In Spinal Cord Stimulation (SCS) systems having sensing capability, conventional wisdom seeks to minimize or avoid sensing of stimulation artifacts caused by the stimulation. Despite this, the present disclosure recognizes that stimulation artifacts in and of itself can include useful information relevant to operation of the SCS implant and/or the status of the patient. In particular, stimulation artifact features as sensed canbe used to determine a posture or activity of the patient, or more generally to adjust the stimulation program that the SCS implant is providing. Furthermore, sensing of stimulation artifact features can be as useful as, and possibly even more useful than, information gleaned from sensing neural responses to stimulation, such as Evoked Compound Action Potentials (ECAPs).

To provide a determination device and program that make it possible to improve accuracy of determining a sign related to a decrease in driving ability of a driver.

Provided is a determination device including: a sign determination section configured to determine a sign related to a decrease in driving ability of a driver who drives a mobile object on a basis of head movement measurement data obtained by measuring head movement of the driver and eye movement measurement data obtained by measuring eye movement of the driver; and a head swing control section configured to control a head swing section in such a manner that the head swing section swings the head of the driver.

According to certain embodiments, an electronic device comprises: a sensor module; a processor operatively connected to the sensor module; and a memory operatively connected to the processor, wherein the memory stores instructions that, when executed, cause the processor to perform a plurality of operations comprising: acquiring sensor information through the sensor module, determining sleep information of a user, based on the sensor information, and determining a biorhythm of the user, based on the sleep information.

The present disclosure relates to systems and methods for detecting sleep-wake activity of a subject using change-point events determined from physiological and/or movement measures. In one implementation, the method may include obtaining at least one set of sensor data generated by one or more sensors for a period of time. The method may also include generating at least two measures from the at least one set of sensor data. The method may further include determining a series of change point events for each measure for the period of time. The method may include determining a sleep stage for each interval of the period of time from at least two sleep stages by processing the series of change point events for each measure using a sleep stage classifier. The sleep stage classifier may include a set of parameters for each measure.

A computer system for blue-light therapy comprising one or more processors, one or more computer-readable memories, and one or more computer-readable storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions including determining a need for blue-light therapy, and providing a blue-light stimulus to a user, wherein the blue-light stimulus comprises a visible range of 380 to 500 nm, and wherein, in response to the blue-light stimulus, a circadian rhythm of the user is shifted to a second rhythm, the second rhythm different than the circadian rhythm prior to the administration of the blue-light stimulus.

The present invention provides a construction method for automatic sleep staging and use thereof. The construction method for automatic sleep staging comprises: acquiring a plurality of sets of PSG signals and manual sleep information of PSG signals; pre-analyzing to decompose the original time series in the PSG signals into a set of pseudo-intrinsic mode functions (pseudo-IMFs); assembling the pseudo-IMFs to obtain m sets of time series; analyzing by multiscale entropy (MSE), to calculate the entropy values of the m sets of time series on n coarse-graining timescales, thus obtaining an entropy matrix with m*n elements; establishing a correlation coefficient matrix between the levels of consciousness and the elements in the entropy matrix, and finding the coarse-graining timescale and filtering timescale corresponding to the most significantly positively correlated element or the most significantly negatively correlated element in the correlation coefficient matrix; and calculating the entropy value on the coarse-graining timescale and filtering timescale corresponding to the most significantly positively correlated element or the most significantly negatively correlated element, and assessing the sleep state according to the entropy value.

A sleepiness prediction system includes a lifelog obtainer, a route information obtainer, and a sleepiness predictor. The lifelog obtainer obtains a lifelog including at least a get-up time of an occupant of a vehicle and a boarding time at which the occupant boards the vehicle. The route information obtainer obtains route information regarding a route to a destination of the vehicle. The sleepiness predictor predicts a sleepiness level of the occupant while the occupant is in the vehicle, based on the lifelog and the route information.

Embodiments of the disclosure provide a system for predicting onset of sleep. The system includes a user worn device that includes a plurality of primary sensors to collect a plurality of primary data of a user, a first processor, in communication with the primary sensors, to receive the data and further process the data, wherein the processor determines an onset of sleep data in the user, a first communications module, in communication with the processor, transmits the onset of sleep data, a secondary device, that collects secondary data of the user, a secondary processor, that receives the onset sleep data from the first communications module and secondary data from the at least one secondary sensor, and combines the received onset sleep data and the secondary data to determine whether an onset of sleep event is possible and warn the user about the onset of sleep event.

A non-transitory computer-readable recording medium records a display program for causing a computer to perform processes of: acquiring data about a space in which a subject sleeps, the data having been detected by a first sensor installed in the space; and displaying a zone including a sleep zone, a first zone before sleep onset, and a second zone after awakening of the subject in a different mode from other zones, when chronologically displaying suitability of the space as a sleep environment on a basis of the acquired data.

Long-term electrocardiographic and physiological monitoring over a period lasting up to several years in duration can be provided through a continuously-recording insertable cardiac monitor. The sensing circuitry and the physical layout of the electrodes are specifically optimized to capture electrical signals from the propagation of low amplitude, relatively low frequency content cardiac action potentials, particularly the P-waves that are generated during atrial activation and storing samples of captured signals. In general, the ICM is intended to be implanted centrally and positioned axially and either over the sternum or slightly to either the left or right of the sternal midline in the parasternal region of the chest.