Jarvis Leighanne, Moninger Sarah, Pavon Juliessa, Throckmorton Chandra, Caves Kevin
Duke University, Durham NC 27710, USA.
Signal Analysis Solutions, Bahama NC 27503, USA.
Comput Help People Spec Needs. 2020 Sep;12377:242-249. doi: 10.1007/978-3-030-58805-2_29. Epub 2020 Sep 4.
This manuscript describes tests and results of a study to evaluate classification algorithms derived from accelerometer data collected on healthy adults and older adults to better classify posture movements. Specifically, tests were conducted to 1) compare performance of 1 sensor vs. 2 sensors; 2) examine custom trained algorithms to classify for a given task 3) determine overall classifier accuracy for healthy adults under 55 and older adults (55 or older). Despite the current variety of commercially available platforms, sensors, and analysis software, many do not provide the data granularity needed to characterize all stages of movement. Additionally, some clinicians have expressed concerns regarding validity of analysis on specialized populations, such as hospitalized older adults. Accurate classification of movement data is important in a clinical setting as more hospital systems are using sensors to help with clinical decision making. We developed custom software and classification algorithms to identify laying, reclining, sitting, standing, and walking. Our algorithm accuracy is 93.2% for healthy adults under 55 and 95% for healthy older adults over 55 for the tasks in our setting. The high accuracy of this approach will aid future investigation into classifying movement in hospitalized older adults. Results from these tests also indicate that researchers and clinicians need to be aware of sensor body position in relation to where the algorithm used was trained. Additionally, results suggest more research is needed to determine if algorithms trained on one population can accurately be used to classify data from another population.
本手稿描述了一项研究的测试和结果，该研究旨在评估从健康成年人和老年人收集的加速度计数据中得出的分类算法，以更好地对姿势运动进行分类。具体而言，进行了以下测试：1）比较1个传感器与2个传感器的性能；2）检查针对给定任务进行分类的定制训练算法；3）确定55岁以下健康成年人和55岁及以上老年人的总体分类器准确性。尽管目前有各种各样的商业可用平台、传感器和分析软件，但许多都没有提供表征运动所有阶段所需的数据粒度。此外，一些临床医生对特殊人群（如住院老年人）的分析有效性表示担忧。在临床环境中，准确分类运动数据很重要，因为越来越多的医院系统正在使用传感器来辅助临床决策。我们开发了定制软件和分类算法来识别躺卧、斜倚、坐着、站立和行走。在我们的设置中，对于55岁以下的健康成年人，我们的算法准确率为93.2%，对于55岁及以上的健康老年人，准确率为95%。这种方法的高准确率将有助于未来对住院老年人运动分类的研究。这些测试结果还表明，研究人员和临床医生需要了解传感器的身体位置与所使用算法的训练位置之间的关系。此外，结果表明需要更多的研究来确定在一个人群上训练的算法是否可以准确地用于对另一人群的数据进行分类。
