The present disclosure is related to systems and methods for motion detection. The method includes obtaining, via at least one detection device, detection data of a subject located in a field of view (FOV) of a medical device. The method also includes determining motion data of the subject based on the detection data.

The present disclosure is related to systems and methods for motion detection. The method includes obtaining, via at least one detection device, detection data of a subject located in a field of view (FOV) of a medical device. The method also includes determining motion data of the subject based on the detection data.

In one aspect, embodiments of the present disclosure can comprise a non-contact signal detection system for detecting movement associated with a subject. The system can comprise a carrier source configured to generate a first carrier signal in phase coherence with a second carrier signal. Additionally, the system may incorporate a phase-locked loop including noise pre-cancellation system for suppressing the noise associated with a beat signal and a controlled oscillation system. The noise pre-cancellation system can be configured to phase-lock the beat signal to a first reference signal in order to stabilize the phase of the beat signal and pre-cancel the noise associated with the beat signal. The controlled oscillation system can include a propagation pathway on which a transmission signal is phase-modulated with a vibratory signal of the subject. Once acquired, the vibratory signal can have suppressed noise.

A biological data obtaining device includes a storage unit, a first generation unit, and a second generation unit. The storage unit is configured to store time-series data in which first to N.sup.th distance-based fluctuation data are arranged. The first to N.sup.th distance-based fluctuation data are obtained based on reflected waves which are reflected from a living body at different times, wherein n.sup.th distance-based fluctuation data indicates changes in signal strength with respect to distance. The first generation unit is configured to generate time-based fluctuation data by performing strength obtaining process. The strength obtaining process includes obtaining one corresponding strength information, wherein the one corresponding strength information is a signal strength based on reflected waves from a predetermined detection part of the living body. The second generation unit is configured to generate biological data of the detection part of the living body based on the time-based fluctuation data.

Solutions are provided for immediate and precise diagnosis of partial obstruction in children and adults and for detection of potentially preventable events of accidental suffocation and strangulation and for the diagnosis of high upper airway resistance syndrome (UARS) or partial airway obstruction during sleep in adults. The solutions identify pathognomonic indices for partial obstruction by utilizing noninvasive miniature sensors for monitoring the breath dynamics.

Solutions are provided for immediate and precise diagnosis of partial obstruction in children and adults and for detection of potentially preventable events of accidental suffocation and strangulation and for the diagnosis of high upper airway resistance syndrome (UARS) or partial airway obstruction during sleep in adults. The solutions identify pathognomonic indices for partial obstruction by utilizing noninvasive miniature sensors for monitoring the breath dynamics.

Provided are apparatuses and methods for non-invasively and continuously measuring physiological parameters of a mammal subject. The apparatus includes multiple sensor systems attached to the mammal subject, and a microcontroller unit (MCU). The sensor systems are time-synchronized and communicate with each other wirelessly and bidirectionally. Each of the sensor systems includes at least one sensor configured to detect a vital sign of the mammal subject and generate a corresponding one of the physiological parameters. The MCU is in wireless communication with the plurality of sensor systems. In operation, the MCU receives, from the sensor systems, and displays the physiological parameters of the mammal subject. The apparatus and method can be used in applications such as developing therapeutics or vaccines for a disease, or diagnosing a disease.

Adaptive respiratory condition assessment can include capturing signals generated by sensors of a portable device positioned to sense a user's body. The sensors can generate the signals in response to sensor-detected movements of the user's body measured along multiple axes and corresponding to lung activity in the user's body during respiratory cycles. A preferred axis of measurement can be selected using signal processing performed by a signal processor embedded in the portable device. Waveforms generated from signals corresponding to signals generated in response to movements of the user's body measured along the preferred axis can be filtered for extracting biomarkers from the waveforms using the signal processor. The one or more biomarkers extracted correspond to the lung activity. Based on the biomarkers, a respiratory condition of the user can be determined. A notification based on the respiratory condition as determined can be generated and conveyed with the portable device.

An integrated cardio-respiratory system that fuses continuously recorded data from multiple physiological sensor sources to acquire signals representative of acoustic events caused by physiological phenomena occurring in the cardiac and/or arterial structures underneath particular areas of the chest and/or neck to monitor cardiac and respiratory conditions.

There are provided a biological signal measurement device capable of obtaining a variety of biological information and applicable also to medical fields and the like, a biological state inference device, and a biological state inference system using these. The biological signal measurement device 1 of the present invention includes three biological signal detection units, namely, a left upper part biological signal detection unit 11, a right upper part biological signal detection unit 12, and a lower part biological signal detection unit 13. The biological state inference device 1 is capable of obtaining a highly precise inference-use processed waveform from which electrical noise has been removed, by using an appropriate combination of time-series data obtained from the three biological signal detection units 11 to 13. Because the precision of an inference-use processed waveform corresponding to target biological information on breathing, heart sound, or the like increases, the precision of inferring a biological state also increases.