Abstract: Methods and devices provide physiological movement detection, such as gesture, breathing, cardiac and/or gross body motion, with active sound generation such as for an interactive audio device. The processor may evaluate, via a microphone coupled to the interactive audio device, a sensed audible verbal communication. The processor may control producing, via a speaker coupled to the processor, a sound signal in a user's vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the generated sound signal from the vicinity or user. The processor may process the reflected sound, such as by a demodulation technique, to derive a physiological movement signal. The processor may generate, in response to the sensed audible verbal communication, an output based on an evaluation of the derived physiological movement signal.

Abstract: A system for predicting adoption of a prescribed treatment plan by an individual includes a data repository, a memory storing instruction, and a control system to execute the instructions. The data repository is communicatively coupled to a network and includes a plurality of storage devices storing data. The control system receives at least a portion of the data stored in the data repository. The at least a portion of the data is associated with the individual. The control system uses the machine learning adoption prediction algorithm to process the received at least a portion of the data to determine a likelihood that the individual will adopt the prescribed treatment plan. Based at least in part on (i) the prescribed treatment plan and (ii) the determined likelihood that the individual will adopt the prescribed treatment plan, the control system generates a personalized treatment adoption plan for the individual.

Abstract: Methods and devices provide physiological movement detection, such as breathing, cardiac and/or gross body motion, with active sound generation using electronic processing device(s). The processor may control producing, via a speaker coupled to the processor, a sound signal in a user's vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the sound signal from the vicinity or user. The processor may process the reflected sound, such as by a demodulation technique. The sound signal may be produced as a dual tone frequency modulation continuous wave signal. Evaluation of detected movement information may determine sleep states or scoring, fatigue indications, subject recognition, chronic disease monitoring/prediction, and other output parameters.

Abstract: Intelligent systems and methods for facilitating insomnia therapy are disclosed. Sensor data (e.g., non-contact sensor data) can be used to determine physiological parameter(s), such as sleep-related physiological parameter(s), which can be used to generate a sleep disorder prediction. The sleep disorder prediction can be used, along with an identified sleep therapy plan, to generate and facilitate application of (e.g., present to a user or automatically apply) a sleep therapy recommendation. When sleep apnea is predicted in concert with an identified cognitive behavioral therapy for insomnia (CBTi) plan, a warning can be presented to the user to not engage in certain CBTi therapies. Sensor data can also be used to automatically update therapy parameter(s) of an ongoing sleep therapy plan, such as in realtime.

Abstract: A system for predicting adoption of a prescribed treatment plan by an individual includes a data repository, a memory storing instruction, and a control system to execute the instructions. The data repository is communicatively coupled to a network and includes a plurality of storage devices storing data. The control system receives at least a portion of the data stored in the data repository. The at least a portion of the data is associated with the individual. The control system uses the machine learning adoption prediction algorithm to process the received at least a portion of the data to determine a likelihood that the individual will adopt the prescribed treatment plan. Based at least in part on (i) the prescribed treatment plan and (ii) the determined likelihood that the individual will adopt the prescribed treatment plan, the control system generates a personalized treatment adoption plan for the individual.

Abstract: A wearable device can automatically switch between modes of collecting sensor data when a docking event is detected between the wearable device and a docking device. In a first mode (e.g., when undocked), data can be collected using a first sensor configuration (e.g., a first set of sensors operating using a first set of sensing parameters). In a second mode (e.g., when docked), data can be collected using a second sensor configuration, which can include the use of one or more different sensors and/or the use of one or more different sensing parameters. The first mode may prioritize battery life, whereas the second mode may prioritize sensor data fidelity, such as by increasing sampling rates, using different sensors, and the like. Sensor data from the first and second modes can be used individually (e.g., to calibrate the other) and/or together (e.g., to determine physiological parameters).

Abstract: Methods and devices provide physiological movement detection, such as gesture, breathing, cardiac and/or gross body motion, with active sound generation such as for an interactive audio device. The processor may evaluate, via a microphone coupled to the interactive audio device, a sensed audible verbal communication. The processor may control producing, via a speaker coupled to the processor, a sound signal in a user's vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the generated sound signal from the vicinity or user. The processor may process the reflected sound, such as by a demodulation technique, to derive a physiological movement signal. The processor may generate, in response to the sensed audible verbal communication, an output based on an evaluation of the derived physiological movement signal.

Abstract: Methods and devices provide physiological movement detection, such as gesture, breathing, cardiac and/or gross body motion, with active sound generation such as for an interactive audio device. The processor may evaluate, via a microphone coupled to the interactive audio device, a sensed audible verbal communication. The processor may control producing, via a speaker coupled to the processor, a sound signal in a user's vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the generated sound signal from the vicinity or user. The processor may process the reflected sound, such as by a demodulation technique, to derive a physiological movement signal. The processor may generate, in response to the sensed audible verbal communication, an output based on an evaluation of the derived physiological movement signal.

Abstract: Various implementations of the present disclosure are directed to systems and methods for managing blood pressure conditions of a user of a respiratory therapy system by controlling operational parameters associated with the respiratory therapy system. The method includes receiving cardiovascular data associated with a user of a respiratory therapy system during a sleep session and determining, based at least in part on the received cardiovascular data, a value of a first physiological parameter associated with the user. The method further includes determining whether the value of the first physiological parameter satisfies a first condition and in response to the first physiological parameter satisfying the first condition, determining a modification of an operational parameter associated with the respiratory therapy system.

Abstract: A method for predicting a subjective comfort level of a user of a respiratory therapy system is disclosed as follows. Data associated with the user of the respiratory therapy system during a therapy session is received. At least one parameter associated with the user is determined based at least in part on a first portion of the received data. A comfort score is determined based at least in part on the determined at least one parameter. The comfort score is indicative of the subjective comfort level of the user of the respiratory therapy system during at least a portion of the therapy session.

Abstract: Methods and devices provide physiological movement detection, such as gesture, breathing, cardiac and/or gross body motion, with active sound generation such as for an interactive audio device. The processor may evaluate, via a microphone coupled to the interactive audio device, a sensed audible verbal communication. The processor may control producing, via a speaker coupled to the processor, a sound signal in a user's vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the generated sound signal from the vicinity or user. The processor may process the reflected sound, such as by a demodulation technique, to derive a physiological movement signal. The processor may generate, in response to the sensed audible verbal communication, an output based on an evaluation of the derived physiological movement signal.

Abstract: Automatic adjustment of an oral appliance, such as a mandibular repositioning device, is disclosed. Sensor data can be received from one or more sensors external to a user using the oral appliance. An adjustment associated with the oral appliance is determined using the sensor data, and an action can be taken to facilitate applying the determined adjustment. Such actions can include transmitting a signal to the oral appliance to effect the adjustment (e.g., using an actuator or onboard electrical stimulator), presenting adjustment parameters to help the user manually make the adjustment, activating actuators in an oral appliance storage receptacle the next time the oral appliance is stored, or other such actions. Adjustments can be made dynamically in real-time or asynchronously (e.g., between sleep sessions).

Abstract: A method of analyzing data related to use of a respiratory therapy system by a user comprises receiving a first type of data, determining a first value of a first parameter based at least in part on the first type of data, identifying a desired second type of data, transmitting a request for consent to receive the second type of data, and determining, based at least in part on the second type of data, a second value of the first parameter, a value of a second parameter, or both. The first type of data and the first parameter are related to the user's use of the respiratory therapy system. The identification of the second type of data is based at least in part on the first type of data, the first value of the first parameter, an accuracy of the first value of the first parameter, or any combination thereof.

Abstract: A method includes receiving first data associated with a first sleep session of a user. The method also includes determining a first set of sleep-related parameters associated with the first sleep session of the user based at least in part on the first data. The method also includes receiving second data associated with a second sleep session of the user. The method also includes determining a second set of sleep-related parameters associated with the second sleep session of the user based at least in part on the second data. The method also includes receiving third data associated with a variable condition. The method also includes causing one or more indications associated with the variable condition and the first sleep session, the second sleep session, or both to be communicated to the user.

Abstract: Methods and devices provide physiological movement detection, such as gesture, breathing, cardiac and/or gross body motion, with active sound generation such as for an interactive audio device. The processor may evaluate, via a microphone coupled to the interactive audio device, a sensed audible verbal communication. The processor may control producing, via a speaker coupled to the processor, a sound signal in a user's vicinity. The processor may control sensing, via a microphone coupled to the processor, a reflected sound signal. This reflected sound signal is a reflection of the generated sound signal from the vicinity or user. The processor may process the reflected sound, such as by a demodulation technique, to derive a physiological movement signal. The processor may generate, in response to the sensed audible verbal communication, an output based on an evaluation of the derived physiological movement signal.

Abstract: A system includes a memory storing a user profile for a user of the system and machine-readable instructions and a control system including one or more processors configured to execute the machine-readable instructions to receive physiological data associated with the user during a sleep session, determine, based at least in part on the received physiological data, a set of sleep-related parameters for the sleep session, subsequent to the sleep session, select one of the set of sleep-related parameters as a targeted parameter, the selection of the targeted parameter being based at least in part on the stored user profile, the set of sleep-related parameters, or both, and cause information to be communicated to the user via a user device, the information being indicative of the targeted parameter, a recommendation associated with improving the targeted parameter for the user in one or more subsequent sleep sessions, or both.

Abstract: A method includes receiving first physiological data associated with a user during a first sleep session. The method also includes receiving second physiological data associated with the user subsequent to the first sleep session and prior to a second sleep session. The method also includes determining a recommended bedtime for the user for the second sleep session based at least in part on the first physiological data, the second physiological data, or both. The method also includes causing an indication of the recommended bedtime for the second sleep session to be communicated to the user via a user device before the recommended bedtime.

Abstract: A system includes a sensor configured to generate data associated with movements of a resident for a period of time, a memory storing machine-readable instructions, and a control system arranged to provide control signals to one or more electronic devices. The control system also includes one or more processors configured to execute the machine-readable instructions to analyze the generated data associated with the movement of the resident, determine, based at least in part on the analysis, a likelihood for a fall event to occur for the resident within a predetermined amount of time, and responsive to the determination of the likelihood for the fall event satisfying a threshold, cause an operation of the one or more electronic devices to be modified.

Abstract: A system for predicting adoption of a prescribed treatment plan by an individual includes a data repository, a memory storing instruction, and a control system to execute the instructions. The data repository is communicatively coupled to a network and includes a plurality of storage devices storing data. The control system receives at least a portion of the data stored in the data repository. The at least a portion of the data is associated with the individual. The control system uses the machine learning adoption prediction algorithm to process the received at least a portion of the data to determine a likelihood that the individual will adopt the prescribed treatment plan. Based at least in part on (i) the prescribed treatment plan and (ii) the determined likelihood that the individual will adopt the prescribed treatment plan, the control system generates a personalized treatment adoption plan for the individual.

Abstract: A method includes receiving, from a first sensor, first physiological data associated with a first sleep session of a user. The method also includes receiving, from a sensor, second physiological data associated with a first sleep session of a user. The method also includes determining a first set of sleep-related parameters associated with the first sleep session of the user based at least in part on the first physiological data. The method also includes determining a second set of sleep-related parameters associated with the first sleep session of the user based at least in part on the second physiological data. The method also includes calibrating the second sensor based at least in part on a comparison between the first set of sleep-related parameters and the second set of sleep-related parameters.