A set of wearable devices, each paired with a companion device, are configured to cumulatively maintain data about physical activities even though a user may switch between wearing them over a period of time. For example, a streak of workouts over many days can be maintained while the user switches between two watches over that period of time.

Technologies for pedometric sensing in footwear include a step tracker compute device. The step tracker compute device is to receive acceleration data indicative of movement of a foot of a user, generate energy contour data indicative of energy levels over time, based on the received acceleration data, determine dynamic energy thresholds indicative of peaks in the energy contour data, and detect steps of the user based on the dynamic energy thresholds and the energy contour data to generate step data. Other embodiments are described and claimed.

Apparatus and methods are provided for determining, recommending, and applying a calibration parameter to collected activity data. In one embodiment, calibration parameter is estimated based on physical aspects of the user and automatically applied to collected data. In another embodiment, the calibration parameter is determined based on secondary data which is more precise than the data which is collected. The calibration factor based on the more precise data may comprise a recommended calibration factor, yet the user may be enabled to select any calibration factor he/she prefers via an interactive display. In one specific variant, the activity comprises a walk or run activity of the user, and the calibration parameter comprises the user's specific stride length. In another variant, the user selects a calibration factor by reviewing a list of previous activity against that same activity after calibration given a particular calibration factor is applied.

A step counting apparatus for a treadmill includes a terminal device. The terminal device is placed on the treadmill. The terminal device includes an acceleration sensor, a processor and a memory. The memory is configured to store a software program. The acceleration sensor is configured to collect a vibration signal generated when a user runs on the treadmill. The processor is configured to execute the software program stored in the memory, to cause the processor to instruct the acceleration sensor to collect the vibration signal generated when the user runs on the treadmill. The processor is further configure to execute the software program to analyze, using a time-frequency analysis algorithm, the vibration signal to determine a quantity of exercise steps of the user on the treadmill.

The present disclosure provides an electronic apparatus. The electronic apparatus includes a sensor and a processor electrically connected to the sensor and configured to generate information indicating a start of an activity when first data indicating that an activity of the electronic apparatus occurs is received from the sensor and generate information indicating a start of an exercise when second data indicating that the activity is associated with the exercise is received from the sensor.

A system for determining queues and reducing queue times for a location determines the number of users that are in a queue based on sensor readings from a device of the users and interactions of the devices with a geofence. The system may determine the queue times based on the number of devices it determines are associated with users in a queue and a rate associated with the movement of the queue. The determined queue times may be displayed on one or more user devices that are inquiring about the location.

A mobile terminal detects a self-device being located at a predetermined location, and stops reception of sensing information from a module device that transmits the sensing information, if being located at the predetermined location is detected. A fixed device receives the sensing information from the module device, and transmits the sensing information to a cloud server.

A fitness tracking system for generating movement variables corresponding to movement of a user includes a monitoring device, a personal electronic device, and a remote processing server. The monitoring device is configured to be worn or carried by the user and includes a movement sensor configured to collect movement data. The personal electronic device is operably connected to the monitoring device. At least one of the personal electronic device and the monitoring device is configured to calculate feature data by applying a set of rules to the movement data, to calculate raw speed data corresponding to a speed of the user from the subset of the movement data, and to calculate raw distance data corresponding to a distance moved by the user from the subset of the movement data. The remote processing server includes a machine learning model for processing at least the feature data.

A method implemented by one or more processors may include determining a rotation between a client device frame and a world frame, determining a rotation between an average gravity aligned (AGA) frame of the client device and the client device frame, performing step detection of the client device, and determining a change in orientation from a first detected step to a second detected step. In one example, computing the change in orientation includes determining a rotation between a horizontally projected AGA (HPAGA) frame and the AGA frame, determining a rotation between the world frame and the HPAGA frame, and determining the change in orientation by using the rotation between the world frame and the HPAGA frame. The method may also include determining, using the computed change in orientation, pedestrian dead reckoning data of the client device over a time period, and determining an output location estimate of the client device using the pedestrian dead reckoning data.

According to various embodiments, a step counter device may be provided. The step counter device may include an accelerometer configured to determine an acceleration in each axis of a plurality of axes; a dominant axis determination circuit configured to determine one axis of the plurality of axis as a dominant axis based on determining, for each axis of the plurality of axis, a plurality of criteria for the respective axis; a step number determination circuit configured to determine a number of steps based on determining the plurality of criteria for the dominant axis; wherein the criteria for a respective axis may include a criterion indicating whether a frequency of acceleration in the respective axis is within a first pre-determined range, a criterion indicating whether a variation in the frequency of acceleration in the respective axis between various cycles is within a second pre-determined range; a criterion indicating whether a variation in the amplitude of acceleration in the respective axis between various cycles is within a third pre-determined range; and a criterion indicating whether the acceleration in the respective axis is higher than a pre-determined threshold.