Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:2180-2185. doi: 10.1109/EMBC46164.2021.9630573.
The Center for Eldercare and Rehabilitation Technology, at University of Missouri, has researched the use of smart, unobtrusive sensors for older adult residents' health monitoring and alerting in aging-in-place communities for many years. Sensors placed in the apartments of older adult residents generate a deluge of daily data that is automatically aggregated, analyzed, and summarized to aid in health awareness, clinical care, and research for healthy aging. When anomalies or concerning trends are detected within the data, the sensor information is converted into linguistic health messages using fuzzy computational techniques, so as to make it understandable to the clinicians. Sensor data are analyzed at the individual level, therefore, through this study we aim to discover various combinations of patterns of anomalies happening together and recurrently in the older adult's population using these text summaries. Leveraging various computational text data processing techniques, we are able to extract relevant analytical features from the health messages. These features are transformed into a transactional encoding, then processed with frequent pattern mining techniques for association rule discovery. At individual level analysis, resident ID 3027 was considered as an exemplar to describe the analysis. Seven combinations of anomalies/rules/associations were discovered in this resident, out of which rule group three showed an increased recurrence during the COVID lockdown of facility. At the population level, a total of 38 associations were discovered that highlight the health patterns, and we continue to explore the health conditions associated with them. Ultimately, our goal is to correlate the combinations of anomalies with certain health conditions, which can then be leveraged for predictive analytics and preventative care. This will improve the current clinical care systems for older adult residents in smart sensor, aging-in-place communities.
密苏里大学老年护理和康复技术中心多年来一直在研究使用智能、不显眼的传感器来监测和提醒老年人居民的健康，以帮助他们在就地老龄化社区中生活。放置在老年人居民公寓中的传感器会产生大量日常数据，这些数据会自动汇总、分析和总结，以帮助提高健康意识、临床护理和健康老龄化研究。当数据中检测到异常或令人担忧的趋势时，传感器信息会使用模糊计算技术转换为语言健康信息，以便临床医生能够理解。传感器数据是在个体层面上进行分析的，因此，通过这项研究，我们旨在通过这些文本摘要发现老年人居民群体中各种异常模式组合的发生和反复出现的情况。通过利用各种计算文本数据处理技术，我们能够从健康信息中提取相关的分析特征。这些特征被转换为事务编码，然后使用频繁模式挖掘技术进行关联规则发现。在个体层面分析中，居民 ID 3027 被视为一个示例来描述分析。在这位居民中发现了七种异常/规则/关联组合，其中规则组三在设施 COVID 封锁期间表现出了更高的复发率。在人群层面上，共发现了 38 个关联，突出了健康模式，我们将继续探索与之相关的健康状况。最终，我们的目标是将异常组合与某些健康状况相关联，然后可以将其用于预测分析和预防保健。这将改善智能传感器、就地老龄化社区中老年人居民的现有临床护理系统。
