Real-time adaptive music playback based on physiological data

1 . A method for adaptive music playback using a wearable device comprising at least one sensor, comprising:

identifying, by at least one processor, at least one first physiological metric associated with a person based on sensor data obtained by the at least one sensor of the wearable device;

selecting, by the at least one processor, a first group of audio tracks from a plurality of groups of audio tracks based on the at least one first physiological metric and an effect indicator associated with each of the plurality of groups of audio tracks, the effect indicator being indicative of at least one of: a relaxation effect, a sleep aid effect, or a focus effect;

outputting a first audio track in the first group of audio tracks;

identifying, by the at least one processor, at least one second physiological metric associated with the person based on additional sensor data obtained by the at least one sensor of the wearable device during playback of the first audio track;

selecting, by the at least one processor, a second audio track based on the at least one second physiological metric; and

outputting, after the first audio track, the second audio track,

wherein selecting the second audio track based on the at least one second physiological metric comprises at least one of:

in response to determining that the at least one first physiological metric and the at least one second physiological metric are within a same physiological metric range, selecting the second audio track from the first group of audio tracks; or

in response to determining that the at least one first physiological metric and the at least one second physiological metric are within different physiological metric ranges, selecting the second audio track in a second group of audio tracks different from the first group of audio tracks.

2 . The method of claim 1 , wherein selecting, by the at least one processor, the first group of audio tracks from the plurality of groups of audio tracks based on the at least one physiological metric and the effect indicator associated with each of the plurality of groups of audio tracks comprises:

determining a current status of the person based on the first physiological metric; and

selecting the first group of audio tracks from the plurality of groups of audio tracks based on the current status of the person and the effect indicator associated with each of the plurality of groups of audio tracks.

3 . The method of claim 1 , wherein selecting, by the at least one processor, the first group of audio tracks from the plurality of groups of audio tracks based on the at least one physiological metric comprises:

selecting the first group of audio tracks from the plurality of groups of audio tracks based on at least one predefined physiological metric associated with each of the plurality of groups of audio tracks and the at least one first physiological metric.

4 . The method of claim 1 , wherein the at least one physiological metric comprises at least one of a cadence, a heartrate, a micro-movement, or a respiration rate.

5 . The method of claim 1 , wherein each of the plurality of group of audio tracks comprises at least one audio track sharing a common audio characteristics, and the common audio characteristics comprises at least one of: an audio tempo, an audio rhythm, an audio intensity, a timbre, or a melody.

6 . The method of claim 1 , wherein each of a beginning segment and an ending segment of the audio tracks is defined based on a music structure comprising at least one of: verse, chorus, or bridge, and a beginning segment of the second audio track has one or more common audio characteristics with an ending segment of the first audio track.

7 . The method of claim 1 , further comprising:

determining if a status of the person is changed based on additional sensor data obtained during playback of the first audio track by the at least one sensor;

upon determining that the status of the person is changed, selecting, from the plurality of groups of audio tracks, a second group of audio tracks corresponding to the changed status from the plurality of group of audio tracks; and

outputting, after the first audio track, a second audio track of the second group of audio tracks.

8 . The method of claim 1 , wherein selecting the second audio track based on the at least one second physiological metric, further comprise: identifying a second group of audio tracks different from the first group of audio tracks based on the at least one second physiological metric; and selecting the second audio track associated with a transition from the first group of audio tracks to the second group of audio tracks, the second audio track being associated with an audio characteristics between a first audio characteristics associated with the first group of audio tracks and a second audio characteristics associated with the second group of audio tracks.

9 . A non-transitory computer readable medium storing instructions operable to cause one or more processors to perform operations for personalizing audio playback using a wearable device comprising at least one sensor, the operations comprising:

identifying, based on sensor data obtained by the at least one sensor of the wearable device, a first physiological metric associated with a person, wherein the first physiological metric includes at least one of a heartrate, a micro-movement, or a respiration rate;

selecting, based on the first physiological metric, a first audio track from a first group of audio tracks, wherein each of the first group of audio tracks is associated with at least one audio characteristics comprising an effect indicator determined by analyzing historical playback information of the first group of audio tracks;

outputting the first audio track;

identifying a second physiological metric obtained from additional sensor data by the at least one sensor during playback of the first audio track;

selecting, by the at least one processor, a second audio track based on the second physiological metric; and

outputting, after the first audio track, the second audio track,

wherein selecting the second audio track based on the second physiological metric comprises at least one of:

in response to determining that the first physiological metric and the second physiological metric are within a same physiological metric range, selecting the second audio track from the first group of audio tracks; or

in response to determining that the physiological metric and the second physiological metric are within different physiological metric ranges, selecting the second audio track in a second group of audio tracks different from the first group of audio tracks.

10 . The non-transitory computer readable medium of claim 9 , wherein selecting, based on the first physiological metric, the first audio track from the first group of audio tracks further comprises:

identifying the first audio track based on a first physiological metric range comprising the first physiological metric and a first effect indicator associated with the first audio track and mapped to the first physiological metric range.

11 . The non-transitory computer readable medium of claim 9 , wherein the operations further comprise:

identifying or updating a first effect indicator associated with the first audio track based on additional sensor data obtained by the at least one sensor during playback of the first audio track; and

outputting a second audio track selected based on a second effect indicator that is different from the first effect indicator.

12 . The non-transitory computer readable medium of claim 9 , wherein selecting, based on the first physiological metric, the first audio track from the first group of audio tracks comprises:

Selecting the first group of audio tracks based on the first physiological metric; and

selecting the first audio track from at least one audio track comprised in the first group of audio tracks based on a first audio characteristics associated with the first audio track.

13 . The non-transitory computer readable medium of claim 9 , wherein the operations further comprise:

comparing the second physiological metric to the first physiological metric to determine whether to identify, for output, another audio track associated with second audio characteristics that is different, at least in part, from the first audio characteristics associated with the first audio track.

14 . The non-transitory computer readable medium of claim 9 , wherein the operations further comprise:

in response to determining that the second physiological metric matches the first physiological metric, outputting, after the first audio track, the second audio track that is associated with a first audio characteristics associated with the first audio track.

15 . The non-transitory computer readable medium of claim 14 , wherein a fade-in effect is applied to a beginning segment of the second audio track and a fade-out effect is applied to an ending segment of the first audio track.

16 . The non-transitory computer readable medium of claim 14 , wherein a beginning segment of the second audio track has one or more common audio characteristics with an ending segment of the first audio track, wherein the one or more common audio characteristics include at least one of an audio tempo and rhythm.

17 . The non-transitory computer readable medium of claim 9 , wherein the operations further comprise:

in response to determining that the second physiological metric does not match the first physiological metric, outputting, after the first audio track, the second audio track that is associated with a second audio characteristics different from a first audio characteristics associated with the first audio track.