88 results about "EMG - Electromyography" patented technology

A “Wearable Electromyography-Based Controller” includes a plurality of Electromyography (EMG) sensors and provides a wired or wireless human-computer interface (HCl) for interacting with computing systems and attached devices via electrical signals generated by specific movement of the user's muscles. Following initial automated self-calibration and positional localization processes, measurement and interpretation of muscle generated electrical signals is accomplished by sampling signals from the EMG sensors of the Wearable Electromyography-Based Controller. In operation, the Wearable Electromyography-Based Controller is donned by the user and placed into a coarsely approximate position on the surface of the user's skin. Automated cues or instructions are then provided to the user for fine-tuning placement of the Wearable Electromyography-Based Controller. Examples of Wearable Electromyography-Based Controllers include articles of manufacture, such as an armband, wristwatch, or article of clothing having a plurality of integrated EMG-based sensor nodes and associated electronics.

Systems, articles, and methods perform gesture identification with limited computational resources. A wearable electromyography (“EMG”) device includes multiple EMG sensors, an on-board processor, and a non-transitory processor-readable memory that stores data and/or processor-executable instructions for performing gesture identification. The wearable EMG device detects and determines features of signals when a user performs a physical gesture, and processes the features by performing a decision tree analysis. The decision tree analysis invokes a decision tree stored in the memory, where storing and executing the decision tree may be managed by limited computational resources. The outcome of the decision tree analysis is a probability vector that assigns a respective probability score to each gesture in a gesture library. The accuracy of the gesture identification may be enhanced by performing multiple iterations of the decision tree analysis across multiple time windows of the EMG signal data and combining the resulting probability vectors.

Systems, articles, and methods for performing gesture identification with improved robustness against variations in use parameters and without requiring a user to undergo an extensive training procedure are described. A wearable electromyography (“EMG”) device includes multiple EMG sensors, an on-board processor, and a non-transitory processor-readable storage medium that stores data and/or processor-executable instructions for performing gesture identification. The wearable EMG device detects, determines, and ranks features in the signal data provided by the EMG sensors and generates a digit string based on the ranked features. The permutation of the digit string is indicative of the gesture performed by the user, which is identified by testing the permutation of the digit string against multiple sets of defined permutation conditions. A single reference gesture may be performed by the user to (re-)calibrate the wearable EMG device before and/or during use.

Systems, articles, and methods perform gesture identification with limited computational resources. A wearable electromyography (“EMG”) device includes multiple EMG sensors, an on-board processor, and a non-transitory processor-readable memory storing data and/or instructions for performing gesture identification. The wearable EMG device detects signals when a user performs a physical gesture and characterizes a signal vector {right arrow over (s)} based on features of the detected signals. A library of gesture template vectors G is stored in the memory of the wearable EMG device and a respective property of each respective angle θ_i formed between the signal vector {right arrow over (s)} and respective ones of the gesture template vectors {right arrow over (g)}_i is analyzed to match the direction of the signal vector {right arrow over (s)} to the direction of a particular gesture template vector {right arrow over (g)}*. The accuracy of the gesture identification may be enhanced by performing multiple iterations across multiple time-synchronized portions of the EMG signal data.

Human-electronics interfaces in which a wearable electromyography (“EMG”) device is operated to control virtually any electronic device are described. In response to detected muscle activity and/or motions of a user, the wearable EMG device transmits generic gesture identification flags that are not specific to the particular electronic device(s) being controlled. An electronic device being controlled is programmed with user-definable instructions for how to interpret and respond to the gesture identification flags.

An endoscopic pedicle probe for use during spinal surgery to form a hole in a pedicle for reception of a pedicle screw has an enlarged proximal end for cooperation with the hand of the surgeon and an elongate shaft terminating in a distal tip that may be pushed through the pedicle to form the hole. An integrated endoscope and light extend through the shaft to enable the surgeon to visually observe the target area, and a conduit extends through the shaft to convey a fluid to irrigate the target area. In a preferred form the probe is connected with an electromyographic or mechanomyographic monitoring system to alert the surgeon when a breach is about to occur. In a further embodiment, two endoscopes are associated with the probe. The complete probe may be disposable, or just the tip may be detachable for disposal or replacement.

Human-electronics interfaces in which at least two wearable electromyography (“EMG”) devices are operated to control virtually any electronic device are described. A first wearable EMG device is worn on a first part/location of a user's body and a second wearable EMG device is worn on a second part/location of the user's body. Muscle activity is detected by the two wearable EMG devices and corresponding communication signals are transmitted to an electronic device to control functions thereof. The two wearable EMG devices may communicate with one another. This configuration enables a user to perform elaborate gestures having multiple components (e.g., “two-arm” gestures) with each wearable EMG device detecting a different component, as well as separate gestures (e.g., separate “one-arm” gestures) individually detected and processed by each wearable EMG device.

A method and apparatus for measuring the biological condition of an animal by acquiring and analyzing its biological signals are provided. The biological signals from skin temperature, a photoplenthysmogram (PPG), an electrocardiogram (ECG), electrodermal activity (EDA), an electromyogram (EMG), and an electrogastrogram (EGG) are detected using a biological signal detection unit which is attached to the animal's skin. Feature vectors, including the mean heart rate of the photoplenthysmogram and its standard deviation, the very low frequency, low frequency, and high frequency components of heart rate variability, the frequency and mean amplitude of skin conductance responses, and the mean and maximum skin temperatures, are extracted from the detected biological signals. The biological condition, including needs and emotions, of the animal as to whether or not the animal feels hunger or fear, how much the animal is stressed, or whether or not the animal needs to have a bowel movement, is determined using a pattern classifier which has learned reference vectors, which reflect the behaviors, needs, and emotions of different kinds of animals for various biological conditions and are stored in a predetermined database. Therefore, the biological condition of the animal can be determined through instrumental communication, not through human languages, and the breeding of pets can be efficiently managed.

Motion analysis is used to classify or rate human capability in a physical domain via a minimized movement and data collection protocol producing a discreet, overall figure of merit of the selected physical capability. The minimal protocol is determined by data mining of a more extensive movement and data collection. Protocols are relevant in medical, sports and occupational applications. Kinematic, kinetic, body type, electromyography (EMG), ground Reactive Force (GRF), demographic, and psychological data are encompassed. Resulting protocols are capable of transforming raw data representing specific human motions into an objective rating of a skill or capability related to those motions.

An apparatus and method for selecting and outputting characters by making a teeth clenching motion for disabled person are disclosed. The apparatus includes: an electromyogram signal obtaining unit including electromyogram sensors disposed at both sides for generating an electromyogram signal according to a predetermined muscle activated when a disabled person clenches teeth, and a ground electrode connected to a body of the disabled persons for providing a reference voltage; and an electromyogram signal processing unit for outputting a character by identifying a teeth clenching motion pattern of each block according to a side of teeth clenched, a duration of clenching teeth and consecutive clenching motions through analyzing the obtained electromyogram and selecting characters according to the identified teeth clenching motion pattern.

The present invention provides a light-weight wearable sensor that can capture electroencephalogram (EEG or brain signals), electromyography (EMG or muscle signal), and electrooculography (EOG or eye movement signal) using a pair of modified off-the-shelf earplugs. The present invention further provides a supervised non-negative matrix factorization learning algorithm to analyze and extract these signals from the mixed signal collected by the sensor. The present invention further provides an autonomous and whole-night sleep staging system utilizing the sensor's outputs.

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.

A sub-vocal speech recognition (SVSR) apparatus includes a headset that is worn over an ear and electromyography (EMG) electrodes and an Inertial Measurement Unit (IMU) in contact with a user's skin in a position over the neck, under the chin and behind the ear. When a user speaks or mouths words, the EMG and IMU signals are recorded by sensors and amplified and filtered, before being divided in multi-millisecond time windows. These time windows are then transmitted to the interface computing device for Mel Frequency Cepstral Coefficients (MFCC) conversion into aggregated vector representation (AVR). The AVR is the input to the SVSR system, which utilizes a neural network, CTC function, and language model to classify the phoneme. The phonemes are then combined into words and sent back to the interface computing device, where they are played either as audible output, such as from a speaker, or non-audible output, such as text.

A therapeutic or diagnostic device comprises a wearable electrodes garment including electrodes disposed to contact skin when the wearable electrodes garment is worn, and an electronic controller operatively connected with the electrodes. The electronic controller is programmed to perform a method including: receiving surface electromyography (EMG) signals via the electrodes and extracting one or more motor unit (MU) action potentials from the surface EMG signals. The method may further include identifying an intended movement based at least on features representing the one or more extracted MU action potentials and delivering functional electrical stimulation (FES) effective to implement the intended movement via the electrodes of the wearable electrodes garment. The method may further include generating a patient performance report based at least on a comparison of features representing the one or more extracted MU action potentials and features representing expected and/or baseline MU action potentials for a known intended movement.

In one embodiment, prior to undergoing surgery, a patient is trained in the use of an anesthesia-awareness detection (AAD) system to produce an observable output by attempting to move a muscle. The AAD system includes an electromyographic (EMG) signal monitor. While undergoing surgery, the patient is anesthetized with a sedative and a paralytic. During surgery, if the surgical team wishes to asses the consciousness state or communicative ability of the patient, then the surgical team asks the patient to move the muscle. If the patient is unable to speak due to the paralytic but is aware and attempts to comply, then the attempt is detected by the AAD system, which provides a corresponding output to inform the surgical team, which may take appropriate action to ask additional questions of the patient and/or address the patient's anesthesia awareness.

The technology provides a system and method for simulating and detecting bio signals such as brain bio-signals. The technology can be used for medical or non-medical purposes, for instance to simulate or evaluate certain medical conditions using a physical brain-type phantom body. A set of optical fibers provides modulated signals received from an optical signal modulator, which is managed by a controller to generate repeatable signals with high fidelity. The modulated signals are received by a set of emission elements such as photoreceivers or other optical electrodes disposed within or otherwise about the phantom body. The emission elements output electrical signals corresponding to the input modulated optical signals. The electrical signals are detected by a set of sensors. The sensors are coupled to a receiver device that is able to evaluate the electrical signals, such as for an electroencephalograph (EEG), electrocardiogram (ECG), electromyogram (EMG) or magnetoencephalography (MEG) diagnostic system.

A method, human interface device, and computer program product that provide improved multilevel switching from each bioelectrical sensor with inclusion of switch filtering based on extraneous events (e.g., spasms). A biosignal is received from a sensor device by an electronic processor of a first electrode switch device. In response to determining that the amplitude of the signal has changed from less than a first switch range to greater than the first switch range and less than the second switch range, the electrode switch device communicates a first switch signal to control the human interface system. In response to determining that the amplitude of the biosignal has changed from less than the second switch range to greater than the second switch range, the electronic switch device performs one of: (i) ignoring the instance and (ii) transmitting a second switch signal to control the human interface system.

Devices, systems, and methods herein relate to electromyography (EMG) that may be used in diagnostic and/or therapeutic applications, including but not limited to electrophysiological study of musclesin the body relating to neuromuscular function and/or disorders. Sensor assemblies and methods are described herein for non-invasively generating an EMG signal corresponding to muscle tissue where the sensor may be positioned directly on a surface of the muscle tissue including any associated membrane (e.g., mucosal, endothelial, synovial) overlying the muscle tissue. A sensor assembly may include one or more pairs of closely spaced, atraumatic electrodes in a bipolar or multipolar configuration. The first and second electrodes may be applied against a surface of muscle tissue (that may include a membrane overlying the muscle) and receive electrical activity signal data corresponding to an electrical potential difference of the portion of muscle between the electrodes.

Devices, systems, and methods for monitoring respiration using surface temperature, humidity, air pressure, carbon dioxide gas sensors, pulse oximetry sensors and electromyography sensors, and/or acceleration sensors to obtain information related to respiration rate (RR), exhalation/inhalation strength, exhalation/inhalation volume, exhalation/inhalation acceleration, and/or exhalation/inhalation regularity.

Systems and methods are described for the coaching of users through successful calibration of a myoelectric prosthetic controller. The systems and methods are comprised of, and/or utilize, hardware and software components to input and analyze electromyography (EMG) based signals in association with movements, and to calibrate and output feedback about the signals. The hardware is further comprised of an apparatus for the detection of EMG signals, a prosthesis, an indicator, and a user interface. The software is further comprised of a user interface, a pattern recognition component, a calibration procedure, and a feedback mechanism. The systems and methods facilitate calibration of a myoelectric controller and provides the user with feedback about the calibration including information of the signal inputs and outputs, and messages about connected hardware and how to optimize signal data.

The invention is applicable to the field of improvement of positioning technology, and provides an indoor and outdoor integrated positioning method based on electromyographic data. The method comprises the steps of enabling an experimenter to move in a specific motion path range, acquiring electromyographic signals through an electromyography sensor, and calculating number of walking steps; according to the data of the electromyography graph, performing processing of the electromyographic signals through a series of signal processing methods, calculating the number of walking steps, and classifying the different motion states; establishing an empirical model for different motion gaits obtained by classification, and calculating the step size in each motion state; and on the other hand, obtaining the gait data through integration of the motion starting point provided by a GPS and the electromyographic signal, and obtaining the orientation data through an electronic compass, so that thecorresponding points of pedestrian positions in the indoor environment, from the outdoor to the indoor, and in the indoor environment. Based on the electromyographic information, simple calculation iscarried out, so that the positioning accuracy is effectively improved.