A measurement unit (110) performs a measurement process for measuring a biological signal (21) from a target person (20). The biological signal (21) contains a plurality of components and can be measured in a manner to be unnoticeable by the target person (20). A component extraction unit (120) extracts an authentication component, which is to be used for authentication, from the plurality of components. A feature amount extraction unit (130) extracts a current feature amount indicating a present feature amount of the authentication component, from the authentication component. A registration unit (140) registers an identifier, which is used for identifying the target person (20), and a template feature amount, which is a feature amount extracted from the target person (20) in the past, in a storage unit (160), as template information (161). A comparison unit (150) compares the current feature amount to the template feature amount. When a difference between the current feature amount and the template feature amount is within a tolerance value (162), the comparison unit (150) returns processing to the measurement process and the authentication is repeated. When the difference between the current feature amount and the template feature amount is larger than the tolerance value (162), the processing is ended.

This detection device is a device for detecting the movement of a human body. The detection device has: a substrate having flexibility; an electric element provided on the substrate and having an electrical characteristic that changes with the movement of the human body; and a semiconductor element that is provided on the substrate, detects a change in the electrical characteristic of the electric element, and outputs a detection value corresponding to the detected result. The substrate is a flexible substrate or a fabric member including conductive fibers, for example.

An evaluation test apparatus is configured to evaluate or test measurement precision of a biological information measurement device configured to measure biological information. The evaluation test apparatus includes: a function generator configured to generate a plurality of input waveform signals by a predetermined operation; an indenter configured to pressure a piezoelectric element of the biological information measurement device; a vibration driver selected from a motor and a solenoid and configured to vibrate the indenter; and a control board configured to control the vibration driver. The control board includes an adder configured to combine the plurality of input waveform signals generated by the function generator. The vibration driver vibrates the indenter based on a composite waveform signal combined by the adder.

An evaluation test apparatus is configured to evaluate or test measurement precision of a biological information measurement device configured to measure biological information. The evaluation test apparatus includes: a function generator configured to generate a plurality of input waveform signals by a predetermined operation; an indenter configured to pressure a piezoelectric element of the biological information measurement device; a vibration driver selected from a motor and a solenoid and configured to vibrate the indenter; and a control board configured to control the vibration driver. The control board includes an adder configured to combine the plurality of input waveform signals generated by the function generator. The vibration driver vibrates the indenter based on a composite waveform signal combined by the adder.

Objective Pulmonary Function (PF) evaluation for respiratory fatigue is vital to the diagnosis and management of many pediatric respiratory diseases in the intensive care, emergency and outpatient settings. A non-invasive PF instrument utilizes sensors and software to access respiratory breathing patterns, vital parameters, asynchrony and measures the work of breathing. Software algorithms predict respiratory fatigue. The hardware includes a microcircuit board that individually links to rib cage (RC) and abdominal (ABD) inductance bands. The bands wirelessly transmit changes in RC and ABD circumference. Point-of-care, real-time indices of respiratory work, breathing patterns and respiratory fatigue indices are developed on a user-friendly graphical user interface. The diagnostic data can later be securely emailed as an attachment for entry into patients' electronic medical records or sent to a caretaker's computer, or used directly to control a respiratory therapy device. The system can also be used for telemedicine homecare.

A frame storage stores an image obtained by imaging a region of at least part of the body of a user. A vital sign signal detector detects a signal sequence of a vital sign that cyclically varies from plural imaged regions of the body of the user by using captured images of a predetermined number of frames stored in the frame storage. A correlation calculator obtains the correlation between the signal sequences of the vital sign detected from the respective imaged regions of the body. An identity determining section determines whether or not the respective imaged regions of the body belong to the same user based on the correlation between the signal sequences of the vital sign detected from the respective imaged regions of the body.

An automatic process of predictive determination of the position and movements of the skin of a subject in a zone of interest, with the subject breathing freely or in an assisted manner, includes preliminarily acquiring multiple configurations of the skin profile in axial planes, at given successive times, in different respiratory positions, and for each axial plane, constructing at least one deformable digital model starting from different skin profiles, then noting, in a repetitive manner, the actual position of a point on the skin at the level of each axial plane, whose position is significantly modified during inhalation and exhalation phases, and providing, essentially in real time, a simulation of the skin profile in each axial plane, as a function of the actual position noted, and an evolving three-dimensional representation of the skin at the level of the zone of interest, by interpolation between the different axial planes.

One general aspect includes a bed having a mattress. The system also includes a sensor configured to: sense pressure of a sleeper on the mattress and transmit, to a controller, pressure data generated from the sensing of pressure of the sleeper on the mattress. The system also includes a controller may include a processor and a memory, the controller configured to receive the pressure data, identify, from the pressure data, one or more motion parameters, and determine, from the motion parameters, one or more neurologic measures of the sleeper. Other embodiments of this aspect include corresponding computer systems, apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

A device of the disclosure determines to which of a plurality of sleep stages in a sleep state including a wake stage a sleep state of a subject belongs, based on a sleep state function that is calculated using as variables a respiratory motion feature and a body motion feature that are respectively extracted from respiratory motion index values and body motion index values of the subject measured in time series. The sleep state function is a function including coefficient parameters that are set based on learning data including sleep stage determination results by a measurement of a sleep state for calibration, and respiratory motion features and body motion features that are measured simultaneously with the measurement for calibration.

A device of the disclosure determines to which of a plurality of sleep stages in a sleep state including a wake stage a sleep state of a subject belongs, based on a sleep state function that is calculated using as variables a respiratory motion feature and a body motion feature that are respectively extracted from respiratory motion index values and body motion index values of the subject measured in time series. The sleep state function is a function including coefficient parameters that are set based on learning data including sleep stage determination results by a measurement of a sleep state for calibration, and respiratory motion features and body motion features that are measured simultaneously with the measurement for calibration.