Abstract
The relevance of automated recognition of human behaviors or actions stems from the breadth of its potential uses, which includes, but is not limited to, surveillance, robots, and personal health monitoring. Several computer vision-based approaches for identifying human activity in RGB and depth camera footage have emerged in recent years. Techniques including space-time trajectories, motion indoctrination, key pose extraction, tenancy patterns in 3D space, motion maps in depth, and skeleton joints are all part of the mix. These camera-based methods can only be used inside a constrained area and are vulnerable to changes in lighting and clutter in the backdrop. Although wearable inertial sensors offer a potential answer to these issues, they are not without drawbacks, including a reliance on the user’s knowledge of their precise location and orientation. Several sensing modalities are being used for reliable human action detection due to the complimentary nature of the data acquired from the sensors. This research therefore introduces a two-tiered hierarchical approach to activity recognition by employing a variety of wearable sensors. Dwarf mongoose optimization process is used to extract the handmade features and pick the best features (DMOA). It predicts the composite’s behavior by emulating how DMO searches for food. The DMO hive is divided into an alpha group, scouts, and babysitters. Every community has a different strategy to corner the food supply. In this study, we tested out a number of different methods for video categorization and action identification, including ConvLSTM, LRCN and C3D. The projected human action recognition (HAR) framework is evaluated using the UTD-MHAD dataset, which is a multimodal collection of 27 different human activities that is available to the public. The suggested feature selection model for HAR is trained and tested using a variety of classifiers. It has been shown experimentally that the suggested technique outperforms in terms of recognition accuracy.
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Kumar, M.R., Likhitha, A., Komali, A., Keerthana, D., Gowthami, G. (2023). Multimodal Body Sensor for Recognizing the Human Activity Using DMOA Based FS with DL. In: Kadry, S., Prasath, R. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2023. Lecture Notes in Computer Science(), vol 13924. Springer, Cham. https://doi.org/10.1007/978-3-031-44084-7_1
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