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

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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE open journal of engineering in medicine and biology 2024-01, Vol.5, p.494-497
Main Authors: Scotland, A., Cosne, G., Juraver, A., Karatsidis, A., Penalver-Andres, J., Bartholome, E., Kanzler, C. M., Mazza, C., Roggen, D., Hinchliffe, C., Del Din, S., Belachew, S.
Format: Article
Language:English
Subjects:
Accelerometers
Algorithms
Angular velocity
balance
Clinical trials
Cognition
Context
Data mining
digital biomarkers
digital health technology
Electronic healthcare
gait
inertial sensor
Libraries
Life estimation
Manual dexterity
Missing data
Modularity
Neurodegenerative diseases
python
Quality of life
Signal processing
smartphone
Smartphones
Wearable devices
wearable sensing
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2644-1276
2644-1276
DOI:10.1109/OJEMB.2024.3402531