Abstract
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.
Highlights
Activity recognition has been an important task for researchers in the field of gaming [1], assisted living [2], sports analysis [3], logistics and other industrial operations [4] and for monitoring of patients diseases, such as Parkinsons to build activity profiles for therapeutic purposes [5]
Data from the Logistics Activity Recognition Challenge (LARa) dataset [11] is utilized to perform continuous recognition of activities in a logistics scenario using two different convolutional neural network (CNN) architectures, one is a typical convolutional network and the other is a modified version of the parallel CNN architecture called CNN-inertial measurement sensors (IMUs) suggested in [12] which performs convolutions in the temporal domain
Segments are first extracted from the IMU sensor data for each trial which are passed to the CNNs to test their performance
Summary
Activity recognition has been an important task for researchers in the field of gaming [1], assisted living [2], sports analysis [3], logistics and other industrial operations [4] and for monitoring of patients diseases, such as Parkinsons to build activity profiles for therapeutic purposes [5]. Eventhough activity recognition, generally speaking, can be performed in a variety of ways [6], [7], [8], inertial sensors have been far by the most popular modality to use for this purpose. This is due to the fact that they are mobile, less cumbersome to wear and cost less than sensing devices for other modalities. With their incorporation in phones and smart watches etc, these sensors are usually available to the subject for use in activity recognition tasks.
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