Abstract: An apparatus, system, and method monitors the motion, breathing, heart rate and sleep state of subjects, e.g., humans, in a convenient, non-invasive/non-contact, and low-cost fashion. More particularly, the motion, breathing, and heart rate signals are obtained through processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Periods of sleep disturbed respiration, or central apnea can be detected through analysis of the respiratory signal. The mean heart rate, and derived information, such as the presence of cardiac arrhythmias can be determined from the cardiac signal. Motion estimates can be used to recognize disturbed sleep and periodic limb movements. The sleep state may be determined by applying a classifier model to the resulting streams of respiratory, cardiac and motion data. A means for display of the sleep state, respiratory, cardiac, and movement status may also be provided.

Abstract: An apparatus, system, and method is disclosed for monitoring the motion, breathing, heart rate of humans in a convenient and low-cost fashion, and for deriving and displaying useful measurements of cardiorespiratory performance from the measured signals. The motion, breathing, and heart rate signals are obtained through a processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Processing into separate cardiac and respiratory components is described. The heart rate can be determined by using either spectral or time-domain processing. The respiratory rate can be calculated using spectral analysis. Processing to derive the heart rate, respiratory sinus arrhythmia, or a ventilatory threshold parameter using the system is described. The sensor, processing, and display can be incorporated in a single device which can be worn or held close to the body while exercising (e.g.

Abstract: An apparatus, system, and method for the measurement, aggregation and analysis of data collected using non-contact or minimally-contacting sensors provides quality of life parameters for individual subjects, particularly in the context of a controlled trial of interventions on human subjects (e.g., a clinical trial of a drug, or an evaluation of a consumer item such as a fragrance). In particular, non-contact or minimal-contact measurement of quality-of-life parameters such as sleep, stress, relaxation, drowsiness, temperature and emotional state of humans may be evaluated, together with automated sampling, storage, and transmission to a remote data analysis center. One component of the system is that the objective data is measured with as little disruption as possible to the normal behavior of the subject. The system can also support behavioral and pharmaceutical interventions aimed at improving quality of life.

Abstract: An apparatus, system, and method for the measurement, aggregation and analysis of data collected using non-contact or minimally-contacting sensors provides quality of life parameters for individual subjects, particularly in the context of a controlled trial of interventions on human subjects (e.g., a clinical trial of a drug, or an evaluation of a consumer item such as a fragrance). In particular, non-contact or minimal-contact measurement of quality-of-life parameters such as sleep, stress, relaxation, drowsiness, temperature and emotional state of humans may be evaluated, together with automated sampling, storage, and transmission to a remote data analysis center. One component of the system is that the objective data is measured with as little disruption as possible to the normal behavior of the subject. The system can also support behavioral and pharmaceutical interventions aimed at improving quality of life.

Abstract: An apparatus, system, and method for monitoring a person suffering from a chronic medical condition predicts and assesses physiological changes which could affect the care of that subject. Examples of such chronic diseases include (but are not limited to) heart failure, chronic obstructive pulmonary disease, asthma, and diabetes. Monitoring includes measurements of respiratory movements, which can then be analyzed for evidence of changes in respiratory rate, or for events such as hypopneas, apneas and periodic breathing. Monitoring may be augmented by the measurement of nocturnal heart rate in conjunction with respiratory monitoring. Additional physiological measurements can also be taken such as subjective symptom data, blood pressure, blood oxygen levels, and various molecular markers. Embodiments for detection of respiratory patterns and heart rate are disclosed, together with exemplar implementations of decision processes based on these measurements.

Abstract: A sensor for physiology sensing may be configured to generate oscillation signals for emitting radio frequency pulses for range gated sensing. The sensor may include a radio frequency transmitter configured to emit the pulses and a receiver configured to receive reflected ones of the emitted radio frequency pulses. The received pulses may be processed to detect physiology characteristics such as motion, sleep, respiration and/or heartbeat. In some embodiments, the sensor may employ a circuit including a pulse generator configured to generate signal pulses. The circuit may also include a dielectric resonator oscillator configured to generate a radio frequency oscillating signal. A switched oscillation circuit may be coupled to the pulse generator and the dielectric resonator oscillator. The switched circuit may be configured to generate a pulsed radio frequency oscillating signal for emitting the radio frequency pulses.

Abstract: Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor (100) may access sensor data signals related to bodily movement and/or respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include one or more of variability of respiration rate and variability of respiration amplitude. A processor may then determine a sleep stage based on one or more of respiration variability and bodily movement, such as with a combination of both. The determination of sleep stages may distinguish between deep sleep and other stages of sleep, or may differentiate between deep sleep, light sleep and REM sleep. The bodily movement and respiration movement signals may be derived from one or more sensors, such as non-invasive sensor (e.g., a non-contact radio-frequency motion sensor or a pressure sensitive mattress).

Abstract: An apparatus, system, and method for monitoring a person suffering from a chronic medical condition predicts and assesses physiological changes which could affect the care of that subject. Examples of such chronic diseases include (but are not limited to) heart failure, chronic obstructive pulmonary disease, asthma, and diabetes. Monitoring includes measurements of respiratory movements, which can then be analyzed for evidence of changes in respiratory rate, or for events such as hypopneas, apneas and periodic breathing. Monitoring may be augmented by the measurement of nocturnal heart rate in conjunction with respiratory monitoring. Additional physiological measurements can also be taken such as subjective symptom data, blood pressure, blood oxygen levels, and various molecular markers. Embodiments for detection of respiratory patterns and heart rate are disclosed, together with exemplar implementations of decision processes based on these measurements.

Abstract: An apparatus, system, and method monitors the motion, breathing, heart rate and sleep state of subjects, e.g., humans, in a convenient, non-invasive/non-contact, and low-cost fashion. More particularly, the motion, breathing, and heart rate signals are obtained through processing applied to a raw signal obtained in a non-contact fashion, typically using a radio-frequency sensor. Periods of sleep disturbed respiration, or central apnea can be detected through analysis of the respiratory signal. The mean heart rate, and derived information, such as the presence of cardiac arrhythmias can be determined from the cardiac signal. Motion estimates can be used to recognize disturbed sleep and periodic limb movements. The sleep state may be determined by applying a classifier model to the resulting streams of respiratory, cardiac and motion data. A means for display of the sleep state, respiratory, cardiac, and movement status may also be provided.

Abstract: Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor (100) may access sensor data signals related to bodily movement and/or respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include one or more of variability of respiration rate and variability of respiration amplitude. A processor may then determine a sleep stage based on one or more of respiration variability and bodily movement, such as with a combination of both. The determination of sleep stages may distinguish between deep sleep and other stages of sleep, or may differentiate between deep sleep, light sleep and REM sleep. The bodily movement and respiration movement signals may be derived from one or more sensors, such as non-invasive sensor (e.g., a non-contact radio-frequency motion sensor or a pressure sensitive mattress).

Abstract: Disclosed is a cardio-pulmonary health monitoring apparatus. The apparatus comprises a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session; a processor; and a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals. The method comprises extracting one or more sleep disordered breathing features from the one or more movement signals, and predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

Abstract: A processing system includes methods to promote sleep. The system may include a monitor such as a non-contact motion sensor from which sleep information may be determined. User sleep information, such as sleep stages, hypnograms, sleep scores, mind recharge scores and body scores, may be recorded, evaluated and/or displayed for a user. The system may further monitor ambient and/or environmental conditions corresponding to sleep sessions. Sleep advice may be generated based on the sleep information, user queries and/or environmental conditions from one or more sleep sessions. Communicated sleep advice may include content to promote good sleep habits and/or detect risky sleep conditions. In some versions of the system, any one or more of a bedside unit 3000 sensor module, a smart processing device, such as a smart phone or smart device 3002, and network servers may be implemented to perform the methodologies of the system.

Abstract: A sleep monitoring system includes an ECG device (2) and a respiration inductance plethysmogram (3) which monitor cardiac activity and physical (ribcage) respiration respectively and feed representative signals to a digital data processor. Operations (5-9) process the beat interval data, while in a second thread, operations (20-24) independently process the amplitude modulation of the ECG data caused by the respiratory motion of the subject. The inductance plethysmogram device (3) provides an input to the processor which represents respiration as directly monitored independently of the ECG. Operations (30-34) process this direct respiration data independently and in parallel, in a third thread. All extracted features are fed to a classifier which in step (10) combines selected combinations of features to make decisions in real time.

Abstract: Methods and apparatus monitor health by detection of sleep stage. For example, a sleep stage monitor may access sensor data signals related to bodily movement and respiration movements. At least a portion of the detected signals may be analyzed to calculate respiration variability. The respiration variability may include variability of respiration rate or variability of respiration amplitude. A processor may then determine a sleep stage based on a combination bodily movement and respiration variability. The determination of sleep stages may distinguish between deep sleep and other stages of sleep, or may differentiate between deep sleep, light sleep and REM sleep. The bodily movement and respiration movement signals may be derived from one or more sensors, such as non-invasive sensor (e.g., a non-contact radio-frequency motion sensor or a pressure sensitive mattress).

Abstract: Disclosed is a cardio-pulmonary health monitoring apparatus. The apparatus comprises a contactless motion sensor configured to generate one or more movement signals representing bodily movement of a patient during a monitoring session; a processor; and a memory storing program instructions configured to cause the processor to carry out a method of processing the one or more movement signals. The method comprises extracting one or more sleep disordered breathing features from the one or more movement signals, and predicting whether a clinical event is likely to occur during a predetermined prediction horizon based on the one or more sleep disordered breathing features.

Abstract: In an embodiment, a data processing method comprises obtaining one or more photoplethysmography (PPG) signals from one or more PPG sensors of a monitoring apparatus, the PPG signals being generated based upon optically detecting pulsed variations in blood flow; obtaining a motion sensor signal from a motion sensor in the monitoring apparatus; identifying, based upon the motion sensor signal, one or more periods of motion (e.g., low motion) of the monitoring apparatus; and selectively obtaining and storing segments of the PPG signals based on a temporal relationship between the segments of the PPG signals and the identified periods of motion.

Abstract: A processing system includes methods to promote sleep. The system may include a monitor such as a non-contact motion sensor from which sleep information may be determined. User sleep information, such as sleep stages, hypnograms, sleep scores, mind recharge scores and body scores, may be recorded, evaluated and/or displayed for a user. The system may further monitor ambient and/or environmental conditions corresponding to sleep sessions. Sleep advice may be generated based on the sleep information, user queries and/or environmental conditions from one or more sleep sessions. Communicated sleep advice may include content to promote good sleep habits and/or detect risky sleep conditions. In some versions of the system, any one or more of a bedside unit 3000 sensor module, a smart processing device, such as a smart phone or smart device 3002, and network servers may be implemented to perform the methodologies of the system.

Abstract: A sleep monitoring system includes an ECG device and a respiration inductance plethysmogram which monitor cardiac activity and physical respiration respectively and feed representative signals to a digital data processor. Operations process the beat interval data, while in a second thread, operations independently process the amplitude modulation of the ECG data caused by the respiratory motion of the subject. The inductance plethysmogram device provides an input to the processor which represents respiration as directly monitored independently of the ECG. Operations process this direct respiration data independently and in parallel, in a third thread. All extracted features are fed to a classifier which in step combines selected combinations of features to make decisions in real time.

Abstract: A method of monitoring sleep comprises simultaneously recording a person's electrocardiogram (ECG) and photoplethysmogram (PPG), deriving a plurality of parameters from the recorded data, and providing an output indicative of a sleep characteristic based upon an analysis of the parameters. The ECG and PPG may be recorded using an apparatus which is a combination of a Holter monitor and a pulse oximeter, which is wearable in ambulatory manner.

Abstract: A sensor for physiology sensing may be configured to generate oscillation signals for emitting radio frequency pulses for range gated sensing. The sensor 402 may include a radio frequency transmitter configured to emit the pulses and a receiver configured to receive reflected ones of the emitted radio frequency pulses. The received pulses may be processed to detect physiology characteristics such as motion, sleep, respiration and/or heartbeat. In some embodiments, the sensor may employ a circuit including a pulse generator configured to generate signal pulses. The circuit may also include a dielectric resonator oscillator configured to generate a radio frequency oscillating signal. A switched oscillation circuit may be coupled to the pulse generator and the dielectric resonator oscillator. The switched circuit may be configured to generate a pulsed radio frequency oscillating signal for emitting the radio frequency pulses.