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A new approach for physical human activity recognition based on co-occurrence matrices
The Journal of Supercomputing ( IF 3.3 ) Pub Date : 2021-06-04 , DOI: 10.1007/s11227-021-03921-2
Fatma Kuncan 1 , Yılmaz Kaya 1 , Ramazan Tekin 2 , Melih Kuncan 3
Affiliation  

In recent years, it has been observed that many researchers have been working on different areas of detection, recognition and monitoring of human activities. The automatic determination of human physical activities is often referred to as human activity recognition (HAR). One of the most important technology that detects and tracks the activity of the human body is sensor-based HAR technology. In recent days, sensor-based HAR attracts attention in the field of computers due to its wide use in daily life and is a rapidly growing field of research. Activity recognition (AR) application is carried out by evaluating the signals obtained from various sensors placed in the human body. In this study, a new approach is proposed to extract features from sensor signals using HAR. The proposed approach is inspired by the Gray Level Co-Occurrence Matrix (GLCM) method, which is widely used in image processing, but it is applied to one-dimensional signals, unlike GLCM. Two datasets were used to test the proposed approach. The datasets were created from the signals obtained from the accelerometer, gyro and magnetometer sensors. Heralick features were obtained from co-occurrence matrix created after 1D-GLCM (One (1) Dimensional-Gray Level Co-Occurrence Matrix) was applied to the signals. HAR operation has been carried out for different scenarios using these features. Success rates of 96.66 and 93.88% were obtained for two datasets, respectively. It has been observed that the new approach proposed within the scope of the study provides high success rates for HAR applications. It is thought that the proposed approach can be used in the classification of different signals.



中文翻译:

基于共现矩阵的人体活动识别新方法

近年来,据观察,许多研究人员一直致力于人类活动的检测、识别和监测的不同领域。人类身体活动的自动确定通常被称为人类活动识别(HAR)。检测和跟踪人体活动的最重要技术之一是基于传感器的 HAR 技术。近年来,基于传感器的HAR因其在日常生活中的广泛应用而受到计算机领域的关注,是一个快速发展的研究领域。通过评估从放置在人体中的各种传感器获得的信号来执行活动识别 (AR) 应用。在这项研究中,提出了一种使用 HAR 从传感器信号中提取特征的新方法。所提出的方法受到灰度共生矩阵 (GLCM) 方法的启发,该方法广泛用于图像处理,但与 GLCM 不同,它适用于一维信号。使用两个数据集来测试所提出的方法。数据集是根据从加速度计、陀螺仪和磁力计传感器获得的信号创建的。Heralick 特征是从在将 1D-GLCM(一 (1) 维灰度共现矩阵)应用于信号后创建的共现矩阵中获得的。已经使用这些特性针对不同的场景进行了 HAR 操作。两个数据集的成功率分别为 96.66% 和 93.88%。据观察,在研究范围内提出的新方法为 HAR 应用提供了高成功率。

更新日期:2021-06-04
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