In this study, we propose a novel, context-based, location-aware algorithm for identifying low-level micro-activities that can be used to derive complex activities of daily living (ADL) performed by home-care patients. This identification is achieved by gathering the location information of the target user by using a wearable beacon embedded with a magnetometer and inertial sensors. The shortcomings of beacon-signal stability and mismatch issues in magnetic-field sequences are overcome by adopting a hybrid, three-phase approach for deducing the locus of micro-activities and their associated zones in a smart home environment. The suggested approach is assessed in two different test environments, where the main intention is to map the location of a person performing an activity with pre-defined house landmarks and zones in the offline labeled database. In addition to the recognition of low-level activities, the proposed method also identifies the person's walking trajectory within the same zone or between different zones of the house. The experimental results demonstrate that it is possible to achieve centimeter-level accuracy for the recognition of micro-activities and to achieve the classification accuracy of 85% for trajectory prediction. These results are encouraging and imply that the collection of accurate low-level information for ADL recognition is possible using integration of inertial sensors, magnetic field and Bluetooth low energy (BLE) technologies from the wearable beacon without relying on other infrastructural sensors.

中文翻译：

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通过可穿戴感知推断微活动，以对家庭护理患者进行ADL识别

在这项研究中，我们提出了一种新颖的，基于上下文的，基于位置的算法，用于识别低水平的微活动，这些活动可用于推导家庭护理患者执行的日常生活（ADL）的复杂活动。通过使用嵌入了磁力计和惯性传感器的可穿戴式信标收集目标用户的位置信息，可以实现这种识别。通过采用混合三相方法来推导智能家居环境中的微活动及其相关区域，可以克服磁场序列中信标信号稳定性和不匹配问题的缺点。在两种不同的测试环境中评估了建议的方法，其中的主要目的是使用离线标签数据库中的预定义房屋地标和区域来映射执行活动的人员的位置。除了识别低级活动外，所提出的方法还可以识别房屋的同一区域内或不同区域之间的人的行走轨迹。实验结果表明，对于微活动的识别，可以达到厘米级的精度，对于轨迹预测，可以达到85％的分类精度。这些结果令人鼓舞，并暗示可以通过集成可穿戴式信标中的惯性传感器，磁场和蓝牙低功耗（BLE）技术来收集ADL识别所需的准确的低级信息，而无需依赖其他基础设施传感器。