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

1. A method for calibrating a user activity model used by a mobile device, the method comprising:

receiving sensor data from a sensor of the mobile device, wherein the sensor is operable to detect a plurality of different activities of a user of the mobile device;

from the sensor, collecting user-specific training data for a specific user of the mobile device performing the plurality of different activities;

generating a weight for each of the different activities based on the user-specific training data;

for the specific user, inputting the sensor data into a general model of the mobile 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 users performing the plurality of different activities;

for the specific user, 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 specific user by selecting a highest value of the adjusted likelihood results; and

performing an action on the device for the specific user based on a determination of the first activity, wherein the different activities comprise walking, running, biking, and resting activities, wherein the action comprises turning on step detection and/or location detection for the mobile device if the first activity comprises walking, running or biking, wherein the action comprises turning off the step detection and/or location detection if the first activity is a resting activity.

2. The method of claim 1 , further comprising:

facilitating an assessment of physical characteristics of the specific user of the mobile device, wherein the physical characteristics comprise age and whether the user is disabled, and wherein generating each weight is based on the assessment of the physical characteristics of the specific user.

3. The method of claim 2 , wherein each weight is generated to improve, for the specific user of the mobile device, an accuracy of each corresponding modified likelihood result.

4. The method of claim 2 , wherein the general model is implemented on the mobile device.

5. The method of claim 1 , wherein the determining the first activity comprises comparing the likelihood results of the plurality of different activities.

6. The method of claim 1 , wherein the applying the weight to each likelihood result comprises increasing or decreasing such likelihood result.

7. The method of claim 1 , wherein the receiving the sensor data comprises, receiving data from at least one of, an accelerometer, a magnetometer, or a gyroscope.

8. A mobile device, comprising:

a sensor;

a processor; and

a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:

receiving sensor data from the sensor, wherein the sensor is operable to detect a plurality of different activities of a user of the mobile device;

from the sensor, collecting user-specific training data for a specific user of the mobile device performing the plurality of different activities;

generating a weight for each of the different activities based on the user-specific training data;

for the specific user, inputting the sensor data into a general model 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 users performing the plurality of different activities;

for the specific user, 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 specific user by selecting a highest value of the adjusted likelihood results; and

performing an action on the device for the specific user based on a determination of the first activity, wherein the different activities comprise walking, running, biking, and resting activities, wherein the action comprises turning on step detection and/or location detection for the mobile device if the first activity comprises walking, running or biking, wherein the action comprises turning off the step detection and/or location detection if the first activity is a resting activity.

9. The mobile device of claim 8 , the operations further comprising:

facilitating an assessment of physical characteristics of the specific user of the mobile device, wherein the physical characteristics comprise age and whether the user is disabled, and wherein generating each weight is based on the assessment of the physical characteristics of the specific user.

10. The mobile device of claim 9 , wherein each weight is generated to improve, for the specific user of the mobile device, an accuracy of each corresponding modified likelihood result.

11. The mobile device of claim 9 , wherein the general model is implemented on the mobile device.

12. The mobile device of claim 8 , wherein the determining the first activity comprises comparing the likelihood results of the plurality of different activities.

13. The mobile device of claim 8 , wherein the applying the weight to each likelihood result comprises increasing or decreasing such likelihood result.

14. The mobile device of claim 8 , wherein the sensor comprises one or more of:

an accelerometer,

a magnetometer, or

a gyroscope.

15. A computer-readable recording medium having program instructions that can be executed by various computer components to perform operations comprising:

receiving sensor data from a sensor of a mobile device, wherein the sensor is operable to detect a plurality of different activities of a user of the mobile device;

from the sensor, collecting user-specific training data for a specific user of the mobile device performing the plurality of different activities;

generating a weight for each of the different activities based on the user-specific training data;

for the specific user, inputting the sensor data into a general model of the mobile 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 users performing the plurality of different activities;

for the specific user, 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 specific user by selecting a highest value of the adjusted likelihood results; and

performing an action on the device for the specific user based on a determination of the first activity, wherein the different activities comprise walking, running, biking, and resting activities, wherein the action comprises turning on step detection and/or location detection for the mobile device if the first activity comprises walking, running or biking, wherein the action comprises turning off the step detection and/or location detection if the first activity is a resting activity.

16. The computer-readable recording medium of claim 15 , wherein the operations further comprise:

facilitating an assessment of physical characteristics of the specific user of the mobile device, wherein the physical characteristics comprise age and whether the user is disabled, and wherein generating each weight is based on the assessment of the physical characteristics of the specific user.

17. The computer-readable recording medium of claim 16 , wherein each weight is generated to improve, for the specific user of the mobile device, an accuracy of each corresponding modified likelihood result.