Abstract

Goal: This paper introduces DISPEL, a Python DIgital Signal ProcEssing Library developed to standardize extraction of sensor-derived measures (SDMs) from wearables or smartphones data. Methods: DISPEL supports custom and third-party data formats and essential end-to-end processing steps from raw data to structured SDM datasets. DISPEL uses an object-oriented codebase for data import, data modelling and SDM extraction and export, with source-to-outcome traceability. Results: DISPEL is publicly available under MIT license. It is a flexible, modular framework with practical examples in extracting SDMs from structured tests and continuous monitoring scenarios (e.g. performance outcome assessments of cognition, manual dexterity, and mobility). Embedded data quality checks ensure robustness of SDMs for remotely collected data. The analysis of a smartphone-based balance and gait turn test illustrates the library's capabilities. Conclusion: DISPEL provides a highly standardized and robust analysis framework to support traceability and reproducibility in SDM development. We encourage contributions of new processing modules. Impact Statement-DISPEL offers a standardized way to extract sensor-derived measures from wearables and smartphone signals with traceability from source to outcome. Its open-source availability aims at supporting multidisciplinary collaborations to accelerate and scale development and adoption of digital biomarkers/endpoints.