Music recommendation based on biometric and motion sensors on mobile device

• US 9,449,084 B2
• Filed: 09/27/2013
• Issued: 09/20/2016
• Est. Priority Date: 03/15/2013
• Status: Active Grant

• Current Assignee: Futurewei Technologies Incorporated (Huawei Investment & Holding Co., Ltd.)
• Original Assignee: Futurewei Technologies Incorporated (Huawei Investment & Holding Co., Ltd.)
• Inventors: Chong, Chia-Chin, Zhang, Jianyu
• Primary Examiner(s): Elbin, Jesse
• Application Number:
  US14/040,438
• Publication Number:
  US 20140277649A1
• Time in Patent Office: 1,089 Days
• Field of Search: 706/12, 706/934, 700/94
• US Class Current: 1/1
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/4806 Sleep evaluation A61B5/4821 takes precedence; devices for inducing sleep A61M21/02
• CPC Class Codes: A63B 24/0062 Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: G06F 16/635 Filtering based on additional data, e.g. user or group profiles
• CPC Class Codes: G06F 16/636 by using biological or physiological data
• CPC Class Codes: G06F 16/639 using playlists
• CPC Class Codes: G06F 16/68 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
• CPC Class Codes: G06F 16/686 using information manually generated, e.g. tags, keywords, comments, title or artist information, time, location or usage information, user ratings
• CPC Class Codes: G06F 16/687 using geographical or spatial information, e.g. location
• CPC Class Codes: G06F 16/90324 using system suggestions
• CPC Class Codes: G06F 3/16 Sound input; Sound output speech processing G10L
• CPC Class Codes: G06F 3/165 Management of the audio stream, e.g. setting of volume, audio stream path
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04H 60/33 Arrangements for monitoring the users' behaviour or opinions
• CPC Class Codes: H04H 60/46 for recognising users' preferences
• CPC Class Codes: H04H 60/49 for identifying locations
• CPC Class Codes: H04H 60/65 for using the result on users' side

Abstract

A method implemented by a mobile device for music recommendation to a user of the mobile device, the method comprising acquiring one or more measurements from at least one biometric sensor to obtain biometric information of the user, determining a music based at least in part on the biometric information, and recommending the music to the user for playing.

Claims

1. A method implemented by a mobile device for music recommendation to a user of the mobile device, the method comprising:

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

acquiring at least one measurement from one or more motion sensors to obtain physical motion data of the user;

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

determining, based on the physical motion data, an activity identifier as a classification of physical motion of the user, wherein the classification of the physical motion is one of a plurality of physical activities;

determining a life index identifier jointly based on the activity identifier and the biometric identifier;

mapping the life index identifier to a mood classifier using a set of initial conditions to generate a music recommendation, wherein the mood classifier identifies at least one mood of a plurality of moods, and wherein the music recommendation includes a music associated with the at least one mood;

recommending the music to the user for playing based on the music recommendation;

updating, using a machine-learning algorithm, the mapping of the life index identifier to the mood classifier based on user feedback, wherein the user feedback indicates a preference of the user regarding previous music recommended to the user; and

recommending future music to the user for playing based on music recommendations via the updated mapping.

2. 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).

3. The method of claim 1, wherein the mobile device stores a mapping of each of a plurality of activity identifiers to a corresponding one of plurality of biometric identifiers, and wherein determining the life index identifier comprises using the mapping.

4. The method of claim 1, wherein the plurality of physical activities comprises one or more of standing, walking, running, biking, dancing, and skating.

5. The method of claim 1, further comprising modeling the music recommendation based on the life index identifier, the mood classifier, the user feedback, location, time patterns, motion status, and current activity.

6. A mobile device comprising:

a processor configured to couple to at least one biometric sensor and further configured to;

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

couple to one or more motion sensors;

acquire at least one measurement from the one or more motion sensors to obtain physical motion data 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, wherein the classification of the physical motion is one of a plurality of physical activities;

determine a life index identifier jointly based on the activity identifier and the biometric identifier;

map the life index identifier to a mood classifier using a set of initial conditions to generate a music recommendation, wherein the mood classifier identifies at least one mood of a plurality of moods, and wherein the music recommendation includes a music associated with the at least one mood;

recommend the music to the user for playing based on the music recommendation;

update, using a machine-learning algorithm, the mapping of the life index identifier to the mood classifier based on user feedback, wherein the user feedback indicates a preference of the user regarding previous music recommended to the user; and

recommend future music to the user for playing based on music recommendations via the updated mapping.

7. The mobile device of claim 6, further comprising the at least one biometric sensor configured to conduct the one or more measurements on the user.

8. The mobile device of claim 6, further comprising the one or more motion sensors configured to conduct the at least one measurement on the user.

9. The mobile device of claim 6, further comprising a storage configured to store the mapping of each of a plurality of activity identifiers to a corresponding one of plurality of biometric identifiers, wherein determining the life index identifier comprises using the mapping.

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

11. The mobile device of claim 6, wherein the processor is further configured to model the music recommendation based on the life index identifier, the mood classifier, the user feedback, location, time patterns, motion status, and current activity.

12. 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 mobile device to:

acquire one or more measurements from at least one sensor to obtain physical condition data of a user using the 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, wherein the classification of the physical motion is one of a plurality of physical activities;

determine a life index identifier jointly based on the activity identifier and the biometric identifier;

map the life index identifier to a mood classifier using a set of initial conditions to generate a music recommendation, wherein the mood classifier identifies at least one mood of a plurality of moods, and wherein the music recommendation includes a music associated with the at least one mood;

recommend the music to the user for playing based on the music recommendation;

update, using a machine-learning algorithm, the mapping of the life index identifier to the mood classifier based on user feedback, wherein the user feedback indicates a preference of the user regarding previous music recommended to the user, and

recommend future music to the user for playing based on the music recommendation via the updated mapping.

13. The computer program product of claim 12, wherein the physical condition data includes at least one of the physical motion data and the biometric information of the user.

14. The computer program product of claim 12, wherein the physical condition data is a combination of physical motion data and biometric information of the user, wherein the physical motion data is acquired from one or more motion sensors, and wherein the biometric information is acquired from one or more biometric sensors.

15. The computer program product of claim 12, wherein the plurality of physical activities comprises one or more of standing, walking, running, biking, dancing, and skating.

16. The computer program product of claim 12, wherein the processor further causes the mobile device to model the music recommendation based on the life index identifier, the mood classifier, the user feedback, location, time patterns, motion status, and current activity.