Abstract
Human activity recognition based on sensors (e.g., accelerometer and gyroscope) embedded in smartphones is of great significance for many applications under uncontrolled environments. Although significant progress has been noticed in this field, one of the challenges limiting its real-life applications lies in robust feature extraction for efficient activity recognition on smartphones. This study addresses this challenge by proposing an improved bag-of-words representation for activity signal characterization. Specifically, raw activity signals are processed by discrete wavelet transformation to extract local features, which will be clustered using K-means to form a bag-of-words dictionary. The vocabularies in the dictionary are regarded as bin centers for histogram feature construction. For each local feature of an activity signal, its distance from all the bin centers will be measured. To capture higher-order information for feature representation, the frequency for the bin centers corresponding to the minimum n distances will be updated. Moreover, the frequency is increased by a trigonometry constraint cosine value of the corresponding distances to account for activity signals’ structural information. The proposed feature representation has been verified with three well-established classifiers, namely SVM, ANN, and KNN on the UCI-HAR dataset. The consistently good performance validates the effectiveness and robustness of the proposed feature representation. Compared with the state-of-the-art, the experimental results also demonstrate the advantage of the proposed method in terms of accuracy and computational cost.
Similar content being viewed by others
References
Nweke, H.F., Teh, Y.W., Al-Garadi, M.A., Alo, U.R.: Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: state of the art and research challenges. Expert Syst. Appl. 105, 233–261 (2018)
Yuan, G., Wang, Z., Meng, F., Yan, Q., Xia, S.: An overview of human activity recognition based on smartphone. Sens. Rev. 39(2), 288–306 (2019)
Cook, D.J., Krishnan, N.C.: Activity Learning: Discovering, Recognizing, and Predicting Human Behavior from Sensor Data. Wiley, London (2015)
Montero Quispe, K.G., Sousa Lima, W., Macedo Batista, D., Souto, E.: MBOSS: A symbolic representation of human activity recognition using mobile sensors. Sensors 18(12), 4354 (2018)
Jain, A., Kanhangad, V.: Human activity classification in smartphones using accelerometer and gyroscope sensors. IEEE Sens. J. 18(3), 1169–1177 (2017)
Chen, Z., Zhu, Q., Soh, Y.C., Zhang, L.: Robust human activity recognition using smartphone sensors via CT-PCA and online SVM. IEEE Trans. Ind. Inform. 13(6), 3070–3080 (2017)
Mubashir, M., Shao, L., Seed, L.: A survey on fall detection: principles and approaches. Neurocomputing 100, 144–152 (2013)
Ehatisham-ul Haq, M., Azam, M.A., Loo, J., Shuang, K., Islam, S., Naeem, U., Amin, Y.: Authentication of smartphone users based on activity recognition and mobile sensing. Sensors 17(9), 2043 (2017)
Akhavian, R., Behzadan, A.H.: Smartphone-based construction workers activity recognition and classification. Autom. Constr. 71, 198–209 (2016)
Yang, X., Tian, Y.: Super normal vector for human activity recognition with depth cameras. IEEE Trans. Pattern Anal. Mach. Intell. 39(5), 1028–1039 (2016)
Franco, A., Magnani, A., Maio, D.: A multimodal approach for human activity recognition based on skeleton and RGB data. Pattern Recogn. Lett. 131, 293–299 (2020)
Wang, A., Chen, G., Yang, J., Zhao, S., Chang, C.Y.: A comparative study on human activity recognition using inertial sensors in a smartphone. IEEE Sens. J. 16(11), 4566–4578 (2016)
Margarito, J., Helaoui, R., Bianchi, A.M., Sartor, F., Bonomi, A.G.: User-independent recognition of sports activities from a single wrist-worn accelerometer: a template-matching-based approach. IEEE Trans. Biomed. Eng. 63(4), 788–796 (2015)
Anguita, D., Ghio, A., Oneto, L., Parra, X., Reyes-Ortiz, J.L.: A public domain dataset for human activity recognition using smartphones. In: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (2013)
Gu, F., Khoshelham, K., Valaee, S., Shang, J., Zhang, R.: Locomotion activity recognition using stacked denoising autoencoders. IEEE Internet Things J. 5(3), 2085–2093 (2018)
Bragancca, H., Colonna, J.G., Lima, W.S., Souto, E.: A smartphone lightweight method for human activity recognition based on information theory. Sensors 20(7), 1856 (2020)
Figo, D., Diniz, P.C., Ferreira, D.R., Cardoso, J.M.: Preprocessing techniques for context recognition from accelerometer data. Pers. Ubiquit. Comput. 14(7), 645–662 (2010)
Ignatov, A.: Real-time human activity recognition from accelerometer data using convolutional neural networks. Appl. Soft Comput. 62, 915–922 (2018)
Ozcan, T., Basturk, A.: Human action recognition with deep learning and structural optimization using a hybrid heuristic algorithm. Cluster Computing, pp. 1–14 (2020)
Chen, K., Yao, L., Zhang, D., Wang, X., Chang, X., Nie, F.: A semisupervised recurrent convolutional attention model for human activity recognition. IEEE Trans. Neural Netw. Learn. Syst. 31(5), 1747–1756 (2019)
Cruciani, F., Vafeiadis, A., Nugent, C., Cleland, I., McCullagh, P., Votis, K., Giakoumis, D., Tzovaras, D., Chen, L., Hamzaoui, R.: Feature learning for human activity recognition using convolutional neural networks. CCF Trans. Pervas. Comput. Interact. 2(1), 18–32 (2020)
Ronao, C.A., Cho, S.B.: Human activity recognition with smartphone sensors using deep learning neural networks. Expert Syst. Appl. 59, 235–244 (2016)
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Zhang, Y., Jin, R., Zhou, Z.H.: Understanding bag-of-words model: a statistical framework. Int. J. Mach. Learn. Cybern. 1(1–4), 43–52 (2010)
Cao, L., Wang, Y., Zhang, B., Jin, Q., Vasilakos, A.V.: Gchar: An efficient group-based context-aware human activity recognition on smartphone. J. Parallel Distrib. Comput. 118, 67–80 (2018)
Acharjee, D., Mukherjee, A., Mandal, J., Mukherjee, N.: Activity recognition system using inbuilt sensors of smart mobile phone and minimizing feature vectors. Microsyst. Technol. 22(11), 2715–2722 (2016)
Ahmed, N., Rafiq, J.I., Islam, M.R.: Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model. Sensors 20(1), 317 (2020)
Ahmed Bhuiyan, R., Ahmed, N., Amiruzzaman, M., Islam, M.R.: A robust feature extraction model for human activity characterization using 3-axis accelerometer and gyroscope data. Sensors 20(23), 6990 (2020)
Tufek, N., Yalcin, M., Altintas, M., Kalaoglu, F., Li, Y., Bahadir, S.K.: Human action recognition using deep learning methods on limited sensory data. IEEE Sens. J. 20(6), 3101–3112 (2019)
Nematallah, H., Rajan, S.C., Cret, A.: Logistic model tree for human activity recognition using smartphone-based inertial sensors. Sensors (2019)
Irvine, N., Nugent, C., Zhang, S., Wang, H., Ng, W.W.: Neural network ensembles for sensor-based human activity recognition within smart environments. Sensors 20(1), 216 (2020)
Wang, Z., Jiang, M., Hu, Y., Li, H.: An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors. IEEE Trans. Inf Technol. Biomed. 16(4), 691–699 (2012)
Teng, Q., Wang, K., Zhang, L., He, J.: The layer-wise training convolutional neural networks using local loss for sensor based human activity recognition. IEEE Sensors J. (2020)
Xiao, F., Pei, L., Chu, L., Zou, D., Yu, W., Zhu, Y., Li, T.: A deep learning method for complex human activity recognition using virtual wearable sensors. arXiv preprint arXiv:2003.01874 (2020)
Qin, Z., Zhang, Y., Meng, S., Qin, Z., Choo, K.K.R.: Imaging and fusing time series for wearable sensor-based human activity recognition. Inform. Fusion 53, 80–87 (2020)
Kwon, Y., Kang, K., Bae, C.: Unsupervised learning for human activity recognition using smartphone sensors. Expert Syst. Appl. 41(14), 6067–6074 (2014)
Wong, T.T., Yeh, P.Y.: Reliable accuracy estimates from k-fold cross validation. IEEE Trans. Knowl. Data Eng. (2019)
Peng, L., Chen, L., Wu, X., Guo, H., Chen, G.: Hierarchical complex activity representation and recognition using topic model and classifier level fusion. IEEE Trans. Biomed. Eng. 64(6), 1369–1379 (2016)
Xia, K., Huang, J., Wang, H.: LSTM-CNN architecture for human activity recognition. IEEE Access 8, 56855–56866 (2020)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bhuiyan, R.A., Tarek, S. & Tian, H. Enhanced bag-of-words representation for human activity recognition using mobile sensor data. SIViP 15, 1739–1746 (2021). https://doi.org/10.1007/s11760-021-01907-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-021-01907-4