Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques.
Analyzing Activity Behavior and Movement in a Naturalistic Environment Using Smart Home Techniques.
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使用智能家庭技术在自然环境中分析活动行为和运动。
DOI：
10.1109/jbhi.2015.2461659
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发表时间：
2015-11
影响因子：
7.7
通讯作者：
Dawadi P
中科院分区：
文献类型：
--
作者：
Cook DJ;Schmitter-Edgecombe M;Dawadi P
智能系统可以提供的众多服务之一是分析不同医疗条件对日常行为的影响的能力。在这项研究中，我们使用智能家居和可穿戴传感器收集数据，同时（n=84）老年人进行复杂的日常生活活动。我们使用机器学习技术分析数据，发现健康老年人和帕金森病患者之间的差异不仅存在于他们的活动模式上，而且这些差异可以被自动识别。我们的机器学习分类器在区分这些组时达到了0.97的精度和0.97的AUC值。我们基于排列的测试证实，这些组之间基于传感器的差异具有统计学意义。
One of the many services that intelligent systems can provide is the ability to analyze the impact of different medical conditions on daily behavior. In this study we use smart home and wearable sensors to collect data while (n=84) older adults perform complex activities of daily living. We analyze the data using machine learning techniques and reveal that differences between healthy older adults and adults with Parkinson disease not only exist in their activity patterns, but that these differences can be automatically recognized. Our machine learning classifiers reach an accuracy of 0.97 with an AUC value of 0.97 in distinguishing these groups. Our permutation-based testing confirms that the sensor-based differences between these groups are statistically significant.