636 results about "Biosignal" patented technology

An apparatus for measuring a bio signal including a bio signal measurement unit, which is insertable into an ear to be in close contact with an internal surface of the ear, the bio signal measurement unit having a photo plethysmography (PPG) measurement module for radiating light of different wavelengths onto the internal surface of the ear, detecting light transmitted through the ear, and outputting a PPG signal including bio information, a control unit having a PPG signal processor for generating the bio information using the PPG signal measured by the PPG measurement module, and an output unit for displaying the bio information generated from the control unit.

Specific ionic interactions with a sensing material that is electrically coupled with the floating gate of a floating gate-based ion sensitive field effect transistor (FGISFET) may be used to sense a target material. For example, an FGISFET can use (e.g., previously demonstrated) ionic interaction-based sensing techniques with the floating gate of floating gate field effect transistors. The floating gate can serves as a probe and an interface to convert chemical and/or biological signals to electrical signals, which can be measured by monitoring the change in the device's threshold voltage, V_T.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.

Disclosed is an apparatus and method for inputting keys using biological signals in an HMD (Head Mounted Display) mobile information terminal. The apparatus provides a virtual screen that includes a key map and a preview window to a user through a display unit having a micro-display, recognizes and inputs a key selected according to the user's biological signals sensed through a biological signal sensing unit having an EOG (Electrooculogram) input unit and an EMG (Electromyogram) input unit for sensing and receiving the biological signals as key inputs. The apparatus recognizes through a recognition unit the key selected according to the user's biological signals sensed through a biological signal sensing unit. The user can freely use the HMD mobile communication terminal without using his/her hands because the user can input his/her desired key to the HMD information terminal only by the movement of the user's eyes and the biting of his/her right and left back teeth.

A method, medium, and apparatus extracting avatar parameters using an avatar transformation algorithm from health state information measured from a user and generating an avatar using the extracted parameters. In the apparatus, a state analysis unit receives at least one among a bio-signal and questionary information of a user and outputs health state information, a parameter extraction unit extracts parameters defining an avatar image, and an avatar generation unit generates the avatar image using the parameters. By providing the method, medium, and apparatus, it is possible to provide a user with a convenience in recognizing his health state by generating an avatar image describing the user's future health state as well as the user's current health state. Further, it is possible to induce a user to manage his health constantly by maintaining a user's with the avatar image change.

The present invention is a biometric security system and method operable to authenticate one or more individuals using physiological signals. The method and system may comprise one of the following modes: instantaneous identity recognition (MR); or continuous identity recognition (CIR). The present invention may include a methodology and framework for biometric recognition using physiological signals and may utilize a machine learning utility. The machine learning utility may be presented and adapted to the needs of different application environments which constitute different application frameworks. The present invention may further incorporate a method and system for continuous authentication using physiological signals and a means of estimating relevant parameters.

A biosignal measurement apparatus including: a headset; a member being detachable from the headset, and being attached onto an ear of a user; a PPG sensor being attached onto the member to detect a PPG signal from the ear of the user; and an acceleration sensor being attached onto the member to detect an acceleration signal due to a motion of the user from the ear of the user is provided.

A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of de-noising a data set comprised of a new set of frames. The method separately performs a non-linear de-noising operation on each of the component frames and combines the resultant de-noised frames to form a combined resultant de-noised input signal. The method is preferably carried out in a digital processor.

A computer system for guiding one or more users through a brain state guidance exercise or routine, such as a meditation exercise, is provided. The computer system includes at least one computing device which may be a smart phone. A computer program which may be a mobile application runs one or more brain state guidance routines that guide at least one user through at least one brain state guidance exercise. The computing device is connected to at least one bio-signal sensor that provides biofeedback information to the computing device, and where the computer program when executed further measures performance of the at least one user relative to one or more brain state guidance related objectives by analyzing the biofeedback information based on stability of state of mind for the user. The computer program may recognize, score and reward states of meditation.

A method and apparatus for de-noising weak bio-signals having a relatively low signal to noise ratio utilizes an iterative process of wavelet de-noising a data set comprised of a new set of frames of wavelet coefficients partially generated through a cyclic shift algorithm. The method preferably operates on a data set having 2^N frames, and the iteration is performed N−1 times. The resultant wavelet coefficients are then linearly averaged and an inverse discrete wavelet transform is performed to arrive at the de-noised original signal. The method is preferably carried out in a digital processor.

The disclosure provides a method, an electronic apparatus, and a computer readable medium of constructing a classifier for disease detection. The method includes the following steps. A codebook of representative features is constructed based on a plurality of disease-irrelevant data. Transfer-learned disease features are extracted from disease-relevant bio-signals according to the codebook without any medical domain knowledge, where both the disease-irrelevant data and the disease-relevant bio-signals are time-series data. Supervised learning is performed based on the transfer-learned disease features to train the classifier for disease detection.

[Problem to be Solved]

The problem to be solved by the present invention is to reduce the load applied to the wearer by correcting a parameter in correspondence with detectivity of biosignals.

[Means to Solve Problem]

The calibration controlling part 162 of the movement assisting apparatus 10 enables the power amplifying part 158 to apply a driving force of the driving source 140 as a load (input torque) from the load generating part 164 to the wearer 12 when the wearer 12 wears the movement assisting wearing device. Then, the wearer 12 applied with the driving force of the driving source 140 generates power from the skeletal muscles by performing a predetermined calibration operation. Accordingly, the physical phenomenon detecting part 142 detects joint angle along with the calibration operation, and the biosignal detecting part 144 detects myoelectric signals. In the parameter correction part 156, a parameter K is corrected based on the difference between the load (input torque) and the driving force (muscular strength) being calculated by the difference deriving part 154 with respect to the phase identified by the phase identifying part 152.

A wearable biofeedback device is provided. The device preferably includes a glove structure and external display device. The glove has at least one sheath for receiving a digit. The glove and sheath include dorsal and palmer surfaces. The palmer surfaces include at least one sensor for acquiring a bio-signal. The dorsal surface includes a compartment for containing an electronics control module. The electronics module is contained in the compartment, and is in communication with the sensor. A circuit system, accommodated in the electronics module, includes an analog to digital converter, a processor for performing an analysis and translation of the digitized bio-signals into a biometric measurement data, a battery, and a wirelessly transmission module for performing wirelessly transmission to the external device. The external device is wirelessly communicated with the glove through the wireless transmission module, and is adapted to display the biometric measurement data in an audio or visual display.

In a training control method using biofeedback, and an apparatus using the method, the method includes setting a target exercise zone for a user based on a bio-signal from the user, the target exercise zone including a range of indices and comparing a current index obtained from the bio-signal with the target exercise zone in time units in an exercise training mode and providing at least one of positive biofeedback information, negative biofeedback information, and a warning message to the user according to a result of the comparison.

A biological information monitoring apparatus operable to measure biological information of a patient by detecting a biological signal of the patient, and operable to output an alarm, includes an alarm controller which changes the alarm. The alarm includes a first alarm of a first priority and a second alarm of a second priority. When a first condition is satisfied, the first alarm is output. When a second condition is satisfied after the first alarm is output, the alarm controller changes from the first alarm to the second alarm to be output.

Ambulatory monitoring of human health is provided by a multi-component multi-sensor wireless wearable biosignal acquisition system comprising a torso device and a peripheral device communicating wirelessly, and a mobile phone for receiving collected data and uploading it over cellular network or WiFi to a remote computer for multivariate analysis. Biosignals include EKG and PPG, from which a determination of pulse transit time can be made.

A wearable biometric monitoring device is configured to assess the biometric signal quality of one or more sensors associated with the monitoring device, determine how the user should adjust the device to improve the biometric fit, and instruct the user to wear the biometric monitoring device a certain way. Communicating instructions to a user may include instructing the user to execute a testing regimen while wearing the biometric monitoring device. The testing regimen facilitates an estimation of a signal quality that can be used to provide feedback to the user that he/she needs to adjust the device to improve the biometric fit and the biometric signal quality.

A wireless modular, multi-modal, multi-node patch platform is described. The platform preferably comprises low-cost semi-disposable patch design aiming at unobtrusive ambulatory monitoring of multiple physiological parameters. Owing to its modular design it can be interfaced with various low-power RF communication and data storage technologies, while the data fusion of multi-modal and multi-node features facilitates measurement of several bio-signals from multiple on-body locations for robust feature extraction. Exemplary results of the patch platform are presented which illustrate the capability to extract respiration rate from three different independent metrics, which combined together can give a more robust estimate of the actual respiratory rate.

A biosignal measurement module is provided and includes a biosignal measurement unit, a pose detection unit, and a processing unit. The biosignal measurement unit measures an electrocardiogram signal and a pulse signal of a subject. The pose detection unit detects a position of the biosignal measurement module and outputs position signals. The processing unit receives the electrocardiogram signal, the pulse signal, and the position signals. The processing unit generates a height variation parameter, which indicates the height difference between the position of the biosignal measurement module and a reference position, according to the position signals. The processing unit calculates a current pulse transit time according to the electrocardiogram signal and the pulse signal and compensates for the current pulse transit time according to the height variation parameter to obtain a compensated pulse transit time. The processing unit obtains a blood pressure signal according to the compensated pulse transit time.

A wearable electromyogram sensor system is provided. The wearable electromyogram sensor system includes: an elastic band having a plurality of electrodes; an electromyogram sensor including an electrode connected to an electrode of the band so as to receive a bio-signal related to contraction of a muscle, and configured to sense a change of motion information through the bio-signal or previously sense the change of the motion information before the motion information is changed; and a fixing unit fixing the electromyogram sensor to the band. The electrode of the electromyogram sensor is connected to an electrode at an arbitrary position of the band.

The invention discloses an automatic diagnosis method for electrocardiographic abnormality and relates to the technical field of automatic diagnosis of electrocardiographic abnormality. The learning capacity of an RNN neural network to a time sequence and the learning capability of CNN to spatial features are combined to learn features of the electrocardiographic biological signal, and abnormal electrocardiographs of different types are automatically characterized. Then, a deep neural network based classifier is constructed, which is trained using an electrocardiograph with type annotations to improve the accuracy of classification, and automatic classification of different arrhythmia types is achieved. According to the invention, the process of manually extracting features is avoided; then, time sequence features and spatial features of the electrocardiographs are learned using RNN and CNN to form the classifier, and the classifier is trained through supervised learning so that the classifier can automatically diagnose abnormal electrocardiographs. As a result, the accuracy of automatic diagnosis and classification of electrocardiographic abnormality is improved.

A bio-monitor is built into a telephone handset or cell phone. Sensors are configured to obtain bio-signals while the handset or cell phone is in the position for normal speaking use of the telephonic device. This enables biosignal acquisition and/or bio-signal telephonic transmission to occur without the need for a position change to effect voice communications. The invention can also be constructed in the form of a case or harness designed to fit over a preexisting cell phone or a pre-existing telephone handset.

Systems and methods define an index of risk for cardiac disease by detecting cellular derangements that may lead to cardiomyopathy, heart rhythm disorders or ischemic heart disease. The markers include fluctuations or abnormal rate-behavior of electrical, mechanical or other measurable biosignals. The invention operates in modes that can be applied to prevent atrial fibrillation or the risk for ventricular arrhythmias. Alternative embodiments are applied to tissue outside the heart such as skeletal muscle, smooth muscle, the central nervous system, the respiratory system, the urogenital system and the gastrointestinal system.

A method and system for providing and aggregating bioelectrical signal data comprising: providing a stimulus configured to prompt an action in both a first user and a second user; at a first biosignal detector and a second biosignal detector, automatically collecting a first bioelectrical signal dataset from the first user as the first user performs the action and a second bioelectrical signal dataset from the second user as the second user performs the action; generating a first anonymized bioelectrical signal dataset from the first bioelectrical signal dataset and a second anonymized bioelectrical signal dataset from the second bioelectrical signal dataset; coupling the first and the second anonymized bioelectrical signal datasets with an action tag characterizing the action; and generating an analysis based upon the first the second anonymized bioelectrical signal datasets. An embodiment of the system comprises a biosignal detector and a processor configured to implement an embodiment of the method.

Pressure sensors are sorted out from the other sensors based on a signal from each sensor element. Sensor outputs of the pressure sensors that have been sorted out are filtered using an FIR filter through which sensor outputs of the other sensors are eliminated. Frequency analysis is performed on the filtered sensor outputs using FFT. A reference sensor is chosen from power spectra of the filtered pressure sensors. Phase differences are calculated between a sensor output of the reference sensor and the sensor outputs of the other pressure sensors. Based on the phase differences, pressure sensors other than the reference sensor are sorted into those with large phase differences and those with small phase differences. Phases of sensor signals of those with large phase differences are reversed, and their sensor outputs are added together. For those with small phase differences, their sensor outputs are added together without reversing the phases.

A computer network implemented system for improving the operation of one or more biofeedback computer systems is provided. The system includes an intelligent bio-signal processing system that is operable to: capture bio-signal data and in addition optionally non-bio-signal data; and analyze the bio-signal data and non-bio-signal data, if any, so as to: extract one or more features related to at least one individual interacting with the biofeedback computer system; classify the individual based on the features by establishing one or more brain wave interaction profiles for the individual for improving the interaction of the individual with the one or more biofeedback computer systems, and initiate the storage of the brain waive interaction profiles to a database; and access one or more machine learning components or processes for further improving the interaction of the individual with the one or more biofeedback computer systems by updating automatically the brain wave interaction profiles based on detecting one or more defined interactions between the individual and the one or more of the biofeedback computer systems. A number of additional system and computer implemented method features are also provided.