Music recommendation based on biometric and motion sensors on mobile device

1. A method implemented by a user-portable mobile device for recommending audio to a user of the user-portable mobile device, the method comprising:

acquiring one or more measurements from at least one biometric sensor to obtain biometric information of the user;

determining, based on the biometric information, a biometric identifier as a classification of a biometric state of the user;

mapping the biometric identifier to a mood classifier to generate an audio selection, wherein the mood classifier identifies a particular mood from a plurality of moods based on the biometric identifier, and wherein the audio selection is associated with the particular mood;

modeling the audio selection based on an activity identifier, the biometric identifier, the mood classifier, user feedback, a location, time patterns, a motion status, and a current activity; and

producing the audio selection associated with the particular mood to the user through an output of the user-portable mobile device, wherein the output can be in the form of a signal or sound from a speaker.

2. The method of claim 1 , further comprising:

updating, using a machine-learning algorithm, the mapping of the biometric identifier to the mood classifier based on user feedback, wherein the user feedback indicates a preference of the user regarding previous audio selections for the user; and

playing future audio selections to the user based on the previous audio selections via the updated mapping.

3. The method of claim 2 , further comprising:

acquiring physical motion data of the user from at least one motion sensor; and

determining, based on the physical motion data, the activity identifier as a classification of physical motion of the user,

wherein the activity identifier and the biometric identifier are mapped to the mood classifier.

4. The method of claim 3 , wherein the user-portable mobile device stores a mapping of each of a plurality of activity identifiers to a corresponding one of a plurality of biometric identifiers.

5. The method of claim 3 , wherein the classification of the physical motion is one of a plurality of physical activities.

6. The method of claim 5 , wherein the plurality of physical activities comprises one or more of standing, walking, running, biking, dancing, and skating.

7. The method of claim 1 , wherein the biometric information of the user includes at least one of heart rate, skin temperature, perspiration level, oxygen level, brain wave, actigraphy sleep pattern, and electrocardiography (ECG).

8. A user-portable mobile device comprising:

a memory configured to store executable instructions;

a processor coupled to the memory and at least one biometric sensor, wherein the processor is configured to:

acquire biometric information of a user from the at least one biometric sensor;

determine, based on the biometric information, a biometric identifier as a classification of a biometric state of the user;

map the biometric identifier to a mood classifier to generate an audio selection, wherein the mood classifier identifies a particular mood from a plurality of moods, and wherein the audio selection is associated with the particular mood;

model the audio selection based on an activity identifier, the biometric identifier, the mood classifier, user feedback, a location, time patterns, a motion status, and a current activity; and

play the audio selection associated with the particular mood to the user through an output of the user-portable mobile device.

9. The user-portable mobile device of claim 8 , wherein the processor is further configured to:

update, using a machine-learning algorithm, the mapping of the biometric identifier to the mood classifier based on user feedback, wherein the user feedback indicates a preference of the user regarding previous audio selections for the user; and

play future audio selections to the user based on the previous audio selections via the updated mapping.

10. The user-portable mobile device of claim 9 , wherein the processor is further coupled to at least one motion sensor, and wherein the processor is further configured to:

acquire physical motion data of the user from at least one motion sensor; and

determine, based on the physical motion data, the activity identifier as a classification of physical motion of the user,

wherein the activity identifier and the biometric identifier are mapped to the mood classifier.

11. The user-portable mobile device of claim 10 , further comprising a storage configured to store the mapping of the activity identifier and the biometric identifier to the mood classifier.

12. The user-portable mobile device of claim 10 , wherein the classification of the physical motion is one of a plurality of physical activities, and wherein the plurality of physical activities comprises one or more of standing, walking, running, biking, dancing, and skating.

13. A computer program product comprising computer executable instructions stored on a non-transitory computer readable medium such that when executed by a processor cause a user-portable mobile device to:

acquire one or more measurements from at least one sensor to obtain physical condition data of a user using the user-portable mobile device, wherein the physical condition data includes physical motion data and biometric information of the user;

determine, based on the biometric information, a biometric identifier as a classification of a biometric state of the user;

determine, based on the physical motion data, an activity identifier as a classification of physical motion of the user;

map the activity identifier and the biometric identifier to a mood classifier to generate an audio selection, wherein the mood classifier identifies a particular mood from a plurality of moods, and wherein the audio selection is associated with the particular mood;

model the audio selection based on the activity identifier, the biometric identifier, the mood classifier, user feedback, a location, time patterns, a motion status, and a current activity; and

play the audio selection associated with the particular mood to the user through an output of the user-portable mobile device.

14. The computer program product of claim 13 , wherein the processor further causes the user-portable mobile device to:

update, using a machine-learning algorithm, the mapping of the activity identifier and the biometric identifier to the mood classifier based on user feedback, wherein the user feedback indicates a preference of the user regarding previous audio selections to the user; and

play future audio selections to the user based on the previous audio selections via the updated mapping.

15. The computer program product of claim 13 , wherein the physical condition data includes at least one of the physical motion data and the biometric information of the user.

16. The computer program product of claim 13 , wherein the physical condition data is a combination of the physical motion data and the biometric information of the user, wherein the physical motion data is acquired from at least one motion sensor, and wherein the biometric information is acquired from at least one biometric sensor.

17. The computer program product of claim 13 , wherein the classification of the physical motion is one of a plurality of physical activities, and wherein the plurality of physical activities comprises one or more of standing, walking, running, biking, dancing, and skating.