A mobile device such as a smart phone, tablet computer, Internet of things (IoT) device, and/or wearable device includes a sensor module coupled to multiple corresponding sensors. The multiple corresponding sensors include multiple instances of the same type of motion sensor in different physical locations on the mobile device. The sensor module includes a software architecture/algorithm that can configure the multiple corresponding sensors to provide sensor measurements having a higher sampling frequency and/or an improved accuracy. In addition, the sensor module may provide sensor measurements having additional degrees of freedom and/or lower power consumption than can be provided by existing sensor modules. In one example, the multiple corresponding sensors include multiple accelerometers that operate at relatively low power to provide linear acceleration samples, the sensor module processes the multiple linear acceleration samples to generate a measure of angular acceleration used to activate the mobile device.

A method of determining a CRF level for a user of a fitness tracking system includes receiving activity data from at least one activity sensor carried by the user during a number of workouts, the activity data including distance data for each of the number of workouts, and then generating workout data based on the activity data. The method further includes storing the workout data in a memory, the memory further including demographic data for the user. When the an attribute of the workout data is less than a threshold number, the method includes determining a first CRF level for the user based on a first CRF model. When the attribute of the workout data is greater than the threshold number, the method includes determining a second CRF level for the user based on a second CRF model, and displaying the first and second CRF levels.

Provided is a display control method for a display control device including a processor and a storage. The method includes generating first display data for a display to display user marks at intervals so as to be added one by one as a time elapses from a timing. The intervals each correspond to a user's step length of a user obtained based on measured data on running or walking of the user. The user marks each represent a position where a foot of the user lands by one step. The method further includes generating second display data for the display to display reference marks at intervals so as to be added one by one as the time elapses. The intervals each correspond to a predetermined reference step length. The reference marks each represent a position where a reference foot lands by one step.

A health monitoring system comprises at least one sensor configured to measure at least one parameter relating to a condition of a subject; and a health monitoring module in communication with the at least one sensor and configured to receive from the at least one sensor a measurement of the at least one parameter; based on the measurement, generate a message; and send the message to a health operator related to the subject. Moreover, in some embodiments, the health monitoring module is further configured to determine a confidence factor for the measurement. Further, in some embodiments the health monitoring module is also configured to receive from the at least one sensor a plurality of measurements of the at least one parameter; derive a trend for the plurality of measurements; and based on the trend, determine a health state for the subject.

Exemplary embodiments are disclosed of systems and methods for monitoring for and lowering the risk of addiction-related or restriction violation-related behavior(s).

Embodiments are disclosed for an electrical bicycle detector for energy expenditure estimation. In an embodiment, a method comprises: determining heart rate energy expenditure of a user wearing or holding the device; determining work rate energy expenditure of a user wearing or holding the device; determining a probability that the user is riding an electrical bike based on the heart rate energy expenditure and the work rate energy expenditure; determining whether or not the probability meets a condition corresponding to a threshold probability; and in accordance with the probability meeting the condition corresponding to the threshold probability: adjusting the work rate energy expenditure; and generating fitness data based at least on the adjusted work rate energy expenditure.

A computer-implemented system and associated methods are disclosed including a device for personalized activity monitoring using the hands The device is worn about a wrist and captures images along the wrist including movement of the hands to monitor predetermined hand movements relative to an object of interest.

Various embodiments described herein relate to a method and related wearable device and non-transitory machine-readable medium including receiving sensor data from at least one sensor of the wearable device; comparing the sensor data to schedule format stored in a memory of the wearable device, wherein the schedule format specifies at least one characteristic of sensor readings previously associated with a predefined context; determining that the received sensor data matches the schedule format; determining, based on the received sensor data matching the schedule format, that the user is currently in the predefined context; identifying an action associated with the predefined context; and executing the action while the user is in the predefined context.

The present invention is directed to a wearable fitness and/or heart rate monitoring device which includes motion detecting sensor(s), a galvanic skin response (GSR) sensor, a user input interface, or any combination thereof. The device accounts for signal noise, produced when an individual passes thru varying exercise states, by filtering out the motion of the individual using a 3-axis accelerometer. The invention also provides for systems and methods of operating the monitor which extracts and/or stores true or correct heart rate values from the heartbeat waveform signal observed from a PPG.

An electronic device and method for data-driven assistance for users involved in physical activities is provided. The electronic device receives first sensor data associated with a movement pattern of one or more parts of a body of a user and receives first information associated with a location where the user performs the physical activity. The electronic device determines one or more first indicators which are likely to have affected the user or the performance of the user in the physical activity. Thereafter, the electronic device generates presentation data based on an application of a first machine learning model on the determined one or more first indicators and the received first information. The generated presentation data includes one or more improvement suggestions for the user in relation to the physical activity. The electronic device controls a display device to display the presentation data.