Beacon-Based Time-Spatial Recognition toward Automatic Daily Care Reporting for Nursing Homes
Beacon-Based Time-Spatial Recognition toward Automatic Daily Care Reporting for Nursing Homes
复制标题
DOI：
10.1155/2018/2625195
复制
发表时间：
2018-08
期刊：
影响因子：
--
通讯作者：
Tatsuya Morita;Kenta Taki;Manato Fujimoto;H. Suwa;Yutaka Arakawa;K. Yasumoto
中科院分区：
文献类型：
--
作者：
Tatsuya Morita;Kenta Taki;Manato Fujimoto;H. Suwa;Yutaka Arakawa;K. Yasumoto
随着世界老年人口的持续增长，照顾他们的专业人员(照顾者)的负担也在增加。在养老院，护理员经常写日常报告，以提高居民的生活质量。然而，由于每个照顾者需要同时照顾多名居民，他们很难全面记录居民的活动。在这篇文章中，我们通过提出一个自动的每日报告生成系统来监控养老院居民的活动来解决这个问题。该系统使用多种方法分析BLE信号的RSSI值，估计居民使用BLE信标所处的多个位置(区域)，并从估计的区域信息识别每个居民的活动。估计居民活动的信息存储在带有时间戳的服务器中，服务器根据这些信息自动生成每日报告。为了展示所提系统的有效性，我们与一家实际的养老院合作，与四名参与者进行了为期五天的实验。我们通过以下四个评估来确定所提出的系统的有效性：(1)不同机器学习算法的性能比较，(2)平滑方法的比较，(3)时间窗口的比较，以及(4)生成的每日报告的评估。我们的评估显示，在156种模式中，最有效的组合模式可以准确地生成每日报告。结果表明，该系统具有高效率、高可用性、高灵活性的特点。
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.