Predictive and real-time adaptive music recommendation system and method

1 . A predictive and real-time adaptive music recommendation method, comprising:

acquiring data, the data comprising biometric data from at least one multimodal biometric sensor and user input data explicitly entered by a user via a user interface, the user input data comprising at least a mood indicator, a workout type, and a workout type intensity level, the user input data indicating a context in which a playlist of music items is to be used;

analyzing the data to determine a user state profile, wherein analyzing comprises:

preprocessing the biometric data to remove se ad artifacts;

identifying key biometric metrics from the preprocessed biometric data; and

jointly classifying a physiological state and a psychological state based on a combination of the key biometric metrics and the user input data, wherein the mood indicator and the workout type and workout intensity level are used together with the key biometric metrics as joint inputs to configure and perform the state classification using a machine learning algorithm; and

generating the playlist based on the user state profile comprising the physiological state and the psychological state.

2 . The method as claimed in claim 1 , wherein the biometric data is collected from at least one multimodal biometric sensor.

3 . The method as claimed in claim 1 , wherein analyzing the data comprises:

preprocessing the biometric data to remove noise and artifacts;

identifying key metrics from the preprocessed biometric data; and

classifying the physiological state and the psychological state based on a combination of the key metrics and the user input data.

4 . The method as claimed in claim 1 , wherein a machine learning algorithm is used to classify the physiological state and the psychological state based on the biometric data and the user input data.

5 . The method as claimed in claim 1 , further comprising:

tracking the data to identify changes in the data;

updating the user state profile based on the changes in the data;

modifying the playlist in response to the updated user state profile.

6 . The method as claimed in claim 1 , wherein the data is acquired from a plurality of users, and wherein analyzing the data comprises analyzing the data acquired from the plurality of users to determine a group user state profile representing aggregated physiological and psychological states of the plurality of users, and wherein the playlist is generated for concurrent playback to the plurality of users to synchronize a physical activity session among the plurality of users based on the group user state profile, wherein generating the playlist for concurrent playback comprises:

initiating playback at the same time on devices associated with the plurality of users; and

coordinating track transitions among the devices based on the group user state profile.

7 . A predictive and real-time adaptive music recommendation system, comprising:

a processor;

a memory, wherein modules are stored in the memory for execution by the processor, the modules comprising:

a data acquiring module configured to acquire data, the data comprising biometric data from at least one multimodal biometric sensor and user input data explicitly entered by a user via a user interface, the user input data comprising at least a mood indicator, a workout type, and a workout intensity level, the user input data indicating a context in which a playlist of music items is to be used;

an analysis module configured to analyze the data to determine a user state profile, wherein the analysis module is configured to,

preprocess the biometric data to remove noise and artifacts;

identify key biometric metrics from the preprocessed biometric data; and

jointly classify a physiological state and a psychological state based on a combination of the key biometric metrics and the user input data, wherein the mood indicator, the workout type, and the workout intensity level are used together with the key biometric metrics as joint inputs to configure and perform the state classification using a machine learning algorithm; and

a recommendation module configured to generate the playlist based on the user state profile comprising the physiological state and the psychological state.

8 . The system as claimed in claim 7 , wherein the biometric data is collected from at least one multimodal biometric sensor.

9 . The system as claimed in claim 7 , wherein the analysis module is configured to preprocess the biometric data to remove noise and artifacts, identify key metrics from the preprocessed biometric data, and classify the physiological state and the psychological state based on a combination of the key metrics and the user input data.

10 . The system as claimed in claim 7 , wherein a machine learning algorithm is used to classify the physiological state and the psychological state based on the biometric data and the user input data.

11 . The system as claimed in claim 7 , wherein the analysis module is further configured to track the data to identify changes in the data and update the user state profile based on the changes in the data, and the recommendation module is further configured to modify the playlist in response to the updated user state profile.

12 . The system as claimed in claim 7 , wherein the data acquiring module is configured to acquire data from a plurality of users, and wherein the analysis module is configured to analyze the data acquired from the plurality of users to determine a group user state profile representing aggregated physiological and psychological es of the plurality of users, and wherein the recommendation module is configured to generate the playlist for concurrent playback to the plurality of users to synchronize a physical activity session among the plurality of users based on the group user state profile, wherein generating the playlist for concurrent playback comprises:

initiating playback at the same time on devices associated with the plurality of users; and

coordinating track transitions among the devices based on the group user state profile.