A fabric strain gauge includes a knitted fabric and a plurality of conductive measuring fibers, a first high conductivity fiber, and a second high conductivity fiber. The conductive measuring fibers are threaded with the knitted fabric, the first high conductivity conductive fiber is threaded with the knitted fabric, and connected to one or more ends of the conductive measuring fibers, and the second high conductivity fiber is threaded with the knitted fabric, and connected to the other ends of at least part of the conductive measuring fibers. In addition, a fabric pressure gauge and a smart clothing are also disclosed herein.

Methods and apparatus provide monitoring of a sleep disordered breathing state of a person such as for screening. One or more sensors may be configured for non-contact active and/or passive sensing. The processor(s) (7304, 7006) may extract respiratory effort signal(s) from one or more motion signals generated by active non-contact sensing with the sensor(s). The processor(s) may extract one or more energy band signals from an acoustic audio signal generated by passive non-contact sensing with the sensor(s). The processor(s) may assess the energy band signal(s) and/or the respiratory efforts signal(s) to generate intensity signal(s) representing sleep disorder breathing modulation. The processor(s) may classify feature(s) derived from the one or more intensity signals to generate measure(s) of sleep disordered breathing. The processor may generate a sleep disordered breathing indicator based on the measure(s) of sleep disordered breathing. Some versions may evaluate sensing signal(s) to generate indication(s) of cough event(s) and/or cough type.

Methods and apparatus provide monitoring of a sleep disordered breathing state of a person such as for screening. One or more sensors may be configured for non-contact active and/or passive sensing. The processor(s) (7304, 7006) may extract respiratory effort signal(s) from one or more motion signals generated by active non-contact sensing with the sensor(s). The processor(s) may extract one or more energy band signals from an acoustic audio signal generated by passive non-contact sensing with the sensor(s). The processor(s) may assess the energy band signal(s) and/or the respiratory efforts signal(s) to generate intensity signal(s) representing sleep disorder breathing modulation. The processor(s) may classify feature(s) derived from the one or more intensity signals to generate measure(s) of sleep disordered breathing. The processor may generate a sleep disordered breathing indicator based on the measure(s) of sleep disordered breathing. Some versions may evaluate sensing signal(s) to generate indication(s) of cough event(s) and/or cough type.

A mat for detecting biological information has an air bag formed in an elongated shape and outputting air according to pressure from a living body, a plate-shaped cushion member in which a concave portion having substantially the same size as the air bag is formed in a central portion, the air bag is fitted in the concave portion, and has substantially the same thickness as the air bag, and a pair of synthetic resin plate-shaped members each having substantially the same size as the plate-shaped cushion member and being formed so as to be bendable as a whole and provided so as to sandwich the plate-shaped cushion member.

A mat for detecting biological information has an air bag formed in an elongated shape and outputting air according to pressure from a living body, a plate-shaped cushion member in which a concave portion having substantially the same size as the air bag is formed in a central portion, the air bag is fitted in the concave portion, and has substantially the same thickness as the air bag, and a pair of synthetic resin plate-shaped members each having substantially the same size as the plate-shaped cushion member and being formed so as to be bendable as a whole and provided so as to sandwich the plate-shaped cushion member.

A system for dynamic registration of autonomy using augmented reality can include an augmented reality system, an imaging system, a measuring system, and a computer system. The augmented reality system can be configured to display an augmented representation. The imaging system can be configured to image an anatomical feature of the patient and can generate anatomical imaging data. The measuring system can be configured to measure an anatomical movement of the patient and can generate an anatomical movement data. The computer system can be configured to receive the anatomical imaging and positional data and the anatomical movement data, generate the augmented representation based on the anatomical imaging data, associate the augmented representation with the anatomical movement data, render the augmented representation on the augmented reality system, and selectively update the augmented representation based on the anatomical movement data.

A monitoring method is performed by a digital processor while a subject is asleep or with little movement. The method includes receiving transducer signals for a subject and training a model for characteristics of a subject or subject group. The model is used in generating sleep data according to real-time correspondence and values of transducer signals including subject rib displacement, subject abdomen displacement, subject torso movement, and subject orientation. Combinations of the transducer inputs are used to determine features from which outputs such as an AHI score are determined. A feature matrix is normalized based on dynamically-generated positional epochs bordered by subject movements to new positions and used to provide a normalization matrix.

The following relates generally to motion prediction in magnetic resonance (MR) imaging. In some embodiments, a “modular” approach is taken to motion correction. That is, individual motion sources (e.g., a patient's breathing, heartbeat, stomach contractions, peristalsis, and so forth) are accounted for individually in the motion correction. In some embodiments, to correct for a particular motion source, a reference state is created from a volume of interest (VOI), and other states are created and deformably aligned to the reference state.

The following relates generally to motion prediction in magnetic resonance (MR) imaging. In some embodiments, a “modular” approach is taken to motion correction. That is, individual motion sources (e.g., a patient's breathing, heartbeat, stomach contractions, peristalsis, and so forth) are accounted for individually in the motion correction. In some embodiments, to correct for a particular motion source, a reference state is created from a volume of interest (VOI), and other states are created and deformably aligned to the reference state.

A garment including a breath sensor module. The breath sensor module includes a stretchable sensor configured to respond to at least one of expansion and contraction of a torso of an individual wearing the garment. The breath sensor module also may include an electronics module. The electronics module includes, for example, a processor and a haptic feedback device. In response to the processor determining that the individual's breathing meets predetermined criteria based on the response of the stretchable sensor, the haptic feedback device produces haptic feedback such that the individual is reminded to breathe. Further, the breath sensor module does not include a transmitter or a receiver configured to transmit or receive data outside of the breath sensor module. Advantageously, this allows for streamlined use, and less-intrusive reminders to the individual wearing the garment, without the complexities of signal transmission or receiving.