Human Activity Recognition using Deep and Machine Learning Algorithms
Bharathi B1, Bhuvana J2
1Bharathi B*, Department of CSE, SSN College of Engineering, Chennai, India.
2Bhuvana J, Department of CSE, SSN College of Engineering, Chennai, India.
Manuscript received on January 18, 2020. | Revised Manuscript received on January 28, 2020. | Manuscript published on February 10, 2020. | PP: 2460-2666 | Volume-9 Issue-4, February 2020. | Retrieval Number: C8835019320/2020©BEIESP | DOI: 10.35940/ijitee.C8835.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Activity recognition in humans is one of the active challenges that finds its application in numerous fields such as, medical health care, military, manufacturing, assistive techniques and gaming. Due to the advancements in technologies the usage of smartphones in human lives become inevitable. The sensors in the smartphones help us to measure the essential vital parameters. These measured parameters enable us to monitor the activities of humans, which we call as human activity recognition. In this paper, we have proposed an automatic human activity recognition system that independently recognizes the actions of the humans. Four deep learning approaches and thirteen different machine learning classifiers such as Multilayer Perceptron, Random Forest, Support Vector Machine, Decision Tree Classifier, AdaBoost Classifier, Gradient Boosting Classifier and others are applied to identify the efficient classifier for human activity recognition. Our proposed system is able to recognize the activities such as Laying, Sitting, Standing, Walking, Walking downstairs and Walking upstairs. Benchmark dataset has been used to evaluate all the classifiers implemented. We have investigated all these classifiers to identify a best suitable classifier for this dataset. The results obtained show that, the Multilayer Perceptron has obtained 98.46% of overall accuracy in detecting the activities. The second-best performance was observed when the classifiers are combined together.
Keywords: Deep Learning Classifiers, Human Activity Recognition, Machine learning Classifiers, Sensors, Smart Phones.
Scope of the Article: Machine learning