146 results about "EMG - Electromyography" patented technology

Methods and systems are disclosed for collecting EMG speech signals using a speech signal detection device. The methods and systems collect a combination of signals comprising electromyograph (EMG) data signals and one or more non-EMG data signals. The methods and systems process the combination of signals by a machine learning (ML) model to detect inner speech of the user, the ML model trained to establish a relationship between training signals comprising training EMG data signals and training non-EMG data signals and ground-truth inner speech data and, in response, performing one or more operations associated with the speech signal detection device.

Methods and systems are disclosed for collecting EMG speech signals. The methods and systems present a target word for electromyograph (EMG) data collection on a graphical user interface (GUI) and receive input to initiate recording of EMG data. The methods and systems, in response to receiving the input, collect, by an EMG communication device, a set of EMG signals generated based on an individual user of the EMG communication device over a threshold period of time. The methods and systems determine whether the set of EMG signals collected over the threshold period of time corresponds to the target word and present feedback in the GUI based on whether the set of EMG signals collected over the threshold period of time corresponds to the target word.

The invention relates to a multi-mode human body respiration prediction method and system. The method comprises the steps that a difference value time sequence T is obtained through a visible light video and a thermal infrared video, and then the respiration frequency f1 of a target human body is obtained; collecting an electrophysiological signal containing a surface diaphragm electromyogram from the thoracic skin surface by means of an electromyographic signal collection system, and processing the electrophysiological signal to obtain a separated diaphragm electromyographic signal E; the heart rate f2 of the target human body is obtained through an electrocardiosignal collecting system; acquiring an environmental parameter Y of the target human body by using an environmental parameter acquisition system; the parameters obtained in the step S1-4 are processed, and a breathing state evaluation model is built; and S4, optimizing and constructing a BP neural network, and predicting the respiratory movement state of the human body by matching the sample data obtained in the step S5 with the BP neural network.

Methods and systems are disclosed for collecting electromyograph (EMG) speech signals using a speech signal detection device and calibrating the speech signal detection device using online learning. The system accesses a machine learning (ML) model that has been trained based on a collection of training data to detect presence of inner speech (silent speech or any other form of speech) and collects, by a speech signal detection device, a combination of signals comprising EMG data signals and one or more non-EMG data signals. The system processes the combination of signals by the ML model to predict presence of inner speech and updates the collection of training data based on the combination of signals and prediction made by the ML model. The system retrains the ML model in an online learning approach using the updated collection of training data.

A closed-loop neuromodulation system includes at least one implantable neurostimulator configured to deliver electrical stimulation to a target nerve innervating a muscle; at least one biosignal sensor configured to detect a physiological signal associated with the target muscle, wherein the physiological signal may include an electromyographic (EMG) or electroneurographic (ENG) signal; and a controller operatively coupled to the neurostimulator and the sensor. The controller is configured to receive the physiological signal from the sensor, determine whether the signal satisfies a predefined condition indicative of weakness, strength, fatigue, muscle underactivity, or overstimulation, and, in response to detecting that the condition is satisfied, automatically adjust one or more stimulation parameters selected from amplitude, frequency, pulse width, train duration, or train count.

An exemplary system and method are disclosed that employs (i) a forearm-based soft wearable hand-gesture recognition system that may detect a user's hand gestures as sensed from electromyographic signals acquired at a user's forearm and (ii) an AI-based classifier to continuously determine in real-time hand gestures as HMI inputs from the sensed EMG signal. The forearm-based soft wearable electronic system may be integrated into a soft, all-in-one wearable device having a scalable electrode array and integrated wireless system that may can measure electromyograms for real-time continuous recognition of hand gestures.

A computer-implemented method, system, and computer program product for predicting fatigue and injury risk for users, such as workers performing material handling operations, in real-time. Motions performed by a user, such as lifting, carrying, and manipulating objects, are captured in real-time, such as via camera-based optical devices, electromyography sensors, and inertia measurement units. Upon capturing motions performed by the user, such captured motions are utilized by a trained machine learning model to predict fatigue and injury risk to users. Such a prediction is made by the trained machine learning model by comparing the captured motions to predefined thresholds of range of motion constraints based on the biomechanical parameters of the user from a digital twin model of the user. Feedback may then be provided based on the predicted fatigue and injury risk of the user, such as in the form of video, audio, and / or haptic alerts.

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.

A computer-implemented method for detecting onset of a spontaneous breath by a patient coupled to a ventilation system includes receiving, at a processor, an electromyography (EMG) signal from an EMG sensor disposed on the patient. The method also includes pre-conditioning, via the processor, the EMG signal to separate the EMG signal into a plurality of components having EMG information utilizing a set of bandpass filters. The method further includes individually analyzing, via the processor, each component of the plurality of components to detect an onset of the spontaneous breath by the patient. The method still further includes determining, via the processor, the onset of the spontaneous breath by the patient is occurring when at least two components of the plurality of components indicate the onset of the spontaneous breath by the patient.

Methods and systems are disclosed for training a machine learning (ML) model to detect inner speech. The system collects, by an electromyograph (EMG) communication device used by a user, a first set of EMG signals over a first time interval. The system generates a first plurality of features based on the first set of EMG signals and generates a first probability associated with presence of inner speech by processing the first plurality of features with a machine learning (ML) model. The system compares the first probability generated by the ML model to a specified threshold and detects presence of the inner speech of the user in response to determining that the first probability generated by the ML model transgresses the specified threshold.

A tremor suppression device includes a garment wearable on an anatomical region and including electrodes contacting the anatomical region when the garment is worn on the anatomical region, and an electronic controller configured to: detect electromyography (EMG) signals as a function of anatomical location and time using the electrodes; identify tremors as a function of anatomical location and time based on the EMG signals; and apply neuromuscular electrical stimulation (NMES) at one or more anatomical locations as a function of time using the electrodes to suppress the identified tremors.

Methods and systems are disclosed for training a user-specific machine learning (ML) model to detect inner speech. The system accesses the ML model trained to detect inner speech based on a general population dataset. The system collects, by an electromyograph (EMG) communication device, a set of EMG signals generated based on an individual user of the EMG communication device. The system updates parameters of the ML model based on the set of EMG signals associated with the individual user. The system detects inner speech of the individual user by applying the ML model with the updated parameters to a new set of EMG signals received from the EMG communication device.

The invention discloses a gesture recognition system and method based on multi-channel mixing of EMG, FMG and IMU. The system comprises a multi-channel signal acquisition module, a signal conditioning and synchronous acquisition circuit, a data preprocessing module, a deep neural network fusion recognition module and an output interface module. By integrating a surface electromyogram, a muscle force diagram and an inertial measurement unit, synchronous acquisition of multi-modal physiological signals of the forearm of a human body is realized; a double-flow convolution-time sequence attention fusion network (DCTANet) is adopted for feature extraction and classification decision making, and neuromuscular activity, muscle mechanical response and limb movement information are effectively fused. The accuracy, robustness and real-time performance of gesture recognition are improved, and the method is suitable for the fields of intelligent prostheses, wearable interaction, rehabilitation training and the like. The system is compact in structure, supports embedded deployment, and has a good practical prospect.

Systems, processes and device configurations are provided for electronic game control with electromyography (EMG) sensing. Embodiments include processes for receiving EMG sensor output and detection of EMG user controls, such as user electrical activity in connection with movement of fingers and hands, and output of user EMG control signals for control of electronic game content. EMG signals may be detected by a one or more sensors on a wrist strap to detect user signals and secure user controllers. Embodiments include game controller configurations and systems for electronic game content presentation. Game controller configurations may include at least one of an interface for receiving EMG data and for powering EMG sensors. System configurations may include processing EMG signals and user activation signals of a game controller to reduce latency of generated game content. Machine learning models are described for training and use to identify user control signals from the EMG signal.

An intelligent interactive neuromodulation system by intravesical and pelvic floor stimulation is provided, including: an electromyography (EMG) acquisition module, an intelligent diagnostic module, a display interaction module, and an electrical stimulation module. The intelligent diagnostic module is configured to receive an original EMG signal acquired by the EMG acquisition module and obtain feature information, generate a first treatment protocol by using the feature information and a preconfigured intelligent fitting model, and generate a classification result of an abnormal bladder activity based on the feature information and a preconfigured intelligent classification model; in response to identifying that a detrusor activity of a patient changes, automatically complete dynamic switching between a first treatment mode and a second treatment mode. The display interaction module is configured to receive a first treatment protocol and a second treatment protocol, and send the first treatment protocol to the electrical stimulation module to apply electrical stimulation pulses.

An electromyography (EMG) measurement system includes a garment configured to be worn on an anatomical region, electrodes arranged on the garment to contact skin of the anatomical region when the garment is worn on the anatomical region, electronics connected with the electrodes to measure EMG data emanating from the anatomical region, and an electronic processor programmed to filter the EMG data to suppress or remove artifacts using filters computed using approximate joint diagonalization of covariance (AJDC) matrices or by transforming the EMG data to source signals using iteratively adjusted forward filters.

Methods and systems are disclosed for collecting EMG speech signals using a speech signal detection device. The methods and systems collect, by a speech signal detection device worn by a first person, a combination of signals comprising electromyograph (EMG) data signals and one or more non-EMG data signals. The methods and systems process the combination of signals by a machine learning (ML) model to detect presence of a second person, the ML model trained to establish a relationship between training signals comprising training EMG data signals and training non-EMG data signals and ground-truth presence of people data. The methods and systems control operation of the speech signal detection device being worn by the first person in response to detecting the presence of the second person.

A user interface system enables communication through inaudible speech by detecting physiological signals associated with inner voice communication. The system includes a wearable device positioned in, on, or around a user's ear, housing one or more sensor modalities such as electromyography (EMG) sensors for detecting muscle movements, functional near-infrared spectroscopy (fNIR) sensors for brain activity, and / or detection-and-ranging systems using sonar or radar for micro-deformations. Pre-processing modules condition the sensor data, which is then analyzed by one or more machine learning models to reconstruct the content of the user's inaudible communication and produce output representations. The system enables high-bandwidth natural language interaction without audible vocalization, maintaining privacy while supporting applications including AI assistant interaction, real-time translation, secure authentication, and inter-party communication.

Systems, apparatuses, and methods described herein facilitate the placement of spinal cord stimulator (SCS) electrode in relationship to neural tissue of the spinal cord based on electromyography (EMG) data. The systems, apparatuses, and methods are designed to assist physicians and surgeons target specific neurophysiological locations through stimulation and subsequent visualization of EMG activity in response to stimulation to more precisely identify the location of the SCS electrodes that will maximize the intended therapeutics effects of SCS.

An electromyography (EMG) measurement system includes a garment configured to be worn on an anatomical region, electrodes arranged on the garment to contact skin of the anatomical region when the garment is worn on the anatomical region, electronics connected with the electrodes to measure EMG data emanating from the anatomical region, and an electronic processor programmed to filter the EMG data to suppress or remove artifacts using filters computed using approximate joint diagonalization of covariance (AJDC) matrices or by transforming the EMG data to source signals using iteratively adjusted forward filters.

Methods and systems are disclosed for training a machine learning (ML) model to detect inner speech. The system collects, by an electromyograph (EMG) communication device used by a user, a first set of EMG signals over a first time interval. The system generates a first plurality of features based on the first set of EMG signals and generates a first probability associated with presence of inner speech by processing the first plurality of features with a machine learning (ML) model. The system compares the first probability generated by the ML model to a specified threshold and detects presence of the inner speech of the user in response to determining that the first probability generated by the ML model transgresses the specified threshold.

Systems, apparatuses, and methods described herein facilitate the placement of spinal cord stimulator (SCS) electrode in relationship to neural tissue of the spinal cord based on electromyography (EMG) data. The systems, apparatuses, and methods are designed to assist physicians and surgeons target specific neurophysiological locations through stimulation and subsequent visualization of EMG activity in response to stimulation to more precisely identify the location of the SCS electrodes that will maximize the intended therapeutics effects of SCS.

The present disclosure relates to an augmented reality input device and method based on an inertial measurement unit and electromyography, the augmented reality input device being configured to be connected through a network to an arm band worn on an arm of a user and an augmented reality module worn on a face of the user and providing virtual augmented reality, and the armlet is provided with an inertial measurement unit (IMU) sensor and an electromyogram (EMG) sensor, and the augmented reality input device is configured to arrange a virtual cursor on augmented reality, the virtual cursor moving corresponding to a change in angle and a change in position of a user's arm detected by the IMU sensor, and providing an input corresponding to the gesture of the user's arm recognized by the EMG sensor.

The present application relates to devices for monitoring pregnancy or labor. In one embodiment, the device includes an electromyography (EMG) sensor having two or more EMG electrodes that monitor fetal or maternal activity during pregnancy or labor, and one or more position sensors that monitor the relative positioning of the two or more EMG electrodes during fetal or maternal activity. In one embodiment, the device includes a monitoring apparatus that is placed on a body and has a plurality of sensors integrated into the monitoring apparatus, the plurality of sensors including at least a first sensor configured to detect a first type of signal from the body indicative of a first type of fetal or maternal activity during pregnancy or labor, and a second sensor configured to detect a second type of signal from the body different from the first type of signal also indicative of the first type of fetal or maternal activity during pregnancy or labor.

An electronic device and a method for determining the intensity of a low-frequency current are provided. The method includes: individually applying a corresponding first current to a body part of a user in N consecutive time intervals, wherein the time intervals include an i-th time interval to an (i+N)-th time interval; obtaining electromyography values of the body part in each time interval; determining a second current corresponding to an (i+N+1)-th time interval based on the first current corresponding to each time interval, the body part, personal information of the user, and the electromyography values of each time interval; and applying a second current to the body part of the user in the (i+N+1)-th time interval.

A bruxism training device is disclosed that is capable of detecting bruxism events (e.g., jaw clenching and teeth grinding) via external electromyography (EMG). The training device is shaped to be easily placed in a proper location and proper orientation over a masseter muscle of a user. A locator region is provided to facilitate easy locating of a reference electrode over the gonial angle of the mandible, while an alignment edge of the training device is provided to facilitate easy locating of end electrodes adjacent an end of the masseter muscle coupled to the mandible. With the locator region and alignment edge in place, an array of mid electrodes is automatically positioned over a bulk of the masseter muscle, allowing useful EMG measurement to be acquired of masseter activation.

The invention discloses a lumbar surgery AI dynamic simulation system and decision making method based on multi-modal data fusion, and belongs to the field of medical artificial intelligence, the technical scheme is that the system comprises a multi-modal data input interface, a dynamic modeling engine, a surgical decision making matrix and a three-dimensional comparison unit, and specifically, CT / MRI, electromyogram and gait mechanics data are fused; constructing a spinal canal 4D model through improved U-Net < 3 + >, and predicting a nerve root dynamic path by means of GCN-LSTM; and in combination with a lumbar stenosis type, matching an adaptive operation form, and generating a pre-operation and post-operation quantitative comparison report marked by three colors. A spinal canal-nerve-biomechanics dynamic correlation model is innovatively established, 217 L4-S1 segment anatomical variation templates are arranged in the model, and the anatomical variation adaptation rate reaches 92.3%. Clinical test results show that the operation time planned by the system is averagely shortened by 26 minutes, and the postoperative reoperation rate is reduced to 2.4%.

Therapeutic dressings for wounds are provided, as well as methods of using the same and methods of fabricating the same. A therapeutic dressing can include a central non-adherent portion, an adherent portion surrounding the central non-adherent portion, a sensor, at least one stimulating electrode, and a circuit in operable communication with the sensor and the at least one stimulating electrode. The electrode(s) can deliver an electrical stimulus to one or more nerves through the skin of a patient / user. The sensor can be, for example, an electromyography (EMG) sensor, and the therapeutic dressing can be configured such that the sensor and the electrode(s) are in direct physical contact with the skin during use.

The invention is suitable for the technical field of deep learning, and provides a multi-attention-based hand electromyogram continuous motion estimation method, which comprises the following steps: carrying out preprocessing and feature extraction processing on a surface electromyogram signal, and inputting the processed surface electromyogram signal into a motion estimation model; feature extraction and dimension improvement are carried out through a depth separable convolution module, and the weight of each channel is adaptively adjusted through efficient channel attention; further capturing a global dependency relationship by using additive attention and a feed-forward network through a Transform encoder module; feature compression is carried out through a one-dimensional adaptive average pooling layer, each channel obtains respective average value, and global information is extracted; and inputting the global information into a single-layer MLP layer for nonlinear mapping, and outputting a joint angle. According to the method, depth separable convolution is used, so that the calculation amount and the parameter scale are reduced; and an efficient channel attention module is introduced, so that the feature expression capability is improved while the relatively high calculation efficiency is maintained.

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.