Human Actions and Hand Gesture Recognition with Deep Learning
Bapireddygari Hema1, J. Arokia Renjit2

1Bapireddygari Hema*, P.G Scholar, Computer Science and Engineering, Jeppiaar Engineering College, Chennai, (Tamil Nadu), India.
2Dr. J. Arokia Renjith, Professor & Head, Computer Science and Engineering, Jeppiaar Engineering College, Chennai, (Tamil Nadu), India.
Manuscript received on November 25, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 1250-1253 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B2815129219/2020©BEIESP | DOI: 10.35940/ijeat.B2815.129219
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Abstract: Over recent times, deep learning has been challenged extensively to automatically read and interpret characteristic features from large volumes of data. Human Action Recognition (HAR) has been experimented with variety of techniques like wearable devices, mobile devices etc., but they can cause unnecessary discomfort to people especially elderly and child. Since it is very vital to monitor the movements of elderly and children in unattended scenarios, thus, HAR is focused. A smart human action recognition method to automatically identify the human activities from skeletal joint motions and combines the competencies are focused. We can also intimate the near ones about the status of the people. Also, it is a low-cost method and has high accuracy. Thus, this provides a way to help the senior citizens and children from any kind of mishaps and health issues. Hand gesture recognition is also discussed along with human activities using deep learning.
Keywords: Deep Learning, Human Action Recognition, Skeletal images, spatial dependencies and temporal dependencies, Hand gesture recognition, Transfer learning, machine learning, Convolutional Neural Network (CNN), Human Computer Interaction (HCI), Hierarchical spatio-temporal model (HSTM).