Embodiments of a system and/or method can include: a set of weight sensor subsystems associated with the set of users, wherein a weight sensor subsystem of the set of weight sensor subsystems comprises a weight sensor operable to collect a weight dataset for a user, wherein the weight dataset is associated with a physical activity characteristic of the user, and a wireless communication module operable to transmit the weight dataset; and a medical improvement subsystem wirelessly connectable to the set of weight sensor subsystems, wherein the medical improvement subsystem is operable to: assign the user to a user subgroup based on the physical activity characteristic of the user; determine a physical activity metric based on the weight dataset; and promote a therapeutic intervention to the user based on the physical activity metric, where the therapeutic intervention is operable to improve the status of the first user.

In at least one embodiment, a system for performing biometric evoked response monitoring and feedback for a vehicle is provided. The system includes a plurality of sensors and at least one controller. The plurality of sensors may be positioned about a main cabin of the vehicle and each sensor is configured to provide a first signal indicative of a measured electromagnetic characteristic of an anatomical feature of a driver for the vehicle in response to an alert being issued to the driver. The at least one controller is configured to receive the first signal and to determine a stimulus response and an attentiveness response of the driver based on the measured electromagnetic characteristic of the anatomical feature of the driver.

A method for identifying changes in an epilepsy patient's disease state, comprising: receiving at least one body data stream; determining at least one body index from the at least one body data stream; detecting a plurality of seizure events from the at least one body index; determining at least one seizure metric value for each seizure event; performing a first classification analysis of the plurality of seizure events from the at least one seizure metric value; detecting at least one additional seizure event from the at least one determined index; determining at least one seizure metric value for each additional seizure event, performing a second classification analysis of the plurality of seizure events and the at least one additional seizure event based upon the at least one seizure metric value; comparing the results of the first classification analysis and the second classification analysis; and performing a further action.

A method for identifying changes in an epilepsy patient's disease state, comprising: receiving at least one body data stream; determining at least one body index from the at least one body data stream; detecting a plurality of seizure events from the at least one body index; determining at least one seizure metric value for each seizure event; performing a first classification analysis of the plurality of seizure events from the at least one seizure metric value; detecting at least one additional seizure event from the at least one determined index; determining at least one seizure metric value for each additional seizure event, performing a second classification analysis of the plurality of seizure events and the at least one additional seizure event based upon the at least one seizure metric value; comparing the results of the first classification analysis and the second classification analysis; and performing a further action.

An external data retrieval apparatus receives a low resolution version of a physiological signal from an active implantable medical device and determines if the physiological signal represents a clinically significant event. The apparatus provides an indication of such determination to the implantable medical device. If the physiological signal does represent a clinically significant event, the apparatus receives a full download of the physiological signal from the implantable device.

The present disclosure pertains to a system configured to determine sleep stages in a subject based on cardiac artifact information and brain activity information in EEG signals. Cardiac artifacts present in EEG signals can cause erroneous sleep stage determinations which may result in inopportune sensory stimulation during sleep, no stimulation at all, discarding long periods of EEG signal information, and/or other events. The present system enhances real-time sleep stage determinations compared to prior art systems and/or provides other advantages because the present system determines the current sleep stage of the subject based on both cardiac activity information and brain activity information included in the EEG signals.

The present disclosure pertains to a system configured to determine sleep stages in a subject based on cardiac artifact information and brain activity information in EEG signals. Cardiac artifacts present in EEG signals can cause erroneous sleep stage determinations which may result in inopportune sensory stimulation during sleep, no stimulation at all, discarding long periods of EEG signal information, and/or other events. The present system enhances real-time sleep stage determinations compared to prior art systems and/or provides other advantages because the present system determines the current sleep stage of the subject based on both cardiac activity information and brain activity information included in the EEG signals.

An information processing apparatus includes a display control unit. The display control unit is configured to perform control to display a signal source in a superimposed manner on a plurality of biological tomographic images sliced in a predetermined direction, the signal source corresponding to a part of biological data indicating a temporal change of a biological signal, and initially display, in a display region of a screen of a display unit, a biological tomographic image on which a predetermined signal source is superimposed among the plurality of sliced biological tomographic images.

An information processing apparatus includes a display control unit. The display control unit is configured to perform control to display a signal source in a superimposed manner on a plurality of biological tomographic images sliced in a predetermined direction, the signal source corresponding to a part of biological data indicating a temporal change of a biological signal, and initially display, in a display region of a screen of a display unit, a biological tomographic image on which a predetermined signal source is superimposed among the plurality of sliced biological tomographic images.

This document discusses, among other things, systems and methods to map an electrical waveform to a user-perceivable input. Some system examples include at least one electrode configured to sense neural activity in a patient, a mapping controller configured to receive an electrical waveform from the at least one electrode and map the sensed neural activity to a user-perceivable output based on the sensed neural activity, and a user interface operably connected to the mapping controller and configured to provide the user-perceivable output to a user.