Annotating activities of daily living (ADL) is vital for developing machine learning models for activity recognition. In addition, it is critical for self-reporting purposes such as in assisted living where the users are asked to log their ADLs. However, data annotation becomes extremely challenging in real-world data collection scenarios, where the users have to provide annotations and labels on their own. Methods such as self-reports that rely on users’ memory and compliance are prone to human errors and become burdensome since they increase users’ cognitive load. In this article, we propose a light yet effective context-aware change point detection algorithm that is implemented and run on a smartwatch for facilitating data annotation for high-level ADLs. The proposed system detects the moments of transition from one to another activity and prompts the users to annotate their data. We leverage freely available Bluetooth low energy (BLE) information broadcasted by various devices to detect changes in environmental context. This contextual information is combined with a motion-based change point detection algorithm, which utilizes data from wearable motion sensors, to reduce the false positives and enhance the system's accuracy. Through real-world experiments, we show that the proposed system improves the quality and quantity of labels collected from users by reducing human errors while eliminating users’ cognitive load and facilitating the data annotation process.

中文翻译：

---

通过智能手表上的上下文感知变化检测促进人类活动数据注释

注释日常生活活动 (ADL) 对于开发用于活动识别的机器学习模型至关重要。此外，这对于自我报告的目的至关重要，例如在要求用户记录其 ADL 的辅助生活中。然而，在现实世界的数据收集场景中，数据标注变得极具挑战性，用户必须自己提供标注和标签。依赖用户记忆和依从性的自我报告等方法容易出现人为错误，并且由于增加了用户的认知负担而变得繁重。在本文中，我们提出了一种轻量级但有效的上下文感知变化点检测算法，该算法在智能手表上实现和运行，以促进高级 ADL 的数据注释。建议的系统检测从一个活动到另一个活动的过渡时刻，并提示用户注释他们的数据。我们利用各种设备广播的免费蓝牙低功耗 (BLE) 信息来检测环境环境的变化。这种上下文信息与基于运动的变化点检测算法相结合，该算法利用来自可穿戴运动传感器的数据，以减少误报并提高系统的准确性。通过真实世界的实验，我们表明，所提出的系统通过减少人为错误，同时消除用户的认知负担并促进数据注释过程，提高了从用户收集的标签的质量和数量。我们利用各种设备广播的免费蓝牙低功耗 (BLE) 信息来检测环境环境的变化。这种上下文信息与基于运动的变化点检测算法相结合，该算法利用来自可穿戴运动传感器的数据，以减少误报并提高系统的准确性。通过真实世界的实验，我们表明，所提出的系统通过减少人为错误，同时消除用户的认知负担并促进数据注释过程，提高了从用户收集的标签的质量和数量。我们利用各种设备广播的免费蓝牙低功耗 (BLE) 信息来检测环境环境的变化。这种上下文信息与基于运动的变化点检测算法相结合，该算法利用来自可穿戴运动传感器的数据，以减少误报并提高系统的准确性。通过真实世界的实验，我们表明，所提出的系统通过减少人为错误，同时消除用户的认知负担并促进数据注释过程，提高了从用户收集的标签的质量和数量。