Smart Home Occupant Identification via Sensor Fusion Across On-Object Devices
Smart Home Occupant Identification via Sensor Fusion Across On-Object Devices
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DOI：
10.1145/3218584
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发表时间：
2018-12
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通讯作者：
Jun Han;Shijia Pan;M. K. Sinha;H. Noh;Pei Zhang;P. Tague
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作者：
Jun Han;Shijia Pan;M. K. Sinha;H. Noh;Pei Zhang;P. Tague
乘员识别在许多智能家居应用中至关重要，例如自动家居控制和活动识别。以前的解决方案在部署成本、识别准确性或可用性方面受到限制。我们提出了SenseTribute，一种新颖的乘员识别解决方案，它利用现有的和普遍的物体上的传感器，这些传感器最初设计用于监测它们所连接的物体的状态。SenseTribute从这种物体传感器中提取更丰富的信息内容，并分析数据，以准确识别与物体交互的人。这种方法是基于物理现象，即不同的居住者以不同的方式与物体进行交互。此外，SenseTribute可能不依赖于用户的真实身份，因此即使没有标记的训练数据，该方法也可以工作。然而，来自单个物体上传感器的信息的分辨率可能不足以区分乘员，这可能导致识别错误。为了克服这个问题，SenseTribute在用户活动中对一系列事件进行操作，利用了最近对活动分割的研究。我们通过在厨房中的五个不同物体上部署传感器并邀请参与者与物体进行交互来评估SenseTribute。我们证明了SenseTribute可以在96%的试验中正确识别乘员，而无需标记训练数据，而即使使用训练数据，每个传感器的识别准确率也只有74%。
Occupant identification proves crucial in many smart home applications such as automated home control and activity recognition. Previous solutions are limited in terms of deployment costs, identification accuracy, or usability. We propose SenseTribute, a novel occupant identification solution that makes use of existing and prevalent on-object sensors that are originally designed to monitor the status of objects to which they are attached. SenseTribute extracts richer information content from such on-object sensors and analyzes the data to accurately identify the person interacting with the objects. This approach is based on the physical phenomenon that different occupants interact with objects in different ways. Moreover, SenseTribute may not rely on users’ true identities, so the approach works even without labeled training data. However, resolution of information from a single on-object sensor may not be sufficient to differentiate occupants, which may lead to errors in identification. To overcome this problem, SenseTribute operates over a sequence of events within a user activity, leveraging recent work on activity segmentation. We evaluate SenseTribute using real-world experiments by deploying sensors on five distinct objects in a kitchen and inviting participants to interact with the objects. We demonstrate that SenseTribute can correctly identify occupants in 96% of trials without labeled training data, while per-sensor identification yields only 74% accuracy even with training data.