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. 2024 May 17:5:494-497.
doi: 10.1109/OJEMB.2024.3402531. eCollection 2024.

DISPEL: A Python Framework for Developing Measures From Digital Health Technologies

A Scotland  1 , G Cosne  1 , A Juraver  1 , A Karatsidis  1 , J Penalver-Andres  1 , E Bartholome  1 , C M Kanzler  1 , C Mazza  1 , D Roggen  1 , C Hinchliffe  2 , S Del Din  2 , S Belachew  1
Affiliations

DISPEL: A Python Framework for Developing Measures From Digital Health Technologies

A Scotland et al. IEEE Open J Eng Med Biol. .

Abstract

Goal: This paper introduces DISPEL, a Python framework to facilitate development of sensor-derived measures (SDMs) from data collected with digital health technologies in the context of therapeutic development for neurodegenerative diseases. Methods: Modularity, integrability and flexibility were achieved adopting an object-oriented architecture for data modelling and SDM extraction, which also allowed standardizing SDM generation, naming, storage, and documentation. Additionally, a functionality was designed to implement systematic flagging of missing data and unexpected user behaviors, both frequent in unsupervised monitoring. Results: DISPEL is available under MIT license. It already supports formats from different data providers and allows traceable end-to-end processing from raw data collected with wearables and smartphones to structured SDM datasets. Novel and literature-based signal processing approaches currently allow to extract SDMs from 16 structured tests (including six questionnaires), assessing overall disability and quality of life, and measuring performance outcomes of cognition, manual dexterity, and mobility. Conclusion: DISPEL supports SDM development for clinical trials by providing a production-grade Python framework and a large set of already implemented SDMs. While the framework has already been refined based on clinical trials' data, ad-hoc validation of the provided algorithms in their specific context of use is recommended to the users.

Keywords: Signal processing; balance; cognition; digital biomarkers; digital health technology; drawing; gait; inertial sensor; python; smartphone; wearable sensing.

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Conflict of interest statement

AS, GC, AJ, AK, JPA, EB, CMK, CM, DR, and SB were employees of Biogen at the time of developing this paper and might hold shares of the company. SDD reports consultancy activity with Hoffmann-La Roche Ltd. outside of this study.

Figures

Fig. 1.
Fig. 1.
Figure shows the core functionalities of DISPEL (white blocks) and the modules implementing them (orange text). The orange rectangular boxes represent the different scenarios in which a user could interact with DISPEL and the type of data and modules they would need to implement and provide as an input in each case. The grey box in the middle of the chart details the content of the data model. This consists of a reading (including data and metadata) structured in a series of levels (Leveli) that may contain different sensor data (RawDataSet), custom-defined windows of analysis (LevelEpoch) and calculated measures (MeasureSet). The Model also includes the flags generated if technical issues or user-related deviations in the test executions are detected. Exerts of info for a data trace graph and SDMs tables obtained as an output are also illustrated, together with an example of automatically standardized measures and flags naming.
See this image and copyright information in PMC

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