technical field of medical electronic equipment, and particularly relates to a wireless surface electromyogram nerve conduction testing system which comprises a flexible wearable sensor module. A multi-mode signal processing module and a wireless transmission and edge calculation module; an intelligent analysis terminal; according to the wireless surface electromyogram nerve conduction test system, a wireless transmission mode is adopted, limitation of a cable to motion is avoided, motion artifacts caused by cable traction are reduced, acquired signals can reflect muscle activity and nerve conduction conditions more truly, the risk of missed diagnosis or misdiagnosis is reduced, and the test efficiency is improved. Secondly, high-precision synchronization of signals and effective noise reduction of the adaptive noise reduction unit are realized through hardware-level timestamps, useful electromyographic signals and nerve conduction signals can be more accurately separated and extracted, interference factors are eliminated, and doctors can more clearly analyze signal characteristics and accurately judge neuromuscular function states; and misjudgment caused by signal interference is avoided.

The embodiment of the invention provides a limb constant-speed motion control method and system based on surface myoelectricity triggering, and belongs to the technical field of constant-speed equipment. The method comprises the following steps: acquiring data of surface myoelectricity; calculating the strength of the electromyographic signals according to the surface electromyographic data to obtain analysis data of muscle activity; adjusting and driving the limbs according to the analysis data of the muscle activity and the data of the surface myoelectricity to obtain activity data; the training condition is analyzed according to the activity data, and limb constant-speed movement control information is obtained. Residual strength of muscles can be fully utilized to actively participate in exercise, and the requirements of users in different rehabilitation stages are met.

The invention discloses a movable muscle magnetic signal detection device, which comprises a magnetic shielding mechanism, an annular sensor array capable of scanning and moving, an arm positioning mechanism and a signal acquisition and processing system, and is characterized in that the magnetic shielding mechanism is used for shielding an environment magnetic field to form a near-zero magnetic measurement environment; the annular sensor array is used for surrounding an arm to collect multi-channel muscle magnetic signals generated by muscle movement, and the arm positioning mechanism is used for fixing the arm and determining a measurement position range. And the signal acquisition and processing system is used for synchronously acquiring a stimulation signal triggered by external stimulation, a multi-channel muscle magnetic signal from the annular sensor array and position information of the annular sensor array, and processing, tracing and positioning the multi-channel muscle magnetic signal. According to the device, miniaturization and mobility of muscle magnetic signal detection equipment are achieved, three-dimensional space distribution information of a muscle magnetic field can be obtained, the spatial resolution is remarkably improved, integrated analysis from signal detection to source positioning is achieved, and a reliable tool is provided for quantitative evaluation of neuromuscular functions.

The invention belongs to the technical field of correction intelligent control, and discloses an intelligent correction evaluation system and method for scoliosis, based on an intelligent multi-module sensor, a scoliosis intelligent correction system is built; based on a human body three-dimensional surface topography measurement technology, establishing an intelligent scoliosis evaluation system; based on an intelligent multi-module sensor and wearable ultrasonic image equipment, internal forms and activity characteristics of spine related muscles during spine correction are evaluated; and comprehensively analyzing a scoliosis correction mechanism by combining a scoliosis correction strategy, a scoliosis correction effect and related muscle activity characteristics of the spine, and determining an optimal biomechanical correction strategy. The external force is applied to the spine to control spine deformity, correction effects generated by different correction strategies are disclosed by analyzing a scoliosis correction mechanism, the conservative treatment effect of a scoliosis patient can be improved, and the device has great significance in improving the life quality of the patient, reducing the family economic burden and relieving the pressure of a medical system.

Method for visualizing multiple sensor signals derived via electrodes from a limb or amputation stump to detect muscle activity, comprising the following steps: • Each signal is assigned a display direction and a display amount • The display direction represents exactly one signal • The displayed value represents the intensity of motor activity of either the respective muscle or its nerve. • The direction and magnitude of all signals are simultaneously displayed in real time on a display device as a single graphical object. • On the display device, reference objects that were saved during previous analysis sessions are displayed simultaneously with the object. • Reference contractions are used as reference objects. • Simultaneously with the visualization, a prosthetic or orthotic device is controlled with the signals, whereby the reference objects are used to parameterize the prosthetic or orthotic control.

An assembly and a method is described. The assembly includes a platform, a motive source, a sensor, a controller and a computerized system. The computerized system has logic configured to cause a processor to send a signal to the controller to cause the motive source to move the platform through at least a portion of a range of inclination angles and to receive data indicating at least one of muscle activity, movement, and ground reaction force applied to the platform at a determined angle, and to cause the processor to analyze the data to determine an orthotic angle for a user's foot.

A muscle activity sensor includes a base textile, an electrode, and an interconnect. The base textile is configured to apply a compression force against a dermal surface of the user. The electrode is coupled to the base textile and includes a sensor layer including a conductive textile coupled to a dermal side of the base textile. The sensor layer is configured to receive electrical signals associated with muscle activity of the user. The electrode may also be configured to provide the electrical signals as an output signal. The interconnect may be coupled to the base textile over a distance from the electrode to an interconnect junction contact such that the interconnect moves with the base textile as the user moves. The interconnect may be further configured to deliver the output signal from the electrode to the interconnect junction contact.

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.

Systems and methods are provided for receiving input via an input device. A first signal is received via a muscle activity sensor. It is determined that the first signal corresponds to a movement of a user digit that does not correspond to a first interaction with an input element of the input device. A digit movement direction is identified, and a prediction of a second interaction with an input element is generated. A first confidence level associated with the prediction is below a threshold, and a second signal is received via a proximity sensor. A velocity of the user digit is determined, and a prediction of the second interaction with a first input element is made. It is identified that a second confidence level associated with the prediction is above the threshold, and a third signal associated with the first input element of the input device is generated for output.

Title of the invention: Monitoring of a patient's intercostal muscle activity during assisted ventilation using a patch sensor. The invention relates to a monitoring system (1) for the respiratory muscle activity of a patient (P), in particular intercostal and / or diaphragmatic muscle activity, comprising a patch sensor (10) including mechanomyography measurement means (13) for performing successive measurements of the patient's muscle activity, and a medical ventilator (20) with control means (25) for processing the measurements performed by the mechanomyography measurement means (13). Display means (30) display an MMG curve (31) representing the patient's muscle activity, in particular intercostal and / or diaphragmatic muscle activity, over time (t) obtained from the processed measurements. Abstract figure: Figure 1

In one embodiment of the present invention, first, muscle activity measurement data is acquired at a prescribed muscle site of a target user, and muscle synergy data at the muscle site of the target user is analyzed on the basis of the acquired muscle activity measurement data. Next, the optimal transportation distance between the muscle synergy data of the target user and reference muscle synergy data analyzed and stored in advance for each of a plurality of reference candidate users is calculated, a reference user having the optimal transportation distance that satisfies a preset condition is selected from among the plurality of reference candidate users, and a stimulus is presented to the muscle site of the target user on the basis of the muscle synergy data of the target user and the reference muscle synergy data of the selected reference user.

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.

The invention discloses a motion function rehabilitation training system for a forearm limb disabled patient based on visual and vibratory tactile collaborative feedback. The system comprises a hardware layer, a data processing layer, an application layer and a user interface layer. The training process is guided through visual feedback, the task completion condition and the gripping strength are reflected through vibration stimulation, the forearm amputation patient is helped to achieve integration of visual information and tactile information, and the accuracy of exercise decision making and the training effect are remarkably improved. Besides, on the basis of the electromyographic signal level of the patient, a personalized rehabilitation training scheme is adopted, and the pertinence and the individual adaptability of rehabilitation training are effectively enhanced by evaluating the muscle activity intensity level and the antagonism coefficient of the patient with the forearm limb disability and customizing the difficulty level of a digital large-synthesis electromyographic game and the training types of life scenes and tasks; functional recovery of the patient is promoted.

According to an example aspect of the present invention, there is provided a method for preparing a biomass-based and biodegradable sensor electrode, e.g. for use in a wearable sensor, the method comprising the steps of combining a natural wax with a vegetable oil in a weight ratio of 1:1 to 1:10 melting the wax with stirring to obtain a melted mixture, then gradually incorporating carbon particles at a weight ratio of 1-50% to the melted mixture, solidifying the melted mixture incorporating carbon particles on a biomass-based substrate into a solidified electrically conductive paste thereon. According to another example aspect of the present invention, there is provided a pressure sensor (1) obtainable by said method. The pressure sensor 1 comprises a porous xerogel dielectric material layer 4 sandwiched between two substrates 11 and 12, whereof each one comprises or consists of a layer of a substrate material 2 respectively 6 which are coated on one side with a layer of an electrically conductive paste 3 respectively 5. Copper tape 7 is affixed to the external surface of each substrate film 11 and 12 to establish electrical contacts.

A system for early detection of movement instability includes a wearable device affixed to a body part of a subject, featuring at least one electromyography (EMG) sensor for detecting electrical muscle activity and at least one motion sensor for detecting movement. A computing device, communicatively connected to the wearable device, receives EMG data from the EMG sensor and motion data from the motion sensor. The computing device identifies movement instability patterns using a machine-learning model trained on datasets of known stable and unstable muscle activity data and movement patterns. It determines impairment indicators based on the identified movement instability patterns and generates a user interface displaying these indicators. This system facilitates early detection and monitoring of movement instability.

The present invention relates to a smart dumbbell for measuring a user's grip strength and a smart device-based exercise amount measurement system including same. The exercise amount measurement system comprises: a smart muscle band which is mounted on a specific body part of a user, obtains an electromyogram signal, generated from muscles of the body part, by using an embedded electrode for electromyogram measurement, and calculates muscle fatigue and muscle activity of the muscles on the basis of the electromyogram signal; a smart dumbbell which generates a strain sensing signal through an embedded first strain sensor when gripped by the user, and inputs the strain sensing signal to a pre-trained neural network-based grip strength prediction model to generate grip strength data; and an evaluation system which stores the muscle fatigue, the muscle activity, and the grip strength data in an internal storage and monitors same.

A pediatric pelvic floor therapy system and method of operating a pelvic floor therapy system. The system may be designed for use by pediatric patients. The system includes non-invasive external electrodes, an electromyography (EMG) device, and a computer or mobile device that integrates gamification to engage users. The electrodes detect muscle activity, and the EMG device processes and transmits the signals wirelessly to the computer or mobile device. The received data is mapped to a gamified user interface that provides interactive game play based on the muscle activity of the user.

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.

The invention discloses a patient rehabilitation therapy real-time monitoring method and system, and relates to the technical field of rehabilitation medicine, and the method comprises the steps: collecting patient information, setting initial therapy parameters, and combining a real-time adaptability test and a feedback mechanism, and can dynamically adjust electrical stimulation therapy parameters, thereby providing personalized and accurate rehabilitation therapy, and improving the rehabilitation therapy efficiency. Electromyographic signals are collected in real time and surface electromyograms are generated by wearing the electrode equipment, detailed muscle activity information can be provided for doctors, the rehabilitation progress of patients can be effectively monitored, the recovery condition of the patients can be evaluated according to real-time data in the treatment process, and through cooperation of a mobile terminal APP and the electrode equipment, the recovery effect of the patients is improved. Personalized optimization of electrical stimulation parameters is achieved, the maximum comfort degree and the maximum curative effect in the treatment process are ensured, the individualized degree of treatment is improved, and excessive or insufficient stimulation is avoided.

A human-machine interface is configured for application to a skin area of an extremity, and may include a flexible layer to contact a surface of the skin area and includes magnetic or magnetizable elements, magnetic fields of which form a magnetic signature changing in response to deformation of the surface of the skin area, a detection device for measuring the magnetic signature, potential-measuring surface electrodes arranged in or on the flexible layer to directly contact the surface of the skin area, wherein measurement signals generated by the electrodes form an electric signature changing in dependence on muscle activity causing deformation of the surface of the skin area, and an integrated or external processing unit configured to map a magnetic signature and an associated electric signature to at least one of a configuration of the extremity, a temporal change in the configuration and a force of the temporal change.

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.

The invention relates to the technical field of biomedical engineering, and discloses an electromyographic signal simulation display method and system, and the method comprises the steps: carrying out the channel analysis and synchronous verification of a mixed signal sequence, and constructing a shunt signal which retains the time sequence correlation; extracting envelope features of the signals and performing trend fitting correction to generate initial intensity distribution; constructing an interference distribution diagram based on cross-correlation analysis, and determining an intensity correction value by using adaptive filtering and residual analysis; carrying out reverse reconstruction on the signal in combination with a tissue impedance model, and restoring real muscle activity distribution; and finally, mapping the distribution data to a visual model, and generating a muscle activity image through interpolation and sharpening processing. According to the method, the problem of low visual image accuracy in the prior art can be solved.

Provided is a muscle activity estimation device comprising a model generation unit and a muscle activity estimation unit. In the present invention, the model generation unit generates a decoding model for each muscle activity pattern and a decoding model for each muscle activity trend. The muscle activity estimation unit uses the decoding model for each muscle activity pattern and the decoding model for each muscle activity trend to calculate a muscle activity from the perspective of the muscle activity pattern and a muscle activity from the perspective of the muscle activity trend on the basis of a brain activity and estimates a muscle activity on the basis of the muscle activity from the perspective of the muscle activity pattern and the muscle activity from the perspective of the muscle activity trend.

The invention discloses a rehabilitation exercise posture monitoring and evaluating method and system, and relates to the technical field of exercise monitoring, and the method comprises the steps: obtaining motion feature data, including joint motion data, muscle motion data and spatial position data, corresponding to a target object in real time through a sensor module worn by the target object; performing space-time alignment on the joint movement data, the muscle movement data and the spatial position data; matching a corresponding action judgment rule according to the current rehabilitation stage of the target object; and calculating and outputting an abnormal action probability and an abnormal action type according to the joint movement data, the muscle movement data and the spatial position data based on the action judgment rule, and correspondingly generating movement posture evaluation data. The method can solve the problem that in the prior art, due to the fact that monitoring means are limited in the rehabilitation exercise process, scientific proceeding of rehabilitation exercise cannot be promoted.