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

IF 2.7 Q3 ENGINEERING, BIOMEDICAL

IEEE Open Journal of Engineering in Medicine and Biology Pub Date : 2024-03-17 DOI:10.1109/OJEMB.2024.3402531

A. Scotland;G. Cosne;A. Juraver;A. Karatsidis;J. Penalver-Andres;E. Bartholomé;C. M. Kanzler;C. Mazzà;D. Roggen;C. Hinchliffe;S. Del Din;S. Belachew

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引用次数: 0

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.

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DISPEL：从数字健康技术中开发衡量标准的 Python 框架。

目标：本文介绍了 DISPEL，这是一个 Python 框架，用于在神经退行性疾病的治疗开发过程中，从数字健康技术收集的数据中促进传感器衍生措施（SDM）的开发。方法：采用面向对象的架构进行数据建模和 SDM 提取，实现了模块化、可集成性和灵活性，并使 SDM 的生成、命名、存储和文档标准化。此外，还设计了一种功能，用于系统地标记缺失数据和意外用户行为，这两种情况在无监督监测中经常出现。成果：DISPEL 采用 MIT 许可。它已支持来自不同数据提供商的格式，并可对从可穿戴设备和智能手机收集的原始数据到结构化 SDM 数据集进行可追溯的端到端处理。新颖的、基于文献的信号处理方法目前可从 16 个结构化测试（包括 6 份问卷）中提取 SDM，评估总体残疾情况和生活质量，并测量认知、手部灵活性和移动能力的表现结果。结论DISPEL 通过提供一个生产级 Python 框架和大量已实施的 SDM，支持临床试验 SDM 的开发。虽然该框架已根据临床试验数据进行了改进，但仍建议用户在特定使用环境中对所提供的算法进行临时验证。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Open Journal of Engineering in Medicine and Biology ENGINEERING, BIOMEDICAL-

CiteScore

9.50

自引率

3.40%

发文量

审稿时长

10 weeks

期刊介绍： The IEEE Open Journal of Engineering in Medicine and Biology (IEEE OJEMB) is dedicated to serving the community of innovators in medicine, technology, and the sciences, with the core goal of advancing the highest-quality interdisciplinary research between these disciplines. The journal firmly believes that the future of medicine depends on close collaboration between biology and technology, and that fostering interaction between these fields is an important way to advance key discoveries that can improve clinical care.IEEE OJEMB is a gold open access journal in which the authors retain the copyright to their papers and readers have free access to the full text and PDFs on the IEEE Xplore® Digital Library. However, authors are required to pay an article processing fee at the time their paper is accepted for publication, using to cover the cost of publication.