Abstract: Embodiments of a system and method for heart rate monitoring are generally described herein. A method may include receiving, at a device, a spectrogram including a plurality of bins of represented frequencies corresponding to potential heart rate measurements, selecting a bin of frequencies from the plurality of bins of represented frequencies with an average power that exceeds average powers of remaining bins of frequencies of the plurality of bins of represented frequencies, determining whether a frequency of the bin of frequencies represents a valid heart rate based on the average power and a past valid heart rate, and in response to determining that the frequency represents a valid heart rate, outputting a heart rate corresponding to the frequency.

Abstract: Embodiments of a system and method for heart rate monitoring are generally described herein. A method may include receiving, at a device, a spectrogram including a plurality of bins of represented frequencies corresponding to potential heart rate measurements, selecting a bin of frequencies from the plurality of bins of represented frequencies with an average power that exceeds average powers of remaining bins of frequencies of the plurality of bins of represented frequencies, determining whether a frequency of the bin of frequencies represents a valid heart rate based on the average power and a past valid heart rate, and in response to determining that the frequency represents a valid heart rate, outputting a heart rate corresponding to the frequency.

Abstract: Systems and methods for recognizing a gesture in a wearable device are disclosed. The system may sense a plurality of sensor measurements during a gesture sensing session, and down-sample the measurements using an adaptive down-sampling interval. The adaptive down-sampling interval may be determined based at least on a fractional part of a ratio of a frame length of the sensor measurements to a specified target length shorter than the frame length. The magnitude of the down-sampled measurements is normalized, and a feature vector may be generated using the normalized measurements. A gesture recognizer module may associate a gesture with the feature vector using gesture classification.

Abstract: Embodiments are directed to gesture recognition in a computing device. Touch-based input by the user is monitored based on an output from a touch sensor. Gestures are directed from among the touch-based input. The detected gestures are analyzed to assign gesture characteristic profiles to the detected gestures according to profiling criteria. A sequential event log is tabulated representing counts of series of gestures based on assigned characteristic profiles and on temporal sequencing of the gestures. Circumstances for invocation of gesture detection re-calibration are assessed based on the tabulated series of gestures.

Abstract: Embodiments are directed to gesture recognition in a computing device. Touch-based input by the user is monitored based on an output from a touch sensor. Gestures are directed from among the touch-based input. The detected gestures are analyzed to assign gesture characteristic profiles to the detected gestures according to profiling criteria. A sequential event log is tabulated representing counts of series of gestures based on assigned characteristic profiles and on temporal sequencing of the gestures. Circumstances for invocation of gesture detection re-calibration are assessed based on the tabulated series of gestures.