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.
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References
Anguita, D., Ghio, A., Oneto, L., Parra Perez, X., Reyes Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 437–442 (2013)
Learning, U. C. I. M.: Human activity recognition with smartphones. Kaggle (13 Nov 2019). https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. Journal of machine learning research, 9(11) (2008)
Chernbumroong, S., Atkins, A.S., Yu, H.: Activity classification using a single wrist-worn accelerometer. In: 2011 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA) Proceedings, pp. 1–6. IEEE (Sept 2011)
Chetty, G., White, M., Akther, F.: Smart phone based data mining for human activity recognition. Procedia Comput. Sci. 46, 1181–1187 (2015)
Gulzar, Z., Leema, A.A., Malaserene, I.: Human Activity Analysis Using Machine Learning Classification Techniques. Int. J. Innov. Technol. Explor. Eng. (IJITEE) (2019)
Imran, H.A., Wazir, S., Iftikhar, U., Latif, U.: Classifying Human Activities with Inertial Sensors: A Machine Learning Approach (2021). arXiv preprint arXiv:2111.05333
Zebin, T., Scully, P.J., Ozanyan, K.B.: Human activity recognition with inertial sensors using a deep learning approach. In: 2016 IEEE SENSORS, pp. 1–3. IEEE (Oct, 2016)
Oh, S., Ashiquzzaman, A., Lee, D., Kim, Y., Kim, J.: Study on human activity recognition using semi-supervised active transfer learning. Sensors 21(8), 2760 (2021)
Schober, P., Vetter, T.R.: Logistic regression in medical research. Anesthesia Analgesia 132(2), 365 (2021)
Chapter logistic regression. Stanford University (29 Dec 2021). Retrieved January 3, 2022, from https://www.web.stanford.edu/~jurafsky/slp3/5.pdf
Liu, L., Shen, B., Wang, X.: Research on kernel function of support vector machine. In: Advanced Technologies, Embedded and Multimedia for Human-Centric Computing, pp. 827–834. Springer, Dordrecht (2014)
Gaye, B., Zhang, D., Wulamu, A.: Improvement of support vector machine algorithm in big data background. Math. Probl. Eng. 2021 (2021)
Achirul Nanda, M., Boro Seminar, K., Nandika, D., Maddu, A.: A comparison study of kernel functions in the support vector machine and its application for termite detection. Information 9(1), 5 (2018)
Izza, Y., Ignatiev, A., Marques-Silva, J.: On explaining decision trees (2020). arXiv preprint arXiv:2010.11034
Sherstinsky, A.: Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D Nonlin. Phenomena 404, 132306 (2020)
Zeyer, A., Doetsch, P., Voigtlaender, P., Schlüter, R., Ney, H.: A comprehensive study of deep bidirectional LSTM RNNs for acoustic modeling in speech recognition. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2462–2466. IEEE (2017, March)
Yang, S., Yu, X., Zhou, Y.: LSTM and GRU neural network performance comparison study: taking yelp review dataset as an example. In: 2020 International Workshop on Electronic Communication and Artificial Intelligence (IWECAI), pp. 98–101. IEEE (2020, June)
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Sanku, S.U., Satti, T.P., Lakshmi, T.J., Nandini, Y.V. (2023). Classifying Human Activities Using Machine Learning and Deep Learning Techniques. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_2
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