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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.
摘要：
目标：本文介绍了DISPEL，一个Python框架，以促进在神经退行性疾病的治疗开发背景下，从数字健康技术收集的数据中开发传感器衍生度量（SDM）。方法：模块化，采用面向对象的体系结构进行数据建模和SDM提取，实现了可集成性和灵活性，这也允许标准化SDM生成，命名,storage,和文档。此外,设计了一种功能来实现对缺失数据和意外用户行为的系统标记，两者都经常在无人监督的监测中。结果：DISPEL在MIT许可下可用。它已经支持来自不同数据提供商的格式，并允许从使用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。新的和基于文献的信号处理方法目前允许从16个结构化测试（包括六个问卷）中提取SDM，评估总体残疾和生活质量，并测量认知的表现结果，手动灵巧，和流动性。结论：DISPEL通过提供生产级Python框架和大量已实施的SDM来支持临床试验的SDM开发。尽管该框架已经根据临床试验数据进行了改进，建议用户在特定使用环境中对所提供的算法进行特别验证。
