Hung Lun-Ping, Hsieh Nan-Chen, Chen Chien-Liang
Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei City, Taiwan.
Department of Innovative Living Design, Overseas Chinese University, Taichung City, Taiwan.
Mob Netw Appl. 2022;27(1):33-46. doi: 10.1007/s11036-020-01707-z. Epub 2021 Jan 6.
In recent years, aged care living has drawn attention because of population aging and extension of average lifespan. Moreover, the rapid development of information communication technology and the internet of things lay the foundation for the application of sensor networks and cloud computing on medical care. Most elderly often suffer from chronic diseases due to weaker immunity causing a higher infection risk. The problem of infection controlling is an essential issue for residences living in aged care facilities. In the past, for infection control, medical personnel use the manual method of tracking, investigating, and interviewing patients to obtain patient contact list. This method cannot identify residents who have a high risk of infection, distinguish the distance between patients and other residents, and evaluate the risk of infection. To isolate all the residents who contact patients is the only solution that does not only cause repulsion of other residents but also waste medical resources. This study proposes a visual infection control positioning system for aged care facilities using the RFID (Radio Frequency Identification) technology and IoT technology. It can calculate the distance between users and reduce the positioning error. The system combines location fingerprinting with the K-nearest neighbor algorithm to fix errors caused by signal fluctuation of RFID to improve the positioning accuracy. The system records the indoor movement path of the residents in a real-time manner through the positioning function. When infectious diseases spread in aged care living residences, to help medical personnel, the system can screen out residents with a high risk of infection who closely contact with patients according to the movement path of the patients and other residents, and visually display them. For some infectious diseases, residents who live together with patients may be infected even if they do not have contact with the patients closely. This system can also identify the residents whose cumulative contact time exceeds the limit according to the medical definition of various infectious diseases and input time value.
近年来，由于人口老龄化和平均寿命延长，老年护理生活受到关注。此外，信息通信技术和物联网的快速发展为传感器网络和云计算在医疗保健中的应用奠定了基础。大多数老年人由于免疫力较弱，常患慢性病，感染风险较高。感染控制问题是老年护理机构居住者的一个重要问题。过去，为了控制感染，医务人员采用手动跟踪、调查和询问患者的方法来获取患者接触名单。这种方法无法识别感染风险高的居民，无法区分患者与其他居民之间的距离，也无法评估感染风险。隔离所有与患者接触的居民是唯一的解决办法，但这不仅会引起其他居民的反感，还会浪费医疗资源。本研究提出一种基于射频识别（RFID）技术和物联网技术的老年护理机构可视化感染控制定位系统。它可以计算用户之间的距离并减少定位误差。该系统将位置指纹与K近邻算法相结合，以修正由RFID信号波动引起的误差，提高定位精度。该系统通过定位功能实时记录居民的室内移动路径。当传染病在老年护理居住场所传播时，为帮助医务人员，该系统可根据患者和其他居民的移动路径筛选出与患者密切接触且感染风险高的居民，并进行可视化显示。对于某些传染病，即使没有与患者密切接触，与患者同住的居民也可能被感染。该系统还可以根据各种传染病的医学定义和输入的时间值，识别出累计接触时间超过限制的居民。
