Wearable devices that predict the context in which they are used based on previously tracked context data, and methods associated with the same, are provided. A wearable device may include a processor, a sensor that records data associated with a user of the wearable device, and memory storing executable instructions. By way of executing the instructions, the wearable device may receive context data representing a first context from the user. The wearable device may correlate the received context data with a first set of recorded sensor data. A second set of recorded sensor data may be recorded subsequent to recording the first set of recorded sensor data. The wearable device may then predict a second context associated with the second set of recorded sensor data based on the correlation between the context data received from the user and the first set of recorded sensor data. The user can label newly identified contexts.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A wearable device comprising: a sensor configured to obtain sensor data descriptive of a physiological parameter of the user; a memory configured to store a plurality of records correlating context data to physiological parameters obtained from sensor data; and a processor configured to: establish a baseline value based on a first set of sensor data obtained from the sensor, compare the baseline value to a second set of sensor data obtained from the sensor to determine whether the second set of sensor data is a change from the first set of sensor data, request user input from the user descriptive of a current context of the user, including one or more emotional states of the user, in response to determining that the second set of sensor data is a change from the first set of sensor data, create a record based on the second set of sensor data and the current context, and store the record in the memory as a member of the plurality of records. 2. The wearable device of claim 1, further comprising a communication interface configured to communicate over a communication network, wherein the communication interface is configured to detect the presence of one or more other wearable devices communicatively coupled to the network, and to receive additional context data from the one or more other wearable devices communicatively coupled to the networks, and wherein the record includes the additional context data. 3. The wearable device of claim 1, wherein the processor is further configured to provide output notifying the user of one or more correlations between the second set of sensor data and one or more emotional states of the user. 4. The wearable device of claim 1, wherein the processor is further configured to: train a machine-learning model using the plurality of records; and apply the machine-learning model to a third set of sensor data obtained from the sensor at a later time to estimate a context at the later time. 5. The wearable device of claim 1, wherein the processor is further configured to: determine one or more geolocations associated with the user provided current context; and provide output notifying the user of one or more correlations between the one or more geolocations and one or both of user provided contexts and sensor data. 6. A method for training a wearable device to predict a context in which a wearable device is used, the method comprising: establishing a baseline value based on a first set of sensor data obtained from a sensor configured to obtain sensor data descriptive of a physiological parameter of a user; comparing the baseline value to a second set of sensor data obtained from the sensor to determine whether the second set of sensor data is a change from the first set of sensor data; requesting user input from the user descriptive of a current context of the user, including one or more emotional states of the user, in response to determining that the second set of sensor data is a change from the first set of sensor data; creating a record based on the second set of sensor data and the current context; and storing the record in a memory as a member of a plurality of records. 7. The method of claim 6, further comprising: detecting the presence of one or more other wearable devices communicatively coupled to the network; receiving additional context data from the one or more other wearable devices communicatively coupled to the network, wherein the record includes the additional context data. 8. The method of claim 6, further comprising providing output, notifying the user of one or more correlations between the second set of sensor data and one or more emotional states of the user. 9. The method of claim 6, further comprising: determining one or more geolocations associated with the user selected current context; and providing output notifying the user of one or more correlations between the one or more geolocations and one or both of user provided contexts and sensor data. 10. The method of claim 6, further comprising training a machine-learning model using the plurality of records; and applying the machine-learning model to a third set of sensor data obtained from the sensor at a later time to estimate a context at the later time. 11. A non-transitory computer-readable medium having a computer program stored thereon that when executed by a processor perform a method for predicting a context in which a wearable health device is used, the method comprising: establishing a baseline value based on a first set of sensor data obtained from a sensor configured to obtain sensor data descriptive of a physiological parameter of a user; comparing the baseline value to a second set of sensor data obtained from the sensor to determine whether the second set of sensor data is a change from the first set of sensor data; providing user selectable elements of a current context, including one or more emotional states; requesting user input from the user descriptive of a current context of the user, including one or more emotional states of the user, in response to determining that the second set of sensor data is a change from the first set of sensor data; creating a record based on the second set of sensor data and the current context; and storing the record in a memory as a member of a plurality of records. 12. The non-transitory computer-readable medium of claim 11, wherein the method further comprises: detecting the presence of one or more other wearable devices communicatively coupled to the network; and receiving additional context data from the one or more other wearable devices communicatively coupled to the network, wherein the record includes the additional context data. 13. The non-transitory computer-readable medium of claim 11, wherein the method further comprises: providing output notifying the user of one or more correlations between the second set of sensor data and one or more contexts. 14. The non-transitory computer-readable medium of claim 11, wherein the method further comprises: determining one or more geolocations associated with the user selected current context; and providing output notifying the user of one or more correlations between the one or more geolocations and one or both of user provided context and sensor data. 15. The non-transitory computer-readable medium of claim 11, wherein the method further comprises: training a machine-learning model using the plurality of records; and applying the machine-learning model to a third set of sensor data obtained from the sensor at a later time to estimate a context at the later time. 16. The wearable device of claim 1, wherein the physiological parameters obtained from the sensors include at least one of heart rate, blood pressure, or respiratory rate. 17. The method of claim 6, wherein the physiological parameter of the user includes at least one of heart rate, blood pressure, or respiratory rate. 18. The non-transitory computer-readable medium of claim 11, wherein the physiological parameter of the user includes at least one of heart rate, blood pressure, or respiratory rate.