Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm
Human activity recognition from smart watch sensor data using a hybrid of principal component analysis and random forest algorithm
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DOI：
10.1177/0020294018813692
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发表时间：
2019-01-01
影响因子：
2
通讯作者：
Peker, Musa
中科院分区：
文献类型：
--
作者：
Balli, Serkan;Sagbas, Ensar Arif;Peker, Musa
背景：人体运动的检测在诸如医疗保健、健身和老年人护理的各个领域中是一项重要任务。现在可以使用移动的应用程序来实现这一目标。这些应用程序使用户、医生和相关人员更好地了解日常身体活动。它还可以通过跟踪用户在日常生活中的活动来养成各种有用的习惯。此外，还识别老人或幼儿跌倒等危险动作，并尽快采取必要的预防措施。利用运动传感器数据对人体运动进行分类是当前的研究课题之一。智能手表内置了这些传感器。因此，可以跟踪仅携带智能手表的用户的活动。方法：这项工作的目的是使用智能手表传感器数据和机器学习方法来检测人体运动。数据从智能手表的加速计、陀螺仪、计步器和心率传感器获得。所获得的数据已被划分为2 s窗口，并为每个类创建了包含每个类的500个模式的数据集。结果和讨论：在特征确定后，采用随机森林、支持向量机、C4. 5和k-近邻法对主成分分析后的数据集进行分类，并比较了它们的分类性能。最成功的结果来自随机森林方法。
Background: Detecting of human movements is an important task in various areas such as healthcare, fitness and eldercare. It is now possible to achieve this aim using mobile applications. These applications provide users, doctors and related persons a better understanding about daily physical activities. It can also lead to various useful habits by following the activities of the users in their daily life. In addition, dangerous actions such as the fall of elderly people or young children are identified and necessary precautions are taken as soon as possible. Classification of human motions with motion sensor data is among the current topics of study. Smart watches have these sensors built-in. Thus, it is possible to follow the activities of a user carrying only a smart watch. Methods: The purpose of this work is to detect human movements using smart watch sensor data and machine learning methods. The data are obtained from the accelerometer, gyroscope, step counter and heart rate sensors of the smart watch. The obtained data have been divided into 2 s windows and a data set containing 500 patterns for each class has been created for each class. Results and Discussion: After the features were determined, the data set to which the principal component analysis has been applied was classified by random forest, support vector machine, C4.5 and k-nearest neighbor methods, and their performances were compared. The most successful result was obtained from the random forest method.