Patent classifications
A61B5/113
System and method for determining sleep stage
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).
System and method for determining sleep stage
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).
Method and system for unattended child detection
A radar sensor system and method for ascertaining whether an unattended child is present within an automotive vehicle. The radar sensor system carries out the method and includes a transmitter, at least one sensor, and processing circuitry. The method includes the steps of: illuminating at least one occupiable position within the vehicle with radiation of multiple frequencies; generating radar sensor signals from reflections of the transmitted radiation, a plurality of the radar sensor signals corresponding to different frequencies; and operating the processing circuitry for generating and determining if a first indicator value indicative of motion in the occupiable position satisfies a first predetermined criteria and, if so, generating and determining a second indicator value indicating a degree of repetitive pattern within the radar sensor signals, and determining presence of an unattended child in the vehicle if the second indicator value satisfies a second predetermined criteria.
Method and system for unattended child detection
A radar sensor system and method for ascertaining whether an unattended child is present within an automotive vehicle. The radar sensor system carries out the method and includes a transmitter, at least one sensor, and processing circuitry. The method includes the steps of: illuminating at least one occupiable position within the vehicle with radiation of multiple frequencies; generating radar sensor signals from reflections of the transmitted radiation, a plurality of the radar sensor signals corresponding to different frequencies; and operating the processing circuitry for generating and determining if a first indicator value indicative of motion in the occupiable position satisfies a first predetermined criteria and, if so, generating and determining a second indicator value indicating a degree of repetitive pattern within the radar sensor signals, and determining presence of an unattended child in the vehicle if the second indicator value satisfies a second predetermined criteria.
HEART VALVE DYSFUNCTION DETECTION
A process of monitoring heart valve function involves placing one or more transducers on a patient's body, receiving, using the one or more transducers, one or more signals indicating a blood flow velocity profile associated with a heart of the patient, identifying a first peak in the blood flow velocity profile, and determining a severity of a dysfunction of a first heart valve of the heart based on the first peak.
HEART VALVE DYSFUNCTION DETECTION
A process of monitoring heart valve function involves placing one or more transducers on a patient's body, receiving, using the one or more transducers, one or more signals indicating a blood flow velocity profile associated with a heart of the patient, identifying a first peak in the blood flow velocity profile, and determining a severity of a dysfunction of a first heart valve of the heart based on the first peak.
Detecting, quantifying, and/or classifying seizures using multimodal data
A method, comprising receiving at least one of a signal relating to a first cardiac activity and a signal relating to a first body movement from a patient; triggering at least one of a test of the patient's responsiveness, awareness, a second cardiac activity, a second body movement, a spectral analysis test of the second cardiac activity, and a spectral analysis test of the second body movement, based on at least one of the signal relating to the first cardiac activity and the signal relating to the first body movement; determining an occurrence of an epileptic event based at least in part on said one or more triggered tests; and performing a further action in response to said determination of said occurrence of said epileptic event. Further methods allow classification of epileptic events. Apparatus and systems capable of implementing the method.
Detecting, quantifying, and/or classifying seizures using multimodal data
A method, comprising receiving at least one of a signal relating to a first cardiac activity and a signal relating to a first body movement from a patient; triggering at least one of a test of the patient's responsiveness, awareness, a second cardiac activity, a second body movement, a spectral analysis test of the second cardiac activity, and a spectral analysis test of the second body movement, based on at least one of the signal relating to the first cardiac activity and the signal relating to the first body movement; determining an occurrence of an epileptic event based at least in part on said one or more triggered tests; and performing a further action in response to said determination of said occurrence of said epileptic event. Further methods allow classification of epileptic events. Apparatus and systems capable of implementing the method.
Computer-based system for educating a baby and methods of use thereof
A system includes a memory, an optical subsystem, an audio system, a plurality of sensors outputting sensor data, a communication circuitry, and a processor. The processor is configured to input to a baby-specific educational machine learning model, image data, audio signal data, sensor data, baby personal data associated with a baby, and a visual image and a sound presented to the baby based on a baby-specific educational plan; to receive an output from the baby-specific educational machine learning model where the output includes an indication that the baby understood or did not understand the visual image and the sound associated with the baby-specific educational plan, and a baby-specific educational recommendation based on the indication; and to execute based on the baby-specific educational recommendation, a modification of the baby-specific educational plan, or a continued execution of the baby-specific educational plan.
Computer-based system for educating a baby and methods of use thereof
A system includes a memory, an optical subsystem, an audio system, a plurality of sensors outputting sensor data, a communication circuitry, and a processor. The processor is configured to input to a baby-specific educational machine learning model, image data, audio signal data, sensor data, baby personal data associated with a baby, and a visual image and a sound presented to the baby based on a baby-specific educational plan; to receive an output from the baby-specific educational machine learning model where the output includes an indication that the baby understood or did not understand the visual image and the sound associated with the baby-specific educational plan, and a baby-specific educational recommendation based on the indication; and to execute based on the baby-specific educational recommendation, a modification of the baby-specific educational plan, or a continued execution of the baby-specific educational plan.