Patent application number | Description | Published |
20130120147 | FALL DETECTION USING SENSOR FUSION - A method and system for fall detection using sensor fusion are disclosed. In a first aspect, the method comprises in response to any of first and second acceleration magnitude thresholds being satisfied, determining whether a height difference before and after impact of a fall satisfies a threshold and whether an angle threshold between an acceleration vector and a calibration vector is satisfied. In a second aspect, the system comprises a processing system and an application coupled to the processing system, wherein the application carries out the steps of the method. | 05-16-2013 |
20130120152 | METHOD AND SYSTEM FOR FALL DETECTION OF A USER - A method, system, and computer-readable medium for fall detection of a user are disclosed. In a first aspect, the method comprises determining whether first or second magnitude thresholds are satisfied. If the first or second magnitude thresholds are satisfied, the method includes determining whether an acceleration vector of the user is at a predetermined angle to a calibration vector. In a second aspect, the system comprises a processing system and an application that is executed by the processing system. The application determines whether first or second magnitude thresholds are satisfied. If the first or second magnitude thresholds are satisfied, the application determines whether an acceleration vector of the user is at a predetermined angle to a calibration vector. | 05-16-2013 |
20130136159 | INTERFERENCE-COGNITIVE TRANSMISSION - Interference cognitive devices are described. An interference cognitive device can be collocated with a transmitter of an interference cognitive transmitter (ICT), as receive chains or portions thereof at the ICT. An interference cognitive device can also be remote with respect to the transmitter, which operates in an interference cognitive network and receives data directly or indirectly from the interference cognitive device. The ICT uses the data to mitigate interference while continuing to operate in accordance with a performance metric. | 05-30-2013 |
20130281875 | DETERMINING RESPIRATORY RATE VIA IMPEDANCE PNEUMOGRAPHY - A method and system for determining a respiratory rate of a user are disclosed. The method comprises measuring a differential voltage across first and second electrodes of a sensor device coupled to the user. The method includes sampling the differential voltage using an analog-to-digital converter to produce an output signal. The method includes processing the output signal to detect a breath of the user based on a positive voltage transition through a midpoint, wherein the breath of the user is utilized to determine the respiratory rate of the user. | 10-24-2013 |
20140015687 | POSTURE CALIBRATION FOR ACTIVITY MONITORING - A method and system for activity monitoring of a user are disclosed. In a first aspect, the method comprises calibrating posture by the user to determine a calibration vector. The method includes validating the calibration vector by comparing an anteroposterior axis to a threshold, wherein activity of the user is monitored using the validated calibration vector. In a second aspect, a wireless sensor device comprises a processor and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to receive a posture calibration request from the user and to determine a calibration vector based on the received request. The application, when executed by the processor, further causes the processor to validate the calibration vector by comparing an anteroposterior axis to a threshold, wherein activity of the user is monitored using the validated calibration vector. | 01-16-2014 |
20140019080 | CALIBRATION OF A CHEST-MOUNTED WIRELESS SENSOR DEVICE FOR POSTURE AND ACTIVITY DETECTION - A method and system for calibrating a wireless sensor device are disclosed. In a first aspect, the method comprises determining a vertical calibration vector and determining a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes. In a second aspect, a wireless sensor device comprises a processor and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to determine a vertical calibration vector and to determine a rotation matrix using the vertical calibration vector to line up native axes of the wireless sensor device with body axes. | 01-16-2014 |
20140066795 | CONTINUOUS ASSESMENT OF ECG SIGNAL QUALITY - A method and system for assessing an electrocardiogram (ECG) signal quality are disclosed. In a first aspect, the method comprises determining a Kurtosis calculation of the ECG signal and determining whether the Kurtosis calculation satisfies a first threshold to continuously assess the ECG signal quality. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to determine a Kurtosis calculation of the ECG signal and to determine whether the Kurtosis calculation satisfies a first threshold to continuously assess the ECG signal quality. | 03-06-2014 |
20140073982 | R-R INTERVAL MEASUREMENT USING MULTI-RATE ECG PROCESSING - A method and system for R-R interval measurement of a user are disclosed. In a first aspect, the method comprises detecting an electrocardiogram (ECG) signal of the user. The method includes performing QRS peak detection on the ECG signal to obtain a low resolution peak and searching near the low resolution peak for a high resolution peak. The method includes calculating the R-R interval measurement based upon the high resolution peak. In a second aspect, a wireless sensor device comprises a processor and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to carry out the steps of the method. | 03-13-2014 |
20140121543 | MEASURING PSYCHOLOGICAL STRESS FROM CARDIOVASCULAR AND ACTIVITY SIGNALS - A method and system for measuring psychological stress disclosed. In a first aspect, the method comprises determining R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN) and determining a stress feature (SF) using the SDNN. In response to reaching a threshold, the method includes performing adaptation to update a probability mass function (PMF). The method includes determining a stress level (SL) using the SF and the updated PMF to continuously measure the psychological stress. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method. | 05-01-2014 |
20140128778 | DETERMINING BODY POSTURES AND ACTIVITIES - A method and wireless sensor device for determining body postures and activities. In one aspect, a method includes receiving sensor data. The method also includes detecting and classifying a body transition of a body based on the sensor data. The method also includes detecting if there is activity of the body based on the sensor data. If there is activity, the method also includes classifying the activity. If there is no activity, the method also includes classifying a rest position of the body based on the sensor data and based on a previous body transition. | 05-08-2014 |
20140200474 | DETECTION OF SLEEP APNEA USING RESPIRATORY SIGNALS - A method and system for sleep apnea detection are disclosed. The method comprises detecting at least one respiratory signal and utilizing a detection algorithm to automatically detect at least one sleep apnea event from the at least one respiratory signal. The system includes a sensor to determine at least one respiratory signal, a processor coupled to the sensor, and a memory device coupled to the processor, wherein the memory device includes a detection algorithm and an application that, when executed by the processor, causes the processor to utilize the detection algorithm to automatically determine at least one sleep apnea event from the at least one respiratory signal. | 07-17-2014 |
20140276127 | CONTEXTUAL HEART RATE MONITORING - A method and system for contextual heart rate monitoring are disclosed. In a first aspect, the method comprises calculating a heart rate using a detected ECG signal and detecting an activity level. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to calculate a heart rate using a detected ECG signal and to detect an activity level. | 09-18-2014 |
20150020571 | FALL DETECTION USING MACHINE LEARNING - A method and system for fall detection using machine learning are disclosed. The method comprises detecting at least one signal by a wireless sensor device and calculating a plurality of features from the at least one detected signal. The method includes training a machine learning unit of the wireless sensor device using the features to create a fall classification and a non-fall classification for the fall detection. The system includes a sensor to detect at least one signal, a processor coupled to the sensor, and a memory device coupled to the processor, wherein the memory device includes an application that, when executed by the processor, causes the processor to calculate a plurality of features from the at least one detected signal and to train a machine learning unit of the wireless sensor device using the features to create a fall classification and a non-fall classification for the fall detection. | 01-22-2015 |