Abstract: Device-based activity classification using predictive feature analysis is described, including evaluating an indicator associated with a predictive feature, identifying an application, using the name, to be performed, and invoking the application, the application being configured to interpret the indicator to determine an operation to perform at one or more levels of a protocol stack using data generated from evaluating a signal detected by a sensor, the sensor being coupled to a wearable device, and the application being configured to perform the operation using other data generated from evaluating another signal detected by another sensor, the another sensor being substantially different than the sensor.

Abstract: Device-based activity classification using predictive feature analysis is described, including receiving a signal from a sensor coupled to a device, the sensor being configured to detect the signal over a time period and to detect motion, evaluating the signal to generate data, the data being used to indicate motion, the data being further evaluated to select a classifier based on whether the motion is detected, activating another sensor coupled to the device, the another sensor being configured to detect another signal that is substantially different than the signal, the another signal being used to generate other data associated with whether the motion is detected, invoking the classifier, the classifier being configured to evaluate a predictive feature to identify a type associated with whether the motion is detected, the predictive feature invoking an application configured to determine the type and a state using a feature interpreter, and processing the data using the application and the feature interpre

Abstract: Device-based activity classification using predictive feature analysis is described, including receiving a signal from a sensor coupled to a device, the sensor being configured to sense the signal over a time period, evaluating the signal to generate data, the data being further evaluated to select a classifier, invoking the classifier, the classifier being configured to evaluate a predictive feature, the predictive feature invoking an application configured to determine a state using a feature interpreter, and processing the data using the application and the feature interpreter to generate information associated with a biological state, the information being configured to display on an interface associated with the device.

Abstract: Device-based activity classification using predictive feature analysis is described, including receiving a signal from a sensor configured to measure a heart rate coupled to a device, the sensor being configured to sense the signal over a time period, evaluating the signal to generate data associated with the heart rate, the data being further evaluated to select a classifier, invoking the classifier, the classifier being configured to evaluate the data to identify a predictive feature, the predictive feature invoking an application configured to determine a state using a feature interpreter, the application also being configured to evaluate other data from another signal, the signal being configured to detect a respiration rate, and processing the data and the other data using the application and the feature interpreter to generate information associated with sleep, the information being configured to display on an interface associated with the device.

Abstract: Embodiments relate generally to a wearable device implementing a touch-sensitive interface in a metal pod cover and/or bioimpedance sensing to determine physiological characteristics, such as heart rate. According to an embodiment, a method includes receiving an amplified signal including a portion of the physiological-related signal component including data representing a physiological characteristic, the amplified signal being derived from bioimpedance signal based on an impedance value of a tissue, and identifying a magnitude of a portion of the physiological-related signal component. Also, the method can compare the magnitude of the portion against another magnitude of a data model (e.g., in a time-domain) to form a matched value.

Abstract: Embodiments relate generally to a wearable device implementing a touch-sensitive interface in a metal pod cover and/or bioimpedance sensing to determine physiological characteristics, such as heart rate. According to an embodiment, a wearable device and method includes determining a drive current signal magnitude for a bioimpedance signal to capture data representing a physiological-related component, and selecting the drive current signal magnitude as a function of an impedance of a tissue. Further, the method can include driving the bioimpedance signal to that are configured to convey the bioimpedance signal to the tissue. Also, the method can receive the sensor signal from the tissue, adjust a gain for an amplifier, and apply the gain to data representing the physiological-related component. The method can include generating an amplified signal to include a portion of the physiological-related signal component that includes data representing a physiological characteristic.