ACTIVITY CLASSIFICATION AND DISPLAY
Methods, systems, and devices for activity classification are described. A system may receive physiological data associated with a user via a wearable device, where the physiological data includes at least motion data. The system may identify an activity segment during which the user is engaged in a physical activity based on the motion data, where the activity segment is associated with activity segment data including at least the physiological data collected during the activity segment. The system may generate activity classification data associated with the activity segment based on the activity segment data, the activity classification data including a set of classified activity types and corresponding confidence values. The system may then cause a graphical user interface (GUI) of a user device to display the activity segment data and at least one classified activity type of the set of classified activity types.
1 . A method for classifying activity segments for a user, comprising:
receiving physiological data associated with the user via a wearable device, the physiological data comprising at least motion data;
identifying, based at least in part on the motion data, an activity segment during which the user is engaged in a physical activity, wherein the activity segment is associated with activity segment data including at least the physiological data collected during the activity segment;
generating activity classification data associated with the activity segment based at least in part on the activity segment data, the activity classification data including a plurality of classified activity types and corresponding confidence values, the confidence values indicating a confidence level associated with the corresponding classified activity type; and
causing a graphical user interface of a user device to display the activity segment data and at least one classified activity type of the plurality of classified activity types.
2 . The method of claim 1 , further comprising:
receiving, via the user device and in response to displaying the at least one classified activity type, a confirmation of the activity segment, wherein causing the graphical user interface to display the activity segment data is based at least in part on receiving the confirmation.
3 . The method of claim 2 , wherein the confirmation comprises a confirmation of the at least one classified activity type, and wherein causing the graphical user interface to display the activity segment data is based at least in part on receiving the confirmation of the at least one classified activity type.
4 . The method of claim 1 , further comprising:
receiving, via the user device and in response to displaying the at least one classified activity type, one or more modifications for the activity segment, wherein causing the graphical user interface to display the activity segment data is based at least in part on receiving the one or more modifications.
5 . The method of claim 4 , wherein the one or more modifications comprise an indication of an additional classified activity type associated with the activity segment.
6 . The method of claim 1 , wherein the physiological data further includes temperature data, the method further comprising:
identifying the activity segment based at least in part on the temperature data.
7 . The method of claim 6 , further comprising:
identifying the activity segment based at least in part on the motion data during the activity segment being greater than or equal to a motion threshold, and based at least in part on a temperature drop during the activity segment being greater than or equal to a threshold temperature drop.
8 . The method of claim 1 , wherein the physiological data further comprises temperature data, the method further comprising:
identifying one or more motion features based at least in part on the motion data; and
identifying one or more temperature features based at least in part on the temperature data, wherein generating the activity classification data is based at least in part on the one or more motion features, the one or more temperature features, or both.
9 . The method of claim 8 , wherein the one or more motion features comprise an amount of motion during the activity segment, and wherein the one or more temperature features comprise a temperature change during the activity segment, a rate of temperature change during the activity segment, or any combination thereof.
10 . The method of claim 1 , further comprising:
identifying historical activity segment data for the user, the historical activity segment data comprising one or more historical activity segments for the user, wherein generating the activity classification data is based at least in part on the historical activity segment data.
11 . The method of claim 10 , wherein the confidence values associated with the plurality of classified activity types are based at least in part on the historical activity segment data.
12 . The method of claim 1 , further comprising:
inputting the activity segment data into a machine learning model, wherein generating the activity classification data is based at least in part on inputting the activity segment data into the machine learning model.
13 . The method of claim 1 , further comprising:
identifying the activity segment based at least in part on one or more additional physiological parameters included within the physiological data, the one or more additional physiological parameters comprising heart rate data, heart rate variability data, respiratory rate data, or any combination thereof.
14 . The method of claim 1 , wherein the wearable device comprises a wearable ring device.
15 . The method of claim 1 , wherein the wearable device collects the physiological data from the user based on arterial blood flow.
16 . An apparatus for classifying activity segments for a user, comprising:
a processor;
memory coupled with the processor; and
instructions stored in the memory and executable by the processor to cause the apparatus to:
receive physiological data associated with the user via a wearable device, the physiological data comprising at least motion data;
identify, based at least in part on the motion data, an activity segment during which the user is engaged in a physical activity, wherein the activity segment is associated with activity segment data including at least the physiological data collected during the activity segment;
generate activity classification data associated with the activity segment based at least in part on the activity segment data, the activity classification data including a plurality of classified activity types and corresponding confidence values, the confidence values indicating a confidence level associated with the corresponding classified activity type; and
cause a graphical user interface of a user device to display the activity segment data and at least one classified activity type of the plurality of classified activity types.
17 . The apparatus of claim 16 , wherein the instructions are further executable by the processor to cause the apparatus to:
receive, via the user device and in response to displaying the at least one classified activity type, a confirmation of the activity segment, wherein causing the graphical user interface to display the activity segment data is based at least in part on receiving the confirmation.
18 . The apparatus of claim 17 , wherein the confirmation comprises a confirmation of the at least one classified activity type, and wherein causing the graphical user interface to display the activity segment data is based at least in part on receiving the confirmation of the at least one classified activity type.
19 . The apparatus of claim 16 , wherein the instructions are further executable by the processor to cause the apparatus to:
receive, via the user device and in response to displaying the at least one classified activity type, one or more modifications for the activity segment, wherein causing the graphical user interface to display the activity segment data is based at least in part on receiving the one or more modifications.
20 . A non-transitory computer-readable medium storing code for classifying activity segments for a user, the code comprising instructions executable by a processor to:
receive physiological data associated with the user via a wearable device, the physiological data comprising at least motion data;
identify, based at least in part on the motion data, an activity segment during which the user is engaged in a physical activity, wherein the activity segment is associated with activity segment data including at least the physiological data collected during the activity segment;
generate activity classification data associated with the activity segment based at least in part on the activity segment data, the activity classification data including a plurality of classified activity types and corresponding confidence values, the confidence values indicating a confidence level associated with the corresponding classified activity type; and
cause a graphical user interface of a user device to display the activity segment data and at least one classified activity type of the plurality of classified activity types.