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首页> 外文期刊>Journal of robotics and mechatronics >Life Pattern Estimation of the Elderly Based on Accumulated Activity Data and its Application to Anomaly Detection
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Life Pattern Estimation of the Elderly Based on Accumulated Activity Data and its Application to Anomaly Detection

机译：基于累积活动数据的老年人生活模式估计及其在异常检测中的应用

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A life pattern estimation method and its application to anomaly detection of a single elderly are proposed. Our observation system deploys some pyroelectric sensors in an elderly's house and monitors and measures activities 24 hours a day to grasp residents' life patterns. Activity data is successively forwarded to the nurse operation center and displayed to nurses at the center. The system reports status related to anomalies together with the basic activities of elderly residents to the nurses, who decide whether recent accumulated data expresses an anomaly or not based on suggestions from the system. In the system, residents whose lifestyle features resemble each other are categorized into the same group. Anomalies that occurred in the past are shared in the group and utilized in an anomaly detection algorithm. This algorithm is based on an "anomaly score." The score is figured out by utilizing the activeness of the house's elderly resident. This activeness is approximately proportional to the frequency of sensor response within one minute. The anomaly score is calculated from the difference between activeness in the present and in the past averaged over the long term. The score is thus positive if activeness in the present is greater than the average in the past, and the score is negative if the value in the present is less than average. If the score exceeds a certain threshold, it means that an anomaly event has occurred. An activity estimation algorithm is also developed that estimates the basic activities of residents such as getting up in the morning, or going out. The estimation is also shown to nurses with the anomaly score of residents. Nurses can understand the condition of elderly residents' health by combining the information and planning the most appropriate way to respond.
机译：提出了一种生活模式估计方法及其在单身老人异常检测中的应用。我们的观察系统在老人的房屋中部署了一些热释电传感器，并一天24小时监视和测量活动，以掌握居民的生活模式。活动数据依次转发到护士操作中心，并显示给中心的护士。该系统将与异常相关的状态以及老年人的基本活动报告给护士，护士将根据系统的建议来决定最近累积的数据是否表示异常。在该系统中，生活方式相似的居民被归为同一组。过去发生的异常将在组中共享，并用于异常检测算法中。该算法基于“异常分数”。通过利用房屋的老年人的活跃度来计算得分。此活动大约在一分钟内与传感器响应的频率成正比。异常分数是根据长期的当前和过去活动性之间的差异计算得出的。因此，如果当前的活动性大于过去的平均值，则该分数为正;如果当前的值小于平均值，则该分数为负。如果分数超过某个阈值，则表示发生了异常事件。还开发了一种活动估计算法，该算法可以估计居民的基本活动，例如早上起床或外出。估计值也显示给具有异常分数的护士。护士可以通过结合信息并计划最适当的应对方式来了解老年人的健康状况。

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