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Beacon-Based Time-Spatial Recognition toward Automatic Daily Care Reporting for Nursing Homes

机译:基于信标的时间空间识别,用于养老院的自动日常护理报告

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摘要

As the world’s population of senior citizens continues to grow, the burden on the professionals who care for them (carers) is also increasing. In nursing homes, carers often write daily reports to improve the resident’s quality of life. However, since each carer needs to simultaneously care for multiple residents, they have difficulty thoroughly recording the activities of residents. In this paper, we address this problem by proposing an automatic daily report generation system that monitors the activities of nursing home residents. The proposed system estimates the multiple locations (areas) at which residents are situated with a BLE beacon, using a variety of methods to analyze the RSSI values of BLE signals, and recognizes the activity of each resident from the estimated area information. The information of the estimated activity of residents is stored in a server with timestamps, and the server automatically generates daily reports based on them. To show the effectiveness of the proposed system, we conducted an experiment for five days with four participants in cooperation with an actual nursing home. We determined the proposed system’s effectiveness with the following four evaluations: (1) comparison of performance of different machine-learning algorithms, (2) comparison of smoothing methods, (3) comparison of time windows, and (4) evaluation of generated daily reports. Our evaluations show the most effective combination pattern among 156 patterns to accurately generate daily reports. We conclude that the proposed system has high effectiveness, high usability, and high flexibility.
机译:随着世界上高级公民的人口继续增长,关心他们(照顾者)的专业人士的负担也在增加。在护理家园里,护理人员经常写日报,以提高居民的生活质量。然而,由于每个护理人员都需要同时关心多个居民,因此他们难以彻底记录居民的活动。在本文中,我们通过提出监督护理家庭居民活动的自动日报生成系统来解决这个问题。所提出的系统估计使用各种方法来分析BLE信号的RSSI值的多个位置(区域),该多个位置(区域)使用各种方法来分析BLE信号的RSSI值,并识别来自估计区域信息的每个居民的活动。居民估计活动的信息存储在具有时间戳的服务器中,并且服务器根据它们自动生成日常报告。为了表明所提出的系统的有效性,我们与四名与会者与实际的疗养院合作进行了五天的实验。我们确定了提出的系统的有效性与以下四个评估:(1)不同机器学习算法的性能比较,(2)平滑方法的比较,(3)时间窗口比较,(4)对生成的日报评估。我们的评估显示了156种模式中最有效的组合模式,以准确地产生日常报告。我们得出结论,拟议的系统具有高效率,高可用性和高灵活性。

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