Provided are systems for measuring an electrocardiogram (ECG) using a wearable device. An example system includes the wearable device. The wearable device has a means for recording an electrical signal from a single wrist of a patient. The wearable device also has a means for detecting a pulse of the patient and recording a photoplethysmogram (PPG) signal, via a PPG optical sensor associated with the wearable device. The wearable device further has a means for generating the electrical signal segments being time-locked to the PPG signal by utilizing the PPG signal as a reference signal. Furthermore, the wearable device has a means for summing the electrical signal segments in a given time period and dividing by the number of segments to produce an average ECG waveform.

There is described a method and system for measuring a level of anxiety. Measured data comprising EEG data collected from a parietal (P) EEG electrode is received. A group 8 indicator based on a power, P-power (dt), associated with a delta-theta frequency band, dt, within a delta-theta frequency range is extracted. Based on said group 8 indicator, a level of anxiety, LoA, is determined which is a value indicative of the level of anxiety of the subject.

A system and method for display of subcutaneous cardiac monitoring data are provided. Cutaneous action potentials of a patient and other sensed data associated with the patient are recorded as electrocardiogram (EGC) data over a set time period using a subcutaneous insertable cardiac monitor. A set of R-wave peaks is identified within the ECG data and an R-R interval plot is constructed. A difference between recording times of successive pairs of the R-wave peaks in the set is determined. A heart rate associated with each difference is also determined. The pairs of the R-wave peaks and associated heart rate are plotted as the R-R interval plot. A diagnosis of cardiac disorder is facilitated based on patterns of the plotted pairs of the R-wave peaks, the associated heart rates in the R-R interval plot, and background data based on the other sensed data.

A continuous glucose monitoring system may utilize externally sourced information regarding the physiological state and ambient environment of its user for externally calibrating sensor glucose measurements. Externally sourced factory calibration information may be utilized, where the information is generated by comparing metrics obtained from the data used to generate the sensor's glucose sensing algorithm to similar data obtained from each batch of sensors to be used with the algorithm in the future. The output sensor glucose value of a glucose sensor may also be estimated by analytically optimizing input sensor signals to accurately correct for changes in sensitivity, run-in time, glucose current dips, and other variable sensor wear effects. Correction actors, fusion algorithms, EIS, and advanced ASICs may be used to implement the foregoing, thereby achieving the goal of improved accuracy and reliability without the need for blood-glucose calibration, and providing a calibration-free, or near calibration-free, sensor.

A fabric-based item may be provided with a stretchable band. The stretchable band may be formed from a ring-shaped strip of stretchable fabric having an opening configured to fit around a body part of a user. Circuitry may be coupled to strands of material in the stretchable band. The circuitry may include sensor circuitry for making measurements on the body part such as electrocardiogram measurements, blood pressure measurements, and respiration rate measurements. Wireless communications circuitry in the fabric-based item may be used to communicate wirelessly with external electronic equipment. A wireless power transmitting device may transmit wireless power. A coil formed from conductive strands in the fabric-based item may be used by wireless power receiving circuitry in the fabric-based item to receive the wireless power. The coil may have one or more turns that run around the ring-shaped strip of stretchable fabric.

Techniques are disclosed for using a cardiac signal sensed via a plurality of electrodes disposed on one or more leads implanted within an epidural space of a patient to control spinal cord stimulation (SCS) therapy. In one example, an implantable medical device (IMD) senses an electrical signal via a plurality of electrodes disposed on one or more leads implanted within an epidural space of a patient. Processing circuitry determines, from the electrical signal, one or more cardiac features indicative of activity of a heart of the patient. The processing circuitry controls, based on the one or more cardiac features, delivery of SCS therapy to the patient.

A method includes capturing, using an Internet of Things (IoT) device, real-time video of a user performing an exercise associated with a first exercise class. Real-time video of the user is displayed, via a display, concurrently with video of an instructor, to provide a visual comparison of the user to the instructor. Image analysis of the real-time video of the user is performed to determine a performance of the user, and a representation thereof is displayed. Biometric data associated with the user is received at the IoT device from a wearable device at multiple points in time. Heart rates are identified based on the biometric data, and scores based on the heart rates are displayed via the display. A recommendation for a second exercise class different from the first exercise class is determined based on a profile of the user, and displayed via the display.

A method includes capturing, using an Internet of Things (IoT) device, real-time video of a user performing an exercise associated with a first exercise class. Real-time video of the user is displayed, via a display, concurrently with video of an instructor, to provide a visual comparison of the user to the instructor. Image analysis of the real-time video of the user is performed to determine a performance of the user, and a representation thereof is displayed. Biometric data associated with the user is received at the IoT device from a wearable device at multiple points in time. Heart rates are identified based on the biometric data, and scores based on the heart rates are displayed via the display. A recommendation for a second exercise class different from the first exercise class is determined based on a profile of the user, and displayed via the display.

The present application relates to radio detection and ranging (radar) systems and, in particular, to a radar system having a photonics-based signal generator. Such a radar system comprises a stepped-frequency optical signal generator, an optical-to-electrical converter, and a transmitter. The stepped-frequency optical signal generator is configured for converting an optical signal into a stepped-frequency optical signal. The optical-to-electrical converter for converting the stepped-frequency optical signal into a stepped-frequency electrical signal. The transmitter for transmitting a microwave signal based on the stepped-frequency electrical signal.

A method of determining a fused sensor measurement is disclosed including: obtaining sensor measurements from sensors detecting a same type of physiological measurement; determining a signal quality index (SQI) of each sensor including determining an extent to which a sensor measurement differs from others among the sensor measurements obtained from each sensor; determining a weightage of each sensor based on the SQI of each sensor; and determining a fused sensor measurement from the plurality of sensors based on the weightage of each sensor and filtered sensor measurements of each sensor obtained from a Kalman filter operation. A vehicle safety system includes: a vehicle electronic control unit configured to: determine the sensor measurement extent, to determine the SQI of each sensor, determine the weightage of each sensor, determine the fused sensor measurement, determine the occupant's physiological condition, and if the physiological condition is abnormal, perform at least one vehicle operation.