Abstract
Human activity identification (HAI) is presently a promising field for artificial intelligence researchers. HAI can be applied in various areas in our daily life such as surveillance, health care, etc. There are several machine learning and deep learning techniques that are applied to recognize the human activity. Various multi-dimensional sensors from smartphones, smart tablets, smartwatches can record different types of human activity. Some extensive sensors in smartphones such as accelerometer, gyroscope, microphone, GPS, and camera which can respond to record human gestures. These gestures can be sitting, lying, standing, walking, etc. In this study, the authors analyze the performance of traditional machine learning techniques as well as 1D-CNN architecture to recognize human activity using the UCI human activity identification dataset. PCA has been enforced to reduce the dimension and feature selection to get better results. 1D-CNN performs better in this study with an accuracy of 97.30%. Where Logistic regression achieves 96.00%, Linear Support Vector Machine achieves 93.71%, Kernel-Support Vector Machine achieves 94.85%, Random Forest achieves 90.59% and Decision Tree achieves 84.46% accuracy. All the accuracies are considered by taking the average of individual class accuracy. However, the 1D-CNN model is proposed by the authors to implement in less computational powered devices such as smartphones, smartwatches, etc.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lee, S.M., Yoon, S.M., Cho, H.: Human activity recognition from accelerometer data using convolutional Neural Network. In: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 131–134. Jeju (2017) https://doi.org/10.1109/BIGCOMP.2017.7881728.
Babiker, M., Khalifa, O.O., Htike, K.K., Hassan, A., Zaharadeen, M.: Automated daily human activity recognition for video surveillance using neural network. In: 2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), pp. 1–5. Putrajaya (2017). https://doi.org/10.1109/ICSIMA.2017.8312024.
Liu, C., Ying, J., Han, F., Ruan, M.: Abnormal human activity recognition using bayes classifier and convolutional neural network. In: 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP), pp. 33–37. Shenzhen (2018). https://doi.org/10.1109/SIPROCESS.2018.8600483.
Patel, A.D., Shah, J.H.: Performance Analysis of Supervised Machine Learning Algorithms to Recognize Human Activity in Ambient Assisted Living Environment. In: 2019 IEEE 16th India Council International Conference (INDICON), pp. 1–4. Rajkot, India (2019). https://doi.org/10.1109/INDICON47234.2019.9030353
Mekruksavanich, S., Jitpattanakul, A.: Exercise activity recognition with surface electromyography sensor using machine learning approach. In: 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON), pp. 75–78. Pattaya, Thailand (2020). https://doi.org/10.1109/ECTIDAMTNCON48261.2020.9090711.
Nguyen, T.D.T., et al.: Performance analysis of data parallelism technique in machine learning for human activity recognition using LSTM. In: 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 387–391. Sydney, Australia (2019). https://doi.org/10.1109/CloudCom.2019.00066.
Aljarrah, A.A., Ali, A.H.: Human activity recognition using PCA and BiLSTM recurrent neural networks. In: 2019 2nd International Conference on Engineering Technology and its Applications (IICETA), pp. 156–160. Al-Najef, Iraq (2019). https://doi.org/10.1109/IICETA47481.2019.9012979
Prabowo, O.M., Mutijarsa, K., Supangkat, S.H.: Missing data handling using machine learning for human activity recognition on mobile device. In: 2016 International Conference on ICT For Smart Society (ICISS), pp. 59–62. Surabaya (2016). https://doi.org/10.1109/ICTSS.2016.7792849
Deep, S., Zheng, X.: Leveraging CNN and transfer learning for vision-based human activity recognition. In: 29th International Telecommunication Networks and Applications Conference (ITNAC), pp. 1–4. Auckland, New Zealand (2019). https://doi.org/10.1109/ITNAC46935.2019.9078016
Bhattacharjee, S., Kishore, S., Swetapadma, A.: A comparative study of supervised learning techniques for human activity monitoring using smart sensors. In: 2018 Second International Conference on Advances in Electronics, Computers and Communications (ICAECC), pp. 1–4. Bangalore (2018). https://doi.org/10.1109/ICAECC.2018.8479436.
Hossain, T., Inoue, S.: A comparative study on missing data handling using machine learning for human activity recognition. In: 2019 Joint 8th International Conference on Informatics, Electronics & Vision (ICIEV) and 2019 3rd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), Spokane, WA, USA, pp. 124–129 (2019). https://doi.org/10.1109/ICIEV.2019.8858520.
Singh, D., et al.: Human activity recognition using recurrent neural networks. In: Holzinger A., Kieseberg P., Tjoa A., Weippl E. (eds.) Machine Learning and Knowledge Extraction, CD-MAKE 2017, Lecture Notes in Computer Science, vol. 10410, Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66808-6_18
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, pp. 24–26. Bruges, Belgium, April 2013
Rafi, T.H.: A comparative analysis of hyper-parameter tuned supervised machine learning algorithms on breast cancer prediction. Ann. Eng. 1(1), 0003 (2020)
Jain, H., Khunteta, A., Srivastava, S.: Churn prediction in telecommunication using logistic regression and logit boost. Procedia Comput. Sci. 167, 101–112 (2020). https://doi.org/10.1016/j.procs.2020.03.187
Zhu, C., Idemudia, C.U., Feng, W.: Improved logistic regression model for diabetes prediction by integrating PCA and K-means techniques. Inf. Med. Unlocked 100179, (2019). https://doi.org/10.1016/j.imu.2019.100179
Subasi, A., Ismail Gursoy, M.: EEG signal classification using PCA, ICA, LDA and support vector machines. Expert Syst. Appl. 37(12), 8659–8666 (2010). https://doi.org/10.1016/j.eswa.2010.06.065
Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X., Xiang, Y.: Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: a case study in China. Energy Convers. Manage. 164, 102–111 (2018). https://doi.org/10.1016/j.enconman.2018.02.087
Subudhi, A., Dash, M., Sabut, S.: Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier. Biocybernetics Biomed. Eng. (2019). https://doi.org/10.1016/j.bbe.2019.04.004
Zhang, T., Chen, W., Li, M.: AR based quadratic feature extraction in the VMD domain for the automated seizure detection of EEG using random forest classifier. Biomed. Signal Process. Control 31, 550–559 (2017). https://doi.org/10.1016/j.bspc.2016.10.001
Trabelsi, A., Elouedi, Z., Lefevre, E.: Decision tree classifiers for evidential attribute values and class labels. Fuzzy Sets Syst. (2018). https://doi.org/10.1016/j.fss.2018.11.006
Chen, J.X., Zhang, P.W., Mao, Z.J., Huang, Y.F., Jiang, D.M., Zhang, Y.N.: Accurate EEG-based emotion recognition on combined features using deep convolutional neural networks. IEEE Access 7, 44317–44328 (2019). https://doi.org/10.1109/ACCESS.2019.2908285
Chowdhury, A.I., Ashraf, M., Islam, A., Ahmed, E., Jaman, M.S., Rahman, M.M.: hActNET: an improved neural network based method in recognizing human activities. In: 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Istanbul, Turkey, pp. 1–6 (2020). https://doi.org/10.1109/ISMSIT50672.2020.9254992
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Rafi, T.H., Farhan, F. (2021). A Performance Evaluation of Machine Learning Models on Human Activity Identification (HAI). In: Abraham, A., et al. Proceedings of the 12th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2020). SoCPaR 2020. Advances in Intelligent Systems and Computing, vol 1383. Springer, Cham. https://doi.org/10.1007/978-3-030-73689-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-73689-7_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-73688-0
Online ISBN: 978-3-030-73689-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)