Method and apparatus for calibrating a user activity model used by a mobile device

An electronic device, comprising:

• a sensor;
• a processor;
• and
• a memory that stores executable instructions thereon,
• wherein the processor, when executing the executable instructions stored in the memory, is configured to
• receive sensor data from the sensor, wherein the sensor is operable to detect a plurality of different activities,
• from the sensor, collect training data associated with the plurality of different activities,
• generate a weight for each of the different activities based on the training data,
• input the sensor data into a general model of the electronic device that outputs a likelihood result for each of the plurality of different activities, wherein the general model was trained based on sensor data from a plurality of different electronic devices detecting the plurality of different activities,
• apply each weight for each of its corresponding one of the different activities to adjust its corresponding likelihood result without modifying the general model and without retraining the general model,
• determine a first one of the different activities is being performed by the electronic device by selecting a highest value of the adjusted likelihood results, and
• perform an action on the electronic device based on a determination of the first activity, wherein the action includes turning on step detection and/or location detection for the electronic device in a case that the first activity is movement based activity, and turning off the step detection and/or location detection in a case that the first activity is a resting activity.

A non-transitory computer-readable storage medium, storing computer-readable instructions thereon which, when executed by an electronic device, cause the electronic device to perform a method, the method comprising:

• receiving sensor data from a sensor of an electronic device, wherein the sensor is operable to detect a plurality of different activities;
• from the sensor, collecting training data for the electronic device associated with the plurality of different activities;
• generating a weight for each of the different activities based on the training data for the electronic device;
• for the electronic device, inputting the sensor data into a general model of the electronic device that outputs a likelihood result for each of the plurality of different activities, wherein the general model was trained based on sensor data from a plurality of different electronic devices detecting the plurality of different activities;
• for the electronic device, applying each weight for each of its corresponding one of the different activities to adjust its corresponding likelihood result without modifying the general model and without retraining the general model;
• determining a first one of the different activities is being performed by the electronic device by selecting a highest value of the adjusted likelihood results;
• and
• performing an action on the electronic device based on a determination of the first activity, wherein the action includes turning on step detection and/or location detection for the electronic device in a case that the first activity is movement based activity, and turning off the step detection and/or location detection in a case that the first activity is a resting activity.