A method for recognizing living activities in homes using positioning sensor and power meters
A method for recognizing living activities in homes using positioning sensor and power meters
复制标题
一种利用定位传感器和功率计识别家庭生活活动的方法
DOI：
10.1109/percomw.2015.7134062
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
K. Yasumoto
中科院分区：
文献类型：
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作者：
Kenki Ueda;M. Tamai;K. Yasumoto
为了实现智能家居，提供包括节能的家庭环境感知家电控制和老年人监控系统在内的复杂服务，自动识别家庭中的人类活动至关重要。到目前为止，已经提出了几种日常活动识别方法，但是它们中的大多数仍然存在有待解决的问题，诸如由于许多传感器而导致的高部署成本和/或由于使用相机而侵犯用户的隐私感。此外，已经提出了许多使用可穿戴传感器的活动识别方法，但是它们集中于简单的人类活动，如步行、跑步等，并且难以将这些方法用于识别家庭中的各种复杂活动。在本文中，我们提出了一种基于机器学习的方法，用于识别家庭中的各种日常活动，只使用居民配备的定位传感器和连接到电器的电表。为了有效地收集用于构建识别模型的训练数据，我们开发了一种工具，该工具可以可视化传感器数据的时间序列，并便于用户将标签（活动类型）放置到传感器数据的指定时间间隔。我们通过将提取的训练数据除以固定的时间窗口并计算每个样本位置和时间窗口上平均的功耗作为特征值来获得训练样本。然后，使用所获得的样本通过机器学习来构建活动识别模型。针对六种不同的活动（看电视，吃饭，做饭，阅读书，洗碗等），我们将我们提出的方法应用于智能家居测试平台中收集的传感器数据。结果，我们的方法识别出6种不同的活动，准确率约为85%，召回率约为82%。
To realize smart homes with sophisticated services including energy-saving context-aware appliance control in homes and elderly monitoring systems, automatic recognition of human activities in homes is essential. Several daily activity recognition methods have been proposed so far, but most of them still have issues to be solved such as high deployment cost due to many sensors and/or violation of users' feeling of privacy due to use of cameras. Moreover, many activity recognition methods using wearable sensors have been proposed, but they focus on simple human activities like walking, running, etc. and it is difficult to use these methods for recognition of various complex activities in homes. In this paper, we propose a machine learning based method for recognizing various daily activities in homes using only positioning sensors equipped by inhabitants and power meters attached to appliances. To efficiently collect training data for constructing a recognition model, we have developed a tool which visualizes a time series of sensor data and facilitates a user to put labels (activity types) to a specified time interval of the sensor data. We obtain training samples by dividing the extracted training data by a fixed time window and calculating for each sample position and power consumptions averaged over a time window as feature values. Then, the obtained samples are used to construct an activity recognition model by machine learning. Targeting six different activities (watching TV, taking a meal, cooking, reading a book, washing dishes, and other), we applied our proposed method to the sensor data collected in a smart home testbed. As a result, our method recognized 6 different activities with precision of about 85% and recall of about 82%.
DOI：
10.1007/978-3-540-24646-6_1
发表时间：
2004-01-01
期刊：
PERVASIVE COMPUTING, PROCEEDINGS
影响因子：
--
作者：
Bao, L;Intille, SS
通讯作者：
Intille, SS