Researchers use wearable sensing data and machine learning (ML) models to predict various health and behavioral outcomes. However, sensor data from commercial wearables are prone to noise, missing, or artifacts. Even with the recent interest in deploying commercial wearables for long-term studies, there does not exist a standardized way to process the raw sensor data and researchers often use highly specific functions to preprocess, clean, normalize, and compute features. This leads to a lack of uniformity and reproducibility across different studies, making it difficult to compare results. To overcome these issues, we present FLIRT: A Feature Generation Toolkit for Wearable Data; it is an open-source Python package that focuses on processing physiological data specifically from commercial wearables with all its challenges from data cleaning to feature extraction.

FLIRT leverages a variety of state-of-the-art algorithms (e.g., particle filters, ML-based artifact detection) to ensure a robust preprocessing of physiological data from wearables. In a subsequent step, FLIRT utilizes a sliding-window approach and calculates a feature vector of more than 100 dimensions – a basis for a wide variety of ML algorithms. Preprocessing the data with FLIRT ensures that unintended noise and artifacts are appropriately filtered.

FLIRT provides functionalities beyond existing packages that can address unmet needs in physiological data processing and feature generation: (a) integrated handling of common wearable file formats (e.g., Empatica E4 archives), (b) robust preprocessing, and (c) standardized feature generation that ensures reproducibility of results. Nevertheless, while FLIRT comes with a default configuration to accommodate most situations, it offers a highly configurable interface for all of its implemented algorithms to account for specific needs.

FLIRT is a joint effort of the Bosch IoT Lab and the Center for Digital Health Interventions at ETH Zurich and the University of St. Gallen, and the Center for Technology and Behavioral Health (CTBH) at Dartmouth College.

GitHub repository: https://github.com/im-ethz/flirt

Documentation: https://flirt.readthedocs.io

Föll, S., Maritsch, M., Spinola, F., Barata, F., Kowatsch, T., Mishra, V., Fleisch, E., Wortmann, F., FLIRT: A Feature Generation Toolkit for Wearable Data, Computer Methods and Programs in Biomedicine, Available online 20 October 2021, 106461, 10.1016/j.cmpb.2021.106461. [PDF]