Abstract: Some embodiments provide a wearable monitoring device including a motion sensor and a photo (PPG) sensor. The PPG sensor includes (i) a periodic light source, (ii) a photo detector, and (iii) circuitry determining a user's heart rate from an output of the photo detector. Some embodiments provide methods for operating a heart rate monitor of a wearable monitoring device to measure one or more characteristics of a heartbeat waveform. Some embodiments provide methods for operating the wearable monitoring device in a low power state when the device determines that the device is not worn by a user.

Abstract: Some embodiments relate to a device, method, and/or computer-readable medium storing processor-executable process steps for processing a photoplethysmographic (“PPG”) signal in a monitoring device that monitors a property of blood flow. In some embodiments, the processing includes obtaining a first digital signal representing a detected light signal having a non-pulsatile (e.g., DC) component and a pulsatile component (e.g., AC). An offset control signal is generated from an estimation of the non-pulsatile component and a second digital signal is generated after subtracting the offset control signal from the detected light signal and applying a gain to the subtracted signal. A reconstructed signal is generated that is calculated from the gain and one or more of (i) the first digital signal, and (ii) the second digital signal and the offset control signal.

Abstract: An activity monitoring device, methods and computer readable media are provided. The activity monitoring device includes a housing configured for attachment to a body part of a user and a display screen attached to the housing. Further included is a first sensor disposed in the housing for capturing motion of the activity monitoring device when attached to the body part of the user and a second sensor disposed in the housing for sampling a heart rate of the user. Memory is disposed in the housing for storing the motion captured by the first sensor and the heart rate sampled by the second sensor. A processor is disposed in the housing and is configured to determine a physical state of the user during a period of time. For motion that is below a threshold the processor identifies the physical state to be a sedentary state and for motion that is at or above the threshold the processor identifies the physical state to be an active state.

Abstract: The disclosure provides BMDs that have multiple device modes depending on operational conditions of the devices, e.g., motion intensity, device placement, and/or activity type, the device modes are associated with various data processing algorithms. In some embodiments, the BMD is implemented as a wrist-worn or arm-worn device. In some embodiments, methods for tracking physiological metrics using the BMDs are provided. In some embodiments, the process and the BMD applies a time domain analysis on data provided by a sensor of the BMD when the data has a high signal (e.g., high signal-to-noise ratio), and applies a frequency domain analysis on the data when the data has a low signal, which contributes to improved accuracy and speed of biometric data.

Abstract: The disclosure provides BMDs that have multiple device modes depending on operational conditions of the devices, e.g., motion intensity, device placement, and/or activity type, the device modes are associated with various data processing algorithms. In some embodiments, the BMD is implemented as a wrist-worn or arm-worn device. In some embodiments, methods for tracking physiological metrics using the BMDs are provided. In some embodiments, the process and the BMD applies a time domain analysis on data provided by a sensor of the BMD when the data has a high signal (e.g., high signal-to-noise ratio), and applies a frequency domain analysis on the data when the data has a low signal, which contributes to improved accuracy and speed of biometric data.

Abstract: Approaches to determining a sleep fitness score for a user are provided, such as may be based upon monitored breathing disturbances of a user. The system receives user state data generated over a time period by a combination of sensors provided via a wearable tracker associated with the user. A system can use this information to calculate a sleep fitness score, breathing disturbance score, or other such value. The system can classify every minute within the time period as either normal or atypical, for example, and may provide such information for presentation to the user.

Abstract: In one aspect of the disclosed implementations, a device includes one or more motion sensors for sensing motion of the device and providing activity data indicative of the sensed motion. The device also includes one or more processors for monitoring the activity data, and receiving or generating annotation data for annotating the activity data with one or more markers or indicators to define one or more characteristics of an activity session. The device also includes one or more feedback devices for providing feedback, a notice, or an indication to a user based on the monitoring. The device further includes a portable housing that encloses at least portions of the motion sensors, the processors and the feedback devices.

Abstract: Biometric monitoring devices, including various technologies that may be implemented in such devices, are discussed herein. Additionally, techniques, systems, and apparatuses are discussed herein for providing power-conserving techniques and systems for efficiently utilizing a GPS receiver are described. The positional fix frequency of the GPS receiver may, according to some implementations, be modified or adjusted between various levels according to data from one or more non-GPS sensors. Such non-GPS sensors may include, for example, ambient light intensity or spectrum sensors, accelerometers, gyroscopes, magnetometers, heart rate sensors, galvanic skin response sensors, infrared sensors, etc.

Abstract: The disclosure relates to methods, devices, and systems to identify a user of a wearable fitness monitor using data obtained using the wearable fitness monitor. Data obtained from motion sensors of the wearable fitness monitor and data obtained from heartbeat waveform sensors of the wearable fitness monitor may be used to identify the user.

Abstract: Biometric monitoring devices, including various technologies that may be implemented in such devices, are discussed herein. Additionally, techniques for enhancing GPS speed and distance measurements based on step counts measured by a biometric monitoring device are provided. Such techniques may, in some implementations, involve blending two independently-measured data streams of raw distance traveled—one based on inter-coordinate GPS coordinate distances and one based on step count and stride length—using a Kalman filter to provide an enhanced-accuracy measurement of distance or speed traveled. In some other or additional implementations, distances or speeds calculated based on inter-coordinate GPS coordinate distances may be smoothed with a smoothing constant that is proportional to the variance in measured step count.

Abstract: In one embodiment, an electronic device to be worn on a user's forearm includes a display and a set of one or more sensors that provide sensor data. In one aspect, a device may detect, using sensor data obtained from a set of sensors, that a first activity state of a user is active. The device may determine, while the first activity state is active, that the sensor data matches a watch check rule associated with the first activity state. Responsive to the detected match, the device may cause a change in visibility of the display.

Abstract: Techniques for automatic tracking of user data for exercises are disclosed. In one aspect, a method of operating a wearable device may involve determining, based on output of one or more biometric sensors, that a user of the wearable device has started an exercise and identifying a type of the exercise that the user has started based on comparing the output of the one or more biometric sensors to defined sensor data for a plurality of exercise types. The method may further include adjusting a global positioning system (GPS) receiver in response to determining the start of the exercise and based on the type of the exercise.

Abstract: A method and apparatus for capacitive off-wrist detection for wearable device are disclosed. In one aspect, the wearable device includes one or more biometric sensors including a capacitive sensor. The method may involve measuring, based on output of the capacitive sensor, a capacitance value indicative of proximity of the wearable device to a user. The method may also involve detecting a change in the capacitance value within a defined time interval, the change being greater than or equal to a threshold change indicative of the wearable device not being proximate to the user's skin. The method may further involve determining that the wearable device has been removed from the user in response to detecting that the change in the capacitance value within the defined time interval is greater than or equal to the threshold change.

Abstract: A biometric monitoring device is used to determine a user's heart rate by using a heartbeat waveform sensor and a motion detecting sensor. In some embodiments, the device collects output data from the heartbeat waveform sensor and output data from the motion detecting sensor, determines an expected heart rate range from the output data from the motion detecting sensor, and determines the user's heart rate within the expected heart rate range using the output data from the heartbeat waveform sensor.

Abstract: An activity monitoring device accumulates a count of steps taken as an individual bearing the activity monitoring device travels by foot along a route, and wirelessly acquires positioning data from a discrete global-positioning device corresponding to a sequence of locations along the route. The activity monitoring device iteratively revises an estimated stride length of the individual based on the positioning data and the step count and iteratively updates a travel distance on a display of the activity monitoring device according to the step count and the estimated stride length as the individual proceeds from one location in the sequence to the next.

Abstract: In one aspect of the disclosed implementations, a device includes one or more motion sensors for sensing motion of the device and providing activity data indicative of the sensed motion. The device also includes one or more processors for monitoring the activity data, and receiving or generating annotation data for annotating the activity data with one or more markers or indicators to define one or more characteristics of an activity session. The device also includes one or more feedback devices for providing feedback, a notice, or an indication to a user based on the monitoring. The device further includes a portable housing that encloses at least portions of the motion sensors, the processors and the feedback devices.

Abstract: The disclosure relates to methods, devices, and systems to identify a user of a wearable fitness monitor using data obtained using the wearable fitness monitor. Data obtained from motion sensors of the wearable fitness monitor and data obtained from heartbeat waveform sensors of the wearable fitness monitor may be used to identify the user.

Abstract: Assisted-GPS for a portable biometric monitoring device is provided. The portable biometric monitoring device may obtain updated ephemeris data from an associated secondary device via a short-range, low-power communication protocol. The secondary device may be a computing device such as a smartphone, tablet, or laptop. Various rules may control when the ephemeris data is updated. The ephemeris data may be used in the calculation of the global position of the portable biometric monitoring device. Additionally, the portable biometric monitoring device may communicate downloaded position fixing data to the associated secondary device. The associated secondary device may then calculate the global position from the position fixing data.

Abstract: A method includes receiving location data of a monitoring device when carried by a user and receiving motion data of the monitoring device. The motion data is associated with a time of occurrence and the location data. The method includes processing the received motion data to identify a group of the motion data having a substantially common characteristic and processing the location data for the group of the motion data. The group of motion data by way of processing the location data provides an activity identifier. The motion data includes metric data that identifies characteristics of the motion data. The method includes transferring the activity identifier and the characteristics of the motion data to a screen of a device for display. The activity identifier being a graphical user interface that receives an input for rendering more or less of the characteristics of the motion data.

Abstract: An activity monitoring device, methods and computer readable media are provided. The activity monitoring device includes a housing configured for attachment to a body part of a user and a display screen attached to the housing. Further included is a first sensor disposed in the housing for capturing motion of the activity monitoring device when attached to the body part of the user and a second sensor disposed in the housing for sampling a heart rate of the user. Memory is disposed in the housing for storing the motion captured by the first sensor and the heart rate sampled by the second sensor. A processor is disposed in the housing and is configured to determine a physical state of the user during a period of time. For motion that is below a threshold the processor identifies the physical state to be a sedentary state and for motion that is at or above the threshold the processor identifies the physical state to be an active state.