Research Article
IoT based Human Activity Recognition using Deep learning
@ARTICLE{10.4108/eetcasa.v9i1.2682, author={Anwar Ahmad and Ankur Varshney}, title={IoT based Human Activity Recognition using Deep learning}, journal={EAI Endorsed Transactions on Context-aware Systems and Applications}, volume={9}, number={1}, publisher={EAI}, journal_a={CASA}, year={2023}, month={4}, keywords={Artificial intelligence, Internet of things, MoveNet, Pose estimation, Machine learning}, doi={10.4108/eetcasa.v9i1.2682} }- Anwar Ahmad
Ankur Varshney
Year: 2023
IoT based Human Activity Recognition using Deep learning
CASA
EAI
DOI: 10.4108/eetcasa.v9i1.2682
Abstract
Artificial intelligence and the Internet of things (IoT) are the fastest and latest growing technologies that can handle a huge amount of data in computing services. This paper presents a smart human activity recognition system based on IoT that can be used for surveillance purposes working as IoT-based armour. Pose estimation model viz. MoveNet has been employed to extract the anatomical key points from RGB video frames. Different subjects from different camera angles were employed to make the approach person-independent. Diverse Machine learning models such as Decision tree, support vector machines, XGboost, and random forest classifiers were employed using extracted keypoints for training the model for estimating human activity during posture estimation monitoring. SMS are sent to the designated person with the raising of buzzer alarm in case of anomalous behaviour detection.
Copyright © 2023 Salman Ahmad Siddiqui et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
