Using modern deep learning models to make predictions on time series data from wearable sensors generally requires large amounts of labeled data. However, labeling these large datasets can be both cumbersome and costly. In this paper, we apply weak supervision to time series data, and programmatically label a dataset from sensors worn by patients with Parkinson's. We then built a LSTM model that predicts when these patients exhibit clinically relevant freezing behavior (inability to make effective forward stepping). We show that (1) when our model is trained using patient-specific data (prior sensor sessions), we come within 9% AUROC of a model trained using hand-labeled data and (2) when we assume no prior observations of subjects, our weakly supervised model matched performance with hand-labeled data. These results demonstrate that weak supervision may help reduce the need to painstakingly hand label time series training data. Time series data generated by wearable sensors are an increasingly common source of biomedical data. With their ability to monitor events in non-laboratory conditions, sensors offer new insights into human health across a diverse range of applications, including continuous glucose monitoring BID1 , atrial fibrillation detection BID11 , fall detection BID2 , and general human movement monitoring BID6 .Supervised machine learning with sensor time series data can help automate many of these monitoring tasks and enable medical professionals make more informed decisions. However, developing these supervised models is challenging due to the cost and difficultly in obtaining labeled training data, especially in settings with considerable inter-subject variability, as is common in human movement research BID5 . Traditionally, medical professionals must hand label events observed in controlled laboratory settings. When the events of interest are rare this process is time consuming, expensive, and does not scale to the sizes needed to train robust machine learning models. Thus there is a need to efficiently label the large amounts of data that machine learning algorithms require for time series tasks.In this work, we explore weakly supervised BID10 ) models for time series classification. Instead of using manually labeled training data, weak supervision encodes domain insights into the form of heuristic labeling functions, which are used to create large, probabilistically labeled training sets. This method is especially useful for time series classification, where the sheer number of data points makes manual labeling difficult.As a motivating test case, we focus on training a deep learning model to classify freezing behaviors in people with Parkinson's disease. We hypothesize that by encoding biomechanical knowledge about human movement and Parkinson's BID5 into our weakly supervised model, we can reduce the need for large amounts of hand labeled data and achieve similar performance to fully supervised models for classifying freezing behavior. We focus on two typical clinical use cases when making predictions for a patient: (1) where we have no prior observations of the patient, and (2) where we have at least one observation of the patient. Our work demonstrates the potential of weak supervision on time series tasks. In both experiments, our weakly supervised models performed close to or match the fully supervised models. Further, the amount of data available for the weak supervision task was fairly small -with more unlabeled data, we expect to be able to improve performance BID9 . These results show that costly and time-intensive hand labeling may not be required to get the desired performance of a given classifier.In the future, we plan to add more and different types of sensor streams and modalities (e.g., video). We also plan to use labeling functions to better model the temporal correlation between individual segments of these streams, which can potentially improve our generative model and hence end to end performance.
We demonstrate the feasibility of a weakly supervised time series classification approach for wearable sensor data. 
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