Song Wen, Ade Carl, Broxterman Ryan, Barstow Thomas, Nelson Thomas, Warren Steve
Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:1586-9. doi: 10.1109/EMBC.2012.6346247.
Accelerometer data provide useful information about subject activity in many different application scenarios. For this study, single-accelerometer data were acquired from subjects participating in field tests that mimic tasks that astronauts might encounter in reduced gravity environments. The primary goal of this effort was to apply classification algorithms that could identify these tasks based on features present in their corresponding accelerometer data, where the end goal is to establish methods to unobtrusively gauge subject well-being based on sensors that reside in their local environment. In this initial analysis, six different activities that involve leg movement are classified. The k-Nearest Neighbors (kNN) algorithm was found to be the most effective, with an overall classification success rate of 90.8%.
加速度计数据在许多不同的应用场景中提供了有关受试者活动的有用信息。在本研究中，从参与模拟宇航员在微重力环境中可能遇到的任务的现场测试的受试者那里获取了单加速度计数据。这项工作的主要目标是应用分类算法，这些算法可以根据相应加速度计数据中存在的特征来识别这些任务，最终目标是建立基于受试者当地环境中的传感器来无创评估其健康状况的方法。在本次初步分析中，对六种涉及腿部运动的不同活动进行了分类。发现k近邻（kNN）算法最为有效，总体分类成功率为90.8%。
