PDF(Pubmed)
Abstract：
As Canada\'s population of older adults rises, the need for aging-in-place solutions is growing due to the declining quality of long-term-care homes and long wait times. While the current standards include questionnaire-based assessments for monitoring activities of daily living (ADLs), there is an urgent need for advanced indoor localization technologies that ensure privacy. This study explores the use of Ultra-Wideband (UWB) technology for activity recognition in a mock condo in the Glenrose Rehabilitation Hospital. UWB systems with built-in Inertial Measurement Unit (IMU) sensors were tested, using anchors set up across the condo and a tag worn by patients. We tested various UWB setups, changed the number of anchors, and varied the tag placement (on the wrist or chest). Wrist-worn tags consistently outperformed chest-worn tags, and the nine-anchor configuration yielded the highest accuracy. Machine learning models were developed to classify activities based on UWB and IMU data. Models that included positional data significantly outperformed those that did not. The Random Forest model with a 4 s data window achieved an accuracy of 94%, compared to 79.2% when positional data were excluded. These findings demonstrate that incorporating positional data with IMU sensors is a promising method for effective remote patient monitoring.
摘要：
随着加拿大老年人口的增加，由于长期护理院的质量下降和等待时间长，对就地老化解决方案的需求正在增长。虽然目前的标准包括基于问卷的评估，用于监测日常生活活动(ADL)，迫切需要确保隐私的先进室内定位技术。这项研究探讨了在Glenrose康复医院的模拟公寓中使用超宽带（UWB）技术进行活动识别。测试了具有内置惯性测量单元（IMU）传感器的UWB系统，使用在公寓里设置的锚和病人佩戴的标签。我们测试了各种UWB设置，改变了锚的数量，并改变标签的位置（在手腕或胸部）。手腕佩戴的标签始终优于胸部佩戴的标签，九锚配置产生了最高的精度。开发了机器学习模型以基于UWB和IMU数据对活动进行分类。包含位置数据的模型明显优于不包含位置数据的模型。具有4s数据窗口的随机森林模型实现了94%的准确率，与排除位置数据时的79.2%相比.这些发现表明，将位置数据与IMU传感器相结合是一种有效的远程患者监测的有前途的方法。
