Machine Learning Algorithms for Human Activity Recognition
K.R. Baskaran1, M.N. Saroja2
1Dr. K.R. Baskaran, Professor, Department of Computer Science and Engineering, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
2M.N. Saroja, Assistant Professor, Department of Information Technology, Kumaraguru College of Technology, Coimbatore (Tamil Nadu), India.
Manuscript received on 07 October 2019 | Revised Manuscript received on 21 October 2019 | Manuscript Published on 26 December 2019 | PP: 401-403 | Volume-8 Issue-12S October 2019 | Retrieval Number: L110010812S19/2019©BEIESP | DOI: 10.35940/ijitee.L1100.10812S19
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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: It becomes essential to monitor the Activity of Daily Living(ADL) of elderly people living alone by keeping track of their day to day activities & helping those having strong health issues. In this paper various machine learning algorithms for human activity recognition is analyzed. Along with this, an extensive study is carried out to learn about the current technologies used in activity recognition. Activity recognition is generally done in the form of signals generated through sensors. The signals are then preprocessed, segmented, features are extracted and activity is recognized. The main objective of Human Activity Recognition System is to explore the limitations of self-dependent old age persons and suggest ways of overcoming it. By using the different wearable and non-wearable sensors, one can easily monitor the human activity and evaluate the data generated through it.
Keywords: Machine Learning, Human Activity Recognition, Hidden Markov Model.
Scope of the Article: Machine Learning