Can a simple approach identify complex nurse care activity?
Can a simple approach identify complex nurse care activity?
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简单的方法可以识别复杂的护士护理活动吗？
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Shoyaib
中科院分区：
文献类型：
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作者：
Md. Eusha Kadir;Pritom Saha Akash;S. Sharmin;A. Ali;M. Shoyaib
在过去的二十年里，越来越复杂的方法已经被开发出来，使用各种类型的传感器来识别人类活动，例如，来自运动捕捉、加速度计和陀螺仪传感器的数据。迄今为止，大多数研究主要集中在识别简单的人类活动，如走路、吃饭和跑步。然而，我们的许多日常生活活动通常比这些更复杂。为了激发对复杂活动识别的研究，“护士护理活动识别挑战”[1]启动，其中根据位置，气压，动作捕捉和加速度计数据识别六种护士活动。我们的团队“IITDU”为此目的研究了简单方法的使用。我们首先从传感器数据中提取特征，并使用最简单的分类器之一，即k近邻（KNN）。使用KNN分类器集合的实验表明，在10倍交叉验证中可以达到大约87%的准确率，在留一个主体的交叉验证中可以达到66%的准确率。
For the last two decades, more and more complex methods have been developed to identify human activities using various types of sensors, e.g., data from motion capture, accelerometer, and gyroscopes sensors. To date, most of the researches mainly focus on identifying simple human activities, e.g., walking, eating, and running. However, many of our daily life activities are usually more complex than those. To instigate research in complex activity recognition, the "Nurse Care Activity Recognition Challenge" [1] is initiated where six nurse activities are to be identified based on location, air pressure, motion capture, and accelerometer data. Our team, "IITDU", investigates the use of simple methods for this purpose. We first extract features from the sensor data and use one of the simplest classifiers, namely K-Nearest Neighbors (KNN). Experiment using an ensemble of KNN classifiers demonstrates that it is possible to achieve approximately 87% accuracy on 10-fold cross-validation and 66% accuracy on leave-one-subject-out cross-validation.