Abstract
Human Activity Recognition (HAR) has become one of the most prominent research topics in the field of ubiquitous computing and pattern recognition over the last decade. In this paper, a comparative analysis of 17 different algorithms is done using a 4-core 940mx machine and a 16-core G4dn.4xlarge Elastic Compute (EC2) instance on a public domain HAR dataset using accelerometer and gyroscope data from the inertial sensors in smartphones. The results are evaluated using the metrics accuracy, F1-score, precision, recall, training time, and testing time. The Machine Learning (ML) models implemented include Logistic Regression (LR), Support Vector Classifier (SVC), Random Forest (RF), Decision Trees (DT), Gradient Boosted Decision Trees (GBDT), linear and Radial Basis Function (RBF) kernel Support Vector Machines (SVM), K- Nearest Neighbors (KNN) and Naive Bayes (NB). The Deep Learning (DL) models implemented include Convolutional Neural Networks (CNN), Long Short Term Memory (LSTM), a combination of CNN-LSTM and Bidirectional LSTM. Neural Structure Learning was also implemented over a CNN-LSTM model along with Deep Belief Networks (DBN). It is identified that the Deep Learning models CNN, LSTM, CNN-LSTM & CNN-BLSTM consistently confuse between dynamic activities and that the machine learning models confuse between static activities. A Divide and Conquer approach was implemented on the dataset and CNN achieved an accuracy of 99.92% on the dynamic activities, whereas the CNN-LSTM model achieved an accuracy of 96.73% eliminating confusion between the static and dynamic activities. Maximum classification accuracy of 99.02% was achieved by DBN on the full dataset after Gaussian standardization. The proposed DBN model is much more efficient, lightweight, accurate, and faster in its classification than the existing models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kasteren, T.V., Englebienne, G., Kröse, B.: An activity monitoring system for elderly care using generative and discriminative models. Pers. Ubiquit. Comput. 14, 489–498 (2009). https://doi.org/10.1007/s00779-009-0277-9
Muralidharan, K., Ramesh, A., Rithvik, G., Prem, S., Reghunaath, A.A., Gopinath, M.P.: 1D Convolution approach to human activity recognition using sensor data and comparison with machine learning algorithms. Int. J. Cogn. Comput. Eng. 2, 130–143 (2021). https://doi.org/10.1016/j.ijcce.2021.09.001
Dorn, D., Gorzelitz, J., Gangnon, R., Bell, D., Koltyn, K., Cadmus-Bertram, L.: Automatic identification of physical activity type and duration by wearable activity trackers: a validation study. JMIR Mhealth Uhealth 7(5), e13547 (2019). https://doi.org/10.2196/13547
San-Segundo, R., Blunck, H., Moreno-Pimentel, J., Stisen, A., Gil-Martín, M.: Robust human activity recognition using smartwatches and smartphones. Eng. Appl. Artif. Intell. 72, 190–202 (2018). https://doi.org/10.1016/j.engappai.2018.04.002
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013, Bruges, Belgium, 24–26 April 2013
Bayat, A., Pomplun, M., Tran, D.A.: A study on human activity recognition using accelerometer Dda from smartphones. Proc. Comput. Sci. 34, 450–457 (2014). https://doi.org/10.1016/j.procs.2014.07.009
Golestani, N., Moghaddam, M.: Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks. Nat. Commun. 11(1), 1551 (2020). https://doi.org/10.1038/s41467-020-15086-2
Wang, H.: Wearable sensor-based human activity recognition using hybrid deep learning techniques. Sec. Commun. Netw. 2020, e2132138 (2020). https://doi.org/10.1155/2020/2132138
Uddin, M.Z., Soylu, A.: Human activity recognition using wearable sensors, discriminant analysis, and long short-term memory-based neural structured learning. Sci. Rep. 11(1), 16455 (2021). https://doi.org/10.1038/s41598-021-95947-y
Wang, L.: Recognition of human activities using continuous autoencoders with wearable sensors. Sens. (Basel, Switzerland) 16(2), 189 (2016). https://doi.org/10.3390/s16020189
Dua, D., Graff, C.: UCI Machine Learning Repository. Opgehaal van (2017). http://archive.ics.uci.edu/ml
Banos, O.: Design, implementation and validation of a novel open framework for agile development of mobile health applications. Biomed. Eng. Online 14(Suppl 2), S6 (2015). https://doi.org/10.1186/1475-925X-14-S2-S6
Banos, O., et al.: mHealthDroid: a novel framework for agile development of mobile health applications. In: Pecchia, L., Chen, L.L., Nugent, C., Bravo, J. (eds.) IWAAL 2014. LNCS, vol. 8868, pp. 91–98. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13105-4_14
Sikder, N., Chowdhury, Md.S., Arif, A.S.M., Nahid, A.-A.: Human activity recognition using multichannel convolutional neural network. In: 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), pp. 560–565 (2019). https://doi.org/10.1109/ICAEE48663.2019.8975649
Crucian, F., et al.: Feature learning for human activity recognition using convolutional neural networks: a case study for Inertial measurement unit and audio data. CCF Trans. Pervasive Comput. Inter. 2(1), 18–32 (2020). https://doi.org/10.1007/s42486-020-00026-2
Ullah, M., Ullah, H., Khan, S.D., Cheikh, F.A.: Stacked Lstm network for human activity recognition using smartphone data. In: 2019 8th European Workshop on Visual Information Processing (EUVIP), pp. 175–180 (2019).https://doi.org/10.1109/EUVIP47703.2019.8946180
Ramachandran, K., Pang, J.: Transfer Learning Technique for Human Activity Recognition based on Smartphone Data. 18 (n.d.)
Roobini, S., Naomi, J.F.: Smartphone sensor based human activity recognition using deep learning Models. 8(1), 9 (2019)
Khatiwada, P., Chatterjee, A., Subedi, M.: Automated human Activity Recognition by Colliding Bodies Optimization-based Optimal Feature Selection with Recurrent Neural Network. arXiv:2010.03324 [Cs, Eess] (2021)
Rabbi, J., Fuad, M.T.H., Awal, M.A.: Human Activity Analysis and Recognition from Smartphones using Machine Learning Techniques. arXiv:2103.16490 [Cs] (2021)
Han, P.Y., Ping, L.Y., Ling, G.F., Yin, O.S., How, K.W.: Stacked deep analytic model for human activity recognition on a UCI HAR database (10:1046). F1000Research (2021). https://doi.org/10.12688/f1000research.73174.1
Oh, S., Ashiquzzaman, A., Lee, D., Kim, Y., Kim, J.: Study on human activity recognition using semi-supervised active transfer learning. Sensors (Basel, Switzerland) 21(8), 2760 (2021). https://doi.org/10.3390/s21082760
Huang, W., Zhang, L., Gao, W., Min, F., He, J.: Shallow convolutional neural networks for human activity recognition using wearable sensors. IEEE Trans. Instrum. Meas. 70, 1–11 (2021). https://doi.org/10.1109/TIM.2021.3091990
Xia, K., Huang, J., Wang, H.: LSTM-CNN Architecture for Human Activity Recognition. IEEE Access 8, 56855–56866 (2020). https://doi.org/10.1109/ACCESS.2020.2982225
Nambissan, G.S., Mahajan, P., Sharma, S., Gupta, N.: The variegated applications of deep learning techniques in human activity recognition. In: 2021 Thirteenth International Conference on Contemporary Computing (IC3-2021), pp. 223–233 (2021). https://doi.org/10.1145/3474124.3474156
Tang, Y., Teng, Q., Zhang, L., Min, F., He, J.: Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors. IEEE Sens. J. 21(1), 581–592 (2021). https://doi.org/10.1109/JSEN.2020.3015521
Bashar, S.K., Al Fahim, A., Chon, K.H.: Smartphone based human activity recognition with feature selection and dense neural network. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, vol. 2020, pp. 5888–5891 (2020). https://doi.org/10.1109/EMBC44109.2020.9176239
Zhang, Y., Ramachandran, K. M.: Offline Machine Learning for Human Activity Recognition with Smartphone. 6 (n.d.)
Nematallah, H., Rajan, S.: Comparative study of time series-based human activity recognition using convolutional neural networks. In: 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6 (2020). https://doi.org/10.1109/I2MTC43012.2020.9128582
Ankita, R.S., Babbar, H., Coleman, S., Singh, A., Aljahdali, H.M.: An efficient and lightweight deep learning model for human activity recognition using smartphones. Sensors 21(11), 3845 (2021). https://doi.org/10.3390/s21113845
Bulling, A., Blanke, U., Schiele, B.: A tutorial on human activity recognition using body-worn inertial sensors. ACM Comput. Surv. 46(3), 33:1–33:33 (2014). https://doi.org/10.1145/2499621
Abadi, M., et al.: TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. arXiv:1603.04467 [Cs] (2016)
Chollet, F., et al.: Keras. GitHub (2015). https://github.com/fchollet/keras
Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(null), 2825–2830 (2011)
Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006). https://doi.org/10.1162/neco.2006.18.7.1527
albertbup. Deep-belief-network [Python] (2021). https://github.com/albertbup/deep-belief-network (Original work published 2015)
Gopalan, A., et al.: Neural structured learning: training neural networks with structured signals. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 1150–1153 (2021). https://doi.org/10.1145/3437963.3441666
Harris, C.R., et al.: Array programming with NumPy. Nature 585(7825), 357–362 (2020). https://doi.org/10.1038/s41586-020-2649-2
McKinney, W.: Data Structures for Statistical Computing in Python. 56–61 (2010). https://doi.org/10.25080/Majora-92bf1922-00a
Waskom, M.L.: seaborn: Statistical data visualization. J. Open Source Soft. 6(60), 3021 (2021). https://doi.org/10.21105/joss.03021
Hunter, J.D.: Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007). https://doi.org/10.1109/MCSE.2007.55
Ke, G.: LightGBM: a highly efficient gradient boosting decision tree. In: Advances in Neural Information Processing Systems, vol. 30 (2017). https://papers.nips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html
Reiss, A., Stricker, D.: Introducing a new benchmarked dataset for activity monitoring. In: 2012 16th International Symposium on Wearable Computers, pp. 108–109 (2012). https://doi.org/10.1109/ISWC.2012.13
Sikder, N., Nahid, A.-A.: KU-HAR: an open dataset for heterogeneous human activity recognition. Pattern Recogn. Lett. 146, 46–54 (2021). https://doi.org/10.1016/j.patrec.2021.02.024
Sutharsan, V., et al.: Electroencephalogram signal processing with independent component analysis and cognitive stress classification using convolutional neural networks. In: Mahapatra, R.P., Peddoju, S.K., Roy, S., Parwekar, P., Goel, L. (eds.) Proceedings of International Conference on Recent Trends in Computing. LNNS, vol. 341, pp. 275–292. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-7118-0_24
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 IFIP International Federation for Information Processing
About this paper
Cite this paper
Swaminathan, A. (2022). Comparative Analysis of Sensor-Based Human Activity Recognition Using Artificial Intelligence. In: Kalinathan, L., R., P., Kanmani, M., S., M. (eds) Computational Intelligence in Data Science. ICCIDS 2022. IFIP Advances in Information and Communication Technology, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-16364-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-031-16364-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16363-0
Online ISBN: 978-3-031-16364-7
eBook Packages: Computer ScienceComputer Science (R0)