Abstract: Embodiments include methods by which mathematical models are applied to data from at least two interrelated physiological parameters employed in parallel with data gathered from various sources to provide an end user (e.g., individual or group of individuals) or consented third party (e.g., insurer of the user) with an estimate of how the physiological parameters will change in the future and a number of years indicative of the terminal age and/or health and life expectancy of the user. Furthermore, utilizing the translation from a lifestyle intervention (e.g., cessation of smoking or commencement of an exercise program) to the physiological parameters from historical data (e.g., wearable data) may permit display of a lifestyle intervention pattern and a current and planned physiological trajectory. The output may be quantified translation from lifestyle intervention to real time health and life expectancy and may be calculated from past and present data and hypothetical planned interventions.

Abstract: A data acquisition device includes measuring instruments to generate physiological and/or psychological data streams. Microprocessors within the acquisition device process the generated data streams into metrics, which feed into stress function algorithms. Algorithm processing may occur either on the device, or metrics may be communicated via wireless communication for external processing on mobile devices and/or cloud-based platforms. The calculated stress functions inform cloud-based computational systems biology-derived models describing the dynamics of hormones and neurotransmitters released in the body in response to stressful stimuli. Stress hormone levels are quantified using these models, and are used in combination to serve as biologically inspired metrics of acute and chronic stress an individual is experiencing.

Abstract: A physiological monitoring system and method for the determination of at least one physiological parameter using a first low energy physiological sensor to produce a first physiological sensor signal and a high energy physiological sensor to produce a second physiological sensor signal. A motion sensor produces a motion reference signal that a mode selector switch uses to select either the first low energy physiological sensor or the second high energy physiological sensor. Alternatively, a motion related embedded component in the second physiological sensor signal is used by the mode selector switch to select in the first low energy sensor or the second high energy sensor.

Abstract: Embodiments include methods by which mathematical models are applied to data from at least two interrelated physiological parameters employed in parallel with data gathered from various sources to provide an end user (e.g., individual or group of individuals) or consented third party (e.g., insurer of the user) with an estimate of how the physiological parameters will change in the future and a number of years indicative of the terminal age and/or health and life expectancy of the user. Furthermore, utilizing the translation from a lifestyle intervention (e.g., cessation of smoking or commencement of an exercise program) to the physiological parameters from historical data (e.g., wearable data) may permit display of a lifestyle intervention pattern and a current and planned physiological trajectory. The output may be quantified translation from lifestyle intervention to real time health and life expectancy and may be calculated from past and present data and hypothetical planned interventions.

Abstract: The current invention pertains to a method whereby the accuracy of a heart rate prediction gathered from sensor data can be improved during periods when motion corrupts the signal. The model utilized can also be inverted to infer information on the physiological state of a subject, such as real-time energy utilization or physiological load. In addition, this method can also be used to segment the contribution of each energy system, namely the phosphagen system, anaerobic glycolysis and aerobic respiration, to the physiological load experienced by the user. At the core of this approach lies a model describing the dynamic adjustment of human heart rate under varying physiological demands.

Abstract: The claimed invention presents methods for predicting the outcomes of physiological systems in real time using limited data input and computational resources. The claimed invention is comprised of simplified, or abstracted, physiological models, derived from detailed cloud-based computational systems biology models. Abstracted models are capable of utilizing limited data streams, typically obtained from non-invasive data acquisition devices, to accurately estimate, predict and display the outcomes of physiological systems in real time on the device, compared to detailed cloud-based estimations that are computationally demanding and can be used to continuously update abstracted models over time.

Abstract: A data acquisition device includes measuring instruments to generate physiological and/or psychological data streams. Microprocessors within the acquisition device process the generated data streams into metrics, which feed into stress function algorithms. Algorithm processing may occur either on the device, or metrics may be communicated via wireless communication for external processing on mobile devices and/or cloud-based platforms. The calculated stress functions inform cloud-based computational systems biology-derived models describing the dynamics of hormones and neurotransmitters released in the body in response to stressful stimuli. Stress hormone levels are quantified using these models, and are used in combination to serve as biologically inspired metrics of acute and chronic stress an individual is experiencing.

Abstract: A data acquisition device includes measuring instruments to generate physiological and/or psychological data streams. Microprocessors within the acquisition device process the generated data streams into metrics, which feed into stress function algorithms. Algorithm processing may occur either on the device, or metrics may be communicated via wireless communication for external processing on mobile devices and/or cloud-based platforms. The calculated stress functions inform cloud-based computational systems biology-derived models describing the dynamics of hormones and neurotransmitters released in the body in response to stressful stimuli. Stress hormone levels are quantified using these models, and are used in combination to serve as biologically inspired metrics of acute and chronic stress an individual is experiencing.

Abstract: A physiological monitoring system and method for the determination of at least one physiological parameter using a first low energy physiological sensor to produce a first physiological sensor signal and a high energy physiological sensor to produce a second physiological sensor signal. A motion sensor produces a motion reference signal that a mode selector switch uses to select either the first low energy physiological sensor or the second high energy physiological sensor. Alternatively, a motion related embedded component in the second physiological sensor signal is used by the mode selector switch to select in the first low energy sensor or the second high energy sensor.