A user identification device according to a disclosed embodiment includes a transmitter for scattering radio-frequency (RF) signals into tissues of a body part of a user, a receiver for receiving the RF signals having passed through the tissues of the body part of the user, a memory for storing parameters of a trained classification algorithm, and a processor for identifying the user by analyzing the received RF signals based on the trained classification algorithm by using the parameters of the trained classification algorithm in response to receiving the RF signals through the receiver.

The invention provides a body-worn system that continuously measures pulse oximetry and blood pressure, along with motion, posture, and activity level, from an ambulatory patient. The system features an oximetry probe that comfortably clips to the base of the patient's thumb, thereby freeing up their fingers for conventional activities in a hospital, such as reading and eating. The probe secures to the thumb and measures time-dependent signals corresponding to LEDs operating near 660 and 905 nm. Analog versions of these signals pass through a low-profile cable to a wrist-worn transceiver that encloses a processing unit. Also within the wrist-worn transceiver is an accelerometer, a wireless system that sends information through a network to a remote receiver, e.g. a computer located in a central nursing station.

A medical device is disclosed. The medical device includes an RFID reader for receiving information from at least one RFID transponder. The medical device also includes a memory for storing a database and at least one processor for processing information. Also, a remote controller for a medical device is disclosed. The remote controller includes an information receiver for receiving information related to food. The infusion device also includes a memory for storing a database and at least one processor for processing information. A method for use in a medical device is also disclosed. The method includes receiving information from an RFID transponder related to food. Also, the processing the information by comparing the information to a database is included in the method. The method also includes determining the acceptability of the food and providing information related to acceptability to the user.

A personal impact monitoring system is described herein comprising a monitoring station that receives impact events sent from personal impact monitors using a monitoring station receiver. The impact events which specify impact parameters associated with the impact events are stored in a data storage location associated with the monitoring station. Software operating on the operating station is configured to receive the impact events from the data storage location and to perform calculations based on the impact events to identify notable impact events.

A capnography system includes a CO.sub.2 sensing system having a CO.sub.2 sensor configured to measure a CO.sub.2 concentration in exhaled breath of a subject, a processor configured to derive one or more breath related parameters based on the measured CO.sub.2 concentration, and a communication unit. The capnography system includes a tubing system configured to allow flow of respiratory gasses therethrough. The tubing system includes a connector configured to connect the tubing system to the CO.sub.2 sensing system and a communication component configured to provide an indication of a type of the tubing system to the communication unit. The communication unit is configured to transfer data to the processor based on the indication obtained from the communication component, and the processor is configured to change or suggest a change of an operation mode of the CO.sub.2 sensing system based on the data.

Aspects of the present disclosure provide methods, apparatuses, and systems for closed-loop sleep protection and/or sleep regulation. According to an aspect, sleep disturbing noises are predicted and a biosignal parameter is measured to dynamically mask predicted disturbing environmental noises in the sleeping environment with active attenuation. Environmental noises in a sleeping environment of a subject are detected, input, or predicted based on historical data of the sleeping environment collected over a period of time. The biosignal parameter is used to determine sleep physiology of a subject. Based on the environmental noises in the sleeping environment and the determined sleep physiology, the noises are predicted to be disturbing or non-disturbing noises. For predicted disturbing noises, one or more actions are taken to regulate sleep and avoid sleep disruption by using sound masking prior to or concurrently with the occurrence of the predicted disturbing noises.

An exercise information acquisition equipment of the present invention is an electronic equipment which builds therein a battery, is driven with electric power of the battery and acquires information relating to an exercise that a user performs and includes a battery remaining amount acquisition device which acquires a battery remaining amount of the battery, a time information acquisition device which acquires information relating to a time taken for the exercise which is information that how long the user plans to perform the exercise and an electric power control device which controls an operation pertaining to a power consumption reduction of the electronic equipment on the basis of the battery remaining amount and the information relating to the time taken for the exercise.

Intraocular physiological sensor implants include a physiological sensor, and a housing comprising a faceplate. The physiological sensor is integrated with the faceplate. The physiological sensor typically comprises an intraocular pressure sensor, such as a capacitive pressure sensor that may further include a flexible diaphragm electrode spaced apart from a counter electrode. The intraocular pressure sensor detects intraocular pressure, to identify patient conditions such as glaucoma.

A process for measuring heart beats fiducial points and classifying heart beats includes sampling a raw ECG signal,—providing a first filtered signal,—providing a second filtered signal, detecting left and right limits data for the beats in said second filtered signal, and receiving the first filtered signal and receiving said left and right limits data, sampling and storing ECG curve data of beats from said first filtered signal synchronized by said left and right limits data and extracting fiducial points of said beats from said ECG curve data, said fiducial points including at least the QRS points values of the beats, and classifying each of said beats in classes based on a correlation of its ECG curve data with respect to average ECG curves data of classes of previously averaged classified beats.

Embodiments of the present disclosure sets forth a computer-implemented method comprising receiving, from at least one sensor, sensor data associated with an environment, computing, based on the sensor data, a cognitive load associated with a user within the environment, computing, based on the sensor data, an affective load associated with an emotional state of the user, determining, based on both the cognitive load at the affective load, an affective-cognitive load, determining, based on the affective-cognitive load, a user readiness state associated with the user, and causing one or more actions to occur based on the user readiness state.