Classifying Human Activities Using Machine Learning and Deep Learning Techniques

SANKU, Satya Uday, SATTI, Thanuja Pavani, TANGIRALA, Jaya Lakshmi and NANDINI, YV (2023). Classifying Human Activities Using Machine Learning and Deep Learning Techniques. In: BHAJETA, Vikrant, CARROLL, Fiona, TAVARES, João Manuel R. S., SENGHAR, Sandeep Singh and PEER, Peter, (eds.) Intelligent Data Engineering and Analytics. Proceedings of the 11th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2023). Smart Innovation, Systems and Technologies (371). Singapore, Springer Nature Singapore, 19-29.

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Abstract

The ability of machines to recognize and categorize human activities is known as human activity recognition (HAR). Most individuals today are health aware; thus, they use smartphones or smartwatches to track their daily activities to stay healthy. Kaggle held a challenge to classify six human activities using smartphone inertial signals from 30 participants. HAR’s key difficulty is distinguishing human activities using data so they do not overlap. Expert-generated features are visualized using t-SNE, then logistic regression, linear SVM, kernel SVM, and decision trees are used to categorize the six human activities. Deep learning algorithms of LSTM, bidirectional LSTM, RNN, and GRU are also trained using raw time series data. These models are assessed using accuracy, confusion matrix, precision, and recall. Empirical findings demonstrated that the linear support vector machine (SVM) in the realm of machine learning, as well as the gated recurrent unit (GRU) in deep learning, obtained higher accuracy for human activity recognition.

Item Type:	Book Section
Additional Information:	Series ISSN - 2190-3026 FICTA 2023 11-12 April, Cardiff, UK.
Identification Number:	https://doi.org/10.1007/978-981-99-6706-3_2
Page Range:	19-29
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	06 Mar 2024 13:47
Last Modified:	07 Mar 2024 12:56
URI:	https://shura.shu.ac.uk/id/eprint/33359

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