Abstract: A wearable device automatically identifies an activity in which a user of the device is engaged while wearing the device. The wearable device receives motion data of the user collected during the activity and determines a magnitude and a frequency of the motion data. Based on the magnitude and frequency of the motion data, the wearable device identifies the activity in which the user is engaged.

Abstract: Disclosed herein are systems, methods and a computer readable storage medium that determine an individual's movement during sleep. In some embodiments, the identification is based on data from a motion sensor included in wearable device. In some embodiments, the individual wears the wearable device on his wrist during the individual's resting and/or sleep activities. In some embodiments, the system, methods and the computer readable storage medium automatically identify the various (stages) types of sleep during an individual's sleep periods that include periods of REM sleep, deep sleep, and light sleep and that are indicative of the individual's overall well-being and health.

Abstract: A wearable device automatically identifies an activity in which a user of the device is engaged while wearing the device. The wearable device receives motion data of the user collected during the activity and determines a magnitude and a frequency of the motion data. Based on the magnitude and frequency of the motion data, the wearable device identifies the activity in which the user is engaged.

Abstract: A wearable device automatically identifies an activity in which a user of the device is engaged while wearing the device. The wearable device receives motion data of the user collected during the activity and determines a magnitude and a frequency of the motion data. Based on the magnitude and frequency of the motion data, the wearable device identifies the activity in which the user is engaged.

Abstract: Disclosed herein are systems, methods and a computer readable storage medium that determine an individual's movement during sleep. In some embodiments, the identification is based on data from a motion sensor included in wearable device. In some embodiments, the individual wears the wearable device on his wrist during the individual's resting and/or sleep activities. In some embodiments, the system, methods and the computer readable storage medium automatically identify the various (stages) types of sleep during an individual's sleep periods that include periods of REM sleep, deep sleep, and light sleep and that are indicative of the individual's overall well-being and health.

Abstract: An optical pulse rate detector includes at least one emitter, a photodetector, and an adaptive filter. The emitter transmits light at a first light output level and a second light output level into body tissue of a user. The photodetector receives reflected light from the body tissue and produces a voltage indicative of an amount of the reflected light. The adaptive filter receives a first output signal from the photodetector indicative of an amount of the light at the first light output level reflected from the body tissue and a second output signal indicative of an amount of the light at the second output level reflected from the body tissue. The adaptive filter removes the second output signal from the first output signal to generate a pulse rate signal indicative of a pulse rate of the user.

Abstract: A computing device analyzes data taken over a period of time using a data transform. Frequency components are extracted by converting the data to a binary signal and computing an approximate inner product between the converted data and one or more basis functions. To compute the inner product between the converted data and a basis function, an exclusive nor (XNOR) function is used to perform element-wise multiplication between the converted data and the basis function, and a lookup table is used to sum the resulting products. Each resulting sum corresponds to an approximation of the basis function's strength in the data. When binary sine and cosine functions are used, the method approximates the discrete Fourier transform. Other basis functions may be used to approximate various transforms.

Abstract: A wearable device automatically identifies an activity in which a user of the device is engaged while wearing the device. The wearable device receives motion data of the user collected during the activity and determines a magnitude and a frequency of the motion data. Based on the magnitude and frequency of the motion data, the wearable device identifies the activity in which the user is engaged.