As part of the competitive iPraktikum program at the Technical University of Munich, iHaus Lassie leverages iHaus AG’s extensive IoT sensor data to build a robust health risk monitoring platform. By processing more than 200,000 data points through a custom-built machine learning pipeline, the system detects unusual patterns—such as sudden inactivity or falls—and automatically connects to an emergency call center operated by the German Red Cross (DRK). The cross-platform solution includes a fully developed iOS application (with Apple Watch integration) and leverages a hierarchical data authorization framework to ensure patient confidentiality. Over 500 tasks were meticulously tracked via Jira and deployed using Bitbucket CI/CD, demonstrating a rigorous, scalable software engineering approach that unites industrial design, data science, and user-centered research for proactive elderly care.

iHaus Lassie 于慕尼黑工业大学的 iPraktikum 项目中研发，依托 iHaus AG 的物联网传感器网络，构建了完备的健康风险监测平台。通过处理超过 20 万条传感器数据，并结合自研的机器学习流程，该系统可识别异常模式，例如突发性静止或跌倒，一旦确认，即可通过与德国红十字会（DRK）连接的紧急呼叫中心第一时间报警。项目完整实现了 iOS 应用与 Apple Watch 集成，并采用分级数据授权机制保障用户隐私。整个开发过程管理了 500 多个任务，使用 Jira 进行追踪与 Bitbucket CI/CD 持续交付，充分体现了高水准的软件工程、数据科学和以用户为中心的设计方法在老年人预防性护理中的融合与应用。