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Study on data acquisition system for living environmental informationfor biofication of living spaces

机译:用于居住空间生物化的生活环境信息数据采集系统研究

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In Japan's rapidly aging society, the number of elderly people living alone increases every year. Theses elderly people require more and more to maintain as independent a life as possible in their own homes. It is necessary to make living spaces that assist in providing safe and comfortable lives. "Biofication of Living Spaces" is proposed with the concept of creating save and pleasant living environments. It implies learning from biological systems, and applying to living spaces features such as high adaptability and excellent tolerance to environmental changes. As a first step towards realizing "Biofied Spaces", a system for acquisition and storing information must be developed. This system is similar to the five human senses. The information acquired includes environmental information such as temperature, human behavior, psychological state and location of furniture. This study addresses human behavior as it is the most important factor in design of a living space. In the present study, pyroelectric infrared sensors were chosen for human behavior recognition. The pyroelectric infrared sensor is advantageous in that it has no limitation on the number of sensors put in a single space because sensors do not interfere with each other. Wavelet analysis was applied to the output time histories of the pyroelectric infrared sensors. The system successfully classified walking patterns with 99.5% accuracy of walking direction (from right or left) and 85.7% accuracy of distance for 440 patterns pre-learned and an accuracy of over 80% accuracy of walking direction for 720 non-learned patterns.
机译:在日本迅速老龄化的社会中,独居老人的人数逐年增加。这些老年人越来越需要在自己的家中维持独立生活。必须创造有助于提供安全舒适生活的居住空间。提出了“对生活空间进行生物化”的概念,以创造节省和令人愉悦的生活环境。它意味着向生物系统学习,并将其应用于居住空间,例如高度适应性和对环境变化的出色耐受性。作为实现“生物空间”的第一步,必须开发一种用于获取和存储信息的系统。该系统类似于人类的五种感官。获取的信息包括环境信息,例如温度,人类行为,心理状态和家具位置。这项研究针对人类行为,因为它是居住空间设计中最重要的因素。在本研究中,选择了热释电红外传感器进行人类行为识别。热释电红外传感器的优点在于,由于传感器之间不会相互干扰,因此对放置在单个空间中的传感器的数量没有限制。小波分析应用于热释电红外传感器的输出时间历史。该系统成功地对440个预先学习的模式进行了分类,其步行方向准确度为99.5%(从右或左),距离准确度为85.7%,对于720个非学习模式,准确度超过了80%。

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