Unobtrusive Monitoring of Affective Symptoms and Cognition using Keyboard Dynamics

父项目“情感症状和认知的非突破性监测”的目标 使用键盘动力学(UnMASCK)“是为了开发源自以下内容的数字生物标记 智能手机打字动力学和运动运动学，可以用来预测 与认知功能障碍相关的脑网络特性及其前瞻性变化 临床情绪症状。数字数据是通过一个新的平台悄悄地收集的 在情绪障碍和健康对照受试者的跨诊断样本中的“BiEffect”。这个 BiEffect平台收集与诸如按键类型等打字行为相关的元数据， 时间戳和加速测量，并将这些数据上传到研究服务器。特定主题 摘要指标在本地计算，并通过控制面板呈现给用户。 通过此补充，我们的目标是更新BiEffect平台的软件基础架构 以促进互操作性、与其他研究人员的协作以及与 智能手机硬件和操作系统升级。为了实现这些目标，我们计划 重构BiEffect代码库以实现更强大的多开发人员协作和版本 控制力。我们还计划创建标准化的数据处理管道来支持协作 研究人员在数据科学和工程方面的能力可能各不相同。

The goal of the parent project “Unobtrusive Monitoring of Affective Symptoms and Cognition using Keyboard Dynamics (UnMASCK)” is to develop digital biomarkers derived from smartphone typing dynamics and motor kinematics which can be used to predict alterations in brain network properties associated with cognitive dysfunction and prospective changes in clinical mood symptoms. The digital data is unobtrusively collected via a novel platform “BiAffect” in a transdiagnostic sample of subjects with mood disorders and health controls. The BiAffect platform collects metadata related to typing behaviors such as keypress types, timestamps, and accelerometry and uploads these data to the study server. Subject specific summary metrics are calculated locally and presented to the user via a dashboard. With this supplement our goal is to update the software infrastructure of the BiAffect platform in order to facilitate interoperability, collaboration with other researchers, and integration with the smartphone hardware and operating system upgrades. To achieve these goals, we plan to refactor the BiAffect codebase to enable more robust multi-developer collaboration and version control. We also plan to create standardized data processing pipelines to support collaborations with researchers who may have varying levels of capacity for data science and engineering.