文章基本信息

标题：Continuous Human Activity Recognition in Logistics from Inertial Sensor Data using Temporal Convolutions in CNN
其他标题：Continuous Human Activity Recognition in Logistics from Inertial Sensor Data using Temporal Convolutions in CNN
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作者：Abbas Shah Syed ; Zafi Sherhan Syed ; Areez Khalil Memon 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：10
DOI：10.14569/IJACSA.2020.0111074
出版社：Science and Information Society (SAI)
摘要：Human activity recognition has been an important task for the research community. With the introduction of deep learning architectures, the performance of activity recognition algorithms has improved significantly. However, most of the research in this area has focused on activity recognition for health/assisted living with other applications being given less attention. This paper considers continuous activity recognition in logistics (order picking and packing operations) using a convolutional neural network with temporal convolutions on inertial measurement sensor data from the recently released LARa dataset. Four variants of the popular CNN-IMU are experimented upon and a discussion of the results is provided. The results indicate that temporal convolutions are able to achieve satisfactory performance for some activities (hand center and cart) whereas they perform poorly for the activities of stand and hand up.
关键词：Convolutional Neural Networks; deep learning; Human Activity Recognition (HAR); inertial sensors; LARa dataset