Systems and methods for self-supervised learning based on naturally-occurring patterns of missing data

A system comprising a computing device comprising at least one processor and instructions executable by the at least one processor to cause the at least one processor to perform operations comprising:

• (a) identifying, based at least in part on one or more demographics of a target user, a population from a larger group of users, wherein the population comprises one or more digital twins of the target user, and wherein the population is characterized by having a common demographic;
• (b) accessing, by a machine learning system, a set of data records for a plurality of users of the population, the set of data records representative of physical statistics measured for each user of the plurality of users of the population over a time period, wherein the physical statistics for each user of the plurality of users of the population are measured using a wearable device associated with each user of the plurality of users of the population;
• (c) for each data record of a subset of the set of data records:
• (i) identifying, by the machine learning system, a portion of the time period associated with a pattern of missing data from the set of data records, wherein the pattern of missing data corresponds to periods of disuse or deactivation of the wearable device associated with each user of the plurality of users of the population, and
• (ii) generating, by the machine learning system, a masked data record by masking a portion of an additional data record of the set of data records corresponding to the identified portion of the time period, wherein the masking of the additional data record of the subset of the set of data records causes the additional data record to resemble the pattern of missing data;
• (d) generating, by the machine learning system, a training dataset comprising both of at least the portion of the additional data record and the corresponding generated masked data record for each data record of the subset of the set of data records;
• (e) training, by the machine learning system, a machine learning model using the generated training dataset, the machine learning model configured to predict, for a received data record comprising masked data, imputed data corresponding to data of the received data record obscured based on the masked data;
• (e) generating, by the machine learning model, a plurality of learned representations, wherein the plurality of learned representations are associated with the prediction of the imputed data;
• and
• (g) fine-tuning, by the machine learning system, a learned representation of the plurality of learned representations to a downstream machine learning task, wherein the downstream machine learning task comprises processing a set of data records from a wearable device associated with the target user, thereby generating complete data for the target user.

A non-transitory computer-readable storage media encoded with instructions executable by one or more processors to cause the at least one processor to perform operations comprising:

• (a) identifying, based at least in part on one or more demographics of a target user, a population from a larger group of users, wherein the population comprises one or more digital twins of the target user, and wherein the population is characterized by having a common demographic;
• (b) accessing, by a machine learning system, a set of data records for a plurality of users of the population, the data records representative of physical statistics measured for each user of the plurality of users of the population over a time period, wherein the physical statistics for each user of the plurality of users of the population are measured using a wearable device associated with each user of the plurality of users of the population;
• (c) for each data record of a subset of data records:
• (i) identifying, by the machine learning system, a portion of the time period corresponding to missing data, and
• (ii) generating, by the machine learning system, a masked data record by masking a portion of an additional data record of the set of data records corresponding to the identified portion of the time period, to resemble a pattern of missing data from the set of data records, wherein the pattern of missing data corresponds to periods of disuse or deactivation of the wearable device associated with each user of the plurality of users of the population;
• (d) generating, by the machine learning system, a training dataset comprising both of at least the portion of the additional data record and the corresponding generated masked data record for each data record of the subset of the set of data records;
• (e) training, by the machine learning system, a machine learning model using the generated training dataset, the machine learning model configured to predict, for a received data record comprising masked data, imputed data corresponding to data of the received data record obscured based on the masked data;
• (f) generating, by the machine learning system, a plurality of learned representations, wherein the plurality of learned representations are associated with the prediction of the imputed data as a result of the imputation of the masked data in the pattern of missing data;
• and
• (g) fine-tuning, by the machine learning machine learning system, a learned representation of the plurality of learned representations to a downstream machine learning task, wherein the downstream machine learning task comprises processing a set of data records from a wearable device associated with the target user, thereby generating complete data for the target user.

A computer-implemented method of training a machine learning model to generate inferences from wearable sensor data, comprising:

• (a) identifying, based at least in part on one or more demographics of a target user, a population from a larger group of users, wherein the population comprises one or more digital twins of the target user, and wherein the population is characterized by having a common demographic;
• (b) retrieving a first set of wearable sensor data for each subject of a plurality of subjects of the population, wherein the first set of wearable sensor data for each subject of the plurality of subjects of the population is measured using a wearable device associated with each subject of the plurality of subjects of the population;
• (c) selectively masking portions of at least a subset of the first set of wearable sensor data, wherein the masked portions of at least the subset of the first set of wearable sensor data are associated with periods of missing data and wherein the masking of the portions of at least the subset of the first set of the wearable sensor data causes the portions of at least the subset of the first set of the wearable sensor data to resemble a pattern of missing data from the set of data records, wherein the pattern of missing data corresponds to periods of disuse or deactivation of the wearable device associated with each user of the plurality of users of the population;
• (d) creating a training set comprising at least the subset of the first set of wearable sensor data;
• (e) training the machine learning model to impute data to the masked portions of at least the subset of the first set of wearable sensor data, thereby producing at least one learned representation from the training;
• and
• (f) fine-tuning the at least one learned representation by using the machine learning model to a downstream machine learning task, wherein the downstream machine learning task comprises processing a second set of wearable sensor data from a wearable device associated with the target user, thereby generating complete data for the target user.