A controllable window of time is provided for waking a user from sleep. A system uses this window to variably control the acquisition of physiological data from a device such as a wearable monitor, such as by initiating data acquisition at the beginning of the window, and the acquired data can be used in turn to control when, during the window, an active alarm to the user might be provided. Using this technique, data acquisition from a physiological monitoring device or the like can be increased around the onset of the window to more accurately calculate a suitable waking time for the user within the window. This advantageously avoids the need for continuous, high-frequency data communications during long intervals of sleep, and focuses data transmission, related communications, and computing resources on those intervals when up-to-date data might be most useful for optimizing the user's wake up experience.

A device for analyzing sleep breathing using a radar includes a transceiver configured to transmit a radar signal toward a subject and receive the radar signal reflected from the subject; an average breathing signal calculation unit configured to calculate an average breathing signal of the subject based on the radar signal; a sleep breathing pattern information generation unit configured to generate sleep breathing pattern information of the subject by comparing the radar signal with the average breathing signal; and a sleep breathing event detection unit configured to detect a sleep breathing event based on the sleep breathing pattern information.

A method may include training one or more machine learning models to predict a decline in employee performance. The machine learning models may be trained in a federated manner to avoid the exchange of personal data. The trained machine learning models may be applied to data associated with an employee that corresponds to one or more leading indicators of employee burnout. In response to the trained machine learning models predicting a decline in the performance of the employee, the root causes of the predicted decline in the performance of the employee may be identified by applying an explainability algorithm such as Shapley Additive Explanations (SHAP). A report including a corrective action for the predicted decline in employee performance may be generated based on the root causes. Related systems and computer program products are also provided.

A system and method for camera-based stress determination. The method includes: determining a plurality of regions-of-interest (ROIs) of a body part; determining a set of bitplanes in a captured image sequence for each ROI that represent HC changes using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set trained using bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets; determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes; for each ROI, determining intervals between heartbeats based on peaks in the HC change signal; determining heart rate variability using the intervals between heartbeats; determining a stress level using at least one determination of a standard deviation of the heart rate variability; and outputting the stress level.

An apparatus includes a body and an actuator is coupled to the body. A needle is mounted to the actuator. The needle comprises a slot along a length to a tip. A sensor is coupled to a plurality of wires. A base is configured to be moveable by the actuator and includes a cutout, a circuit board having a microprocessor, and a plurality of contacts. Each contact is coupled to a wire of the plurality of wires. A power source is connected to the circuit board and to the base. A bracket is coupled to the bottom surface of the body and configured to receive the base. A patch is coupled to the bracket and has an adhesive. A needle is configured to be moveable by the actuator. The plurality of wires extend from the circuit board of the base, through the needle and out of the slot.

A camera (26) takes an image of at least one user (U1, U2, U3). A room environment information sensor (13) senses room environment information relating to an environment of a room (r1) in which the at least one user (U1, U2, U3) is present. The estimator (66) estimates a drowsiness condition of the at least one user (U1, U2, U3) based on the image of the at least one user (U1, U2, U3) taken by the camera (26) and the room environment information sensed by the room environment information sensor (13).

The invention relates to a device for determining the driving behavior of a driver, the device comprising at least means for receiving data from two or more data sources, of which at least one produces data relating to changes in the state of motion of a vehicle and at least one other produces measured data on the well-being of the driver; means for scoring the received data by comparing it with data-specific reference values; means for forming a respective sub-index from each scored item of data; means for determining a driving behavior index on the basis of the formed sub-indices; and means for controlling control equipment of the vehicle on the basis of the driving behavior index and/or for storing the driving behavior index in a database.

In one aspect of the disclosed implementations, a device includes one or more motion sensors for sensing motion of the device and providing activity data indicative of the sensed motion. The device also includes one or more processors for monitoring the activity data, and receiving or generating annotation data for annotating the activity data with one or more markers or indicators to define one or more characteristics of an activity session. The device also includes one or more feedback devices for providing feedback, a notice, or an indication to a user based on the monitoring. The device further includes a portable housing that encloses at least portions of the motion sensors, the processors and the feedback devices.

A breath detecting system and breath detecting mat thereof are disclosed. The breath detecting mat is placed under bed mattress and has a hollow board, a vibration sensor and a signal processing circuit. The vibration sensor and the signal processing circuit are mounted in the hollow board. The vibration sensor senses the micro-vibrations caused by the breathing of the person is lying on the bed mattress and outputs the breath sensing signal to the signal processing circuit. The signal processing circuit samples the sensing signal according to different moving average points to generate the fast-moving and slow-moving average signals. Since the first fast-moving and slow-moving average signals have many cross points, the signal processing circuit calculates each time difference between every two adjacent cross points. A present breath frequency is calculated according to the time differences. Therefore, the noises of the sensing signal are effectively removed.

A method of monitoring physiological conditions including an acoustic measurement device and a remote controller. The remove controller configured to measure physiological conditions; calculate absolute tidal volume, a direction of tidal volume, and a rate of change of tidal volume; correlate absolute tidal volume, a direction of tidal volume, and a rate of change of tidal volume to a risk score; calculate a direction trend and rate of change for each physiological condition; correlate the direction trend and rate of change for each physiological condition; detect predefined patterns in the direction trend and rate of change of the measured physiological condition at the predetermined interval; and generating at least one from the group consisting of an alert, an alarm, and a medical treatment if the risk score defined on the predefined scale exceeds a first predetermined risk score threshold and at least one predefined pattern is detected.