Abstract
There has been an increased interest in the development of models to identify and predict human activities. However, the sparsity of the data gathered from the sensory devices in an ambient living environment creates the challenge of representing activities accurately. Also, such data usually comprise arbitrary lengths of dimensions. Recurrent Neural Networks (RNNs) are one of the widely used algorithms in sequential modelling due to their ability to handle the arbitrary lengths of data. In an attempt to address the above challenges, this paper proposes a method of fuzzy feature representation with Bidirectional Long Short-Term Memory (Bi-LSTM) for human activities modelling and recognition. To obtain optimal feature representation, sensor data are fuzzified and the membership degrees represent the selected features which are then applied to the Bi-LSTM model for activity modelling and recognition. The learning capability of the Bi-LSTM allows the model to learn the temporal relationship in sequential data which is used to identify human activities pattern. The learned pattern is then utilised in the prediction of further activities. The proposed method is tested and evaluated using dataset representing Activity of Daily Living (ADL) for a single user in a smart home environment. The obtained results are also compared with existing approaches that are used for modelling and recognising human activities.
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
References
Adama, D.A., Lotfi, A., Langensiepen, C.: Key frame extraction and classification of human activities using motion energy. In: Advances in Computational Intelligence Systems, pp. 303–311. Springer (2019)
Adama, D.A., Lotfi, A., Langensiepen, C., Lee, K., Trindade, P.: Human activity learning for assistive robotics using a classifier ensemble. Soft Comput. 22(21), 7027–7039 (2018)
Benmansour, A., Bouchachia, A., Feham, M.: Modeling interaction in multi-resident activities. Neurocomputing 230, 133–142 (2017)
Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob. Comput. 5(4), 277–298 (2009)
Deng, Y., Ren, Z., Kong, Y., Bao, F., Dai, Q.: A hierarchical fused fuzzy deep neural network for data classification. IEEE Trans. Fuzzy Syst. 25(4), 1006–1012 (2017)
Gochoo, M., Tan, T.-H., Liu, S.-H., Jean, F.-R., Alnajjar, F.S., Huang, S.-C.: Unobtrusive activity recognition of elderly people living alone using anonymous binary sensors and dcnn. IEEE J. Biomed. Health Inform. 23(2), 693–702 (2019)
Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5), 602–610 (2005)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Ke, S.-R., Thuc, H., Lee, Y.-J., Hwang, J.-N., Yoo, J.-H., Choi, K.-H.: A review on video-based human activity recognition. Computers 2(2), 88–131 (2013)
Medina-Quero, J., Zhang, S., Nugent, C., Espinilla, M.: Ensemble classifier of long short-term memory with fuzzy temporal windows on binary sensors for activity recognition. Expert. Syst. Appl. 114, 441–453 (2018)
Mohmed, G., Lotfi, A., Pourabdollah, A.: Long, short-term memory fuzzy finite state machine for human activity modelling. In: The 12th PErvasive Technologies Related to Assistive Environments Conference (PETRA). ACM, New York (2019)
Mohmed, G., Lotfi, A., Pourabdollah, A.: Human activities recognition based on neuro-fuzzy finite state machine. Technologies 6(4), 110 (2018)
Rashidi, P., Mihailidis, A.: A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 17(3), 579–590 (2013)
Steven Eyobu, O., Han, D.: Feature representation and data augmentation for human activity classification based on wearable imu sensor data using a deep LSTM neural network. Sensors 18, 9 (2018)
Tan, T.-H., Gochoo, M., Jean, F.-R., Huang, S.-C., Kuo, S.-Y.: Front-door event classification algorithm for elderly people living alone in smart house using wireless binary sensors. IEEE Access 5, 10734–10743 (2017)
Yulita, I.N., Fanany, M.I., Arymurthy, A.M.: Fuzzy clustering and bidirectional long short-term memory for sleep stages classification. In: International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT), pp. 11–16. IEEE (2017)
Yulita, I.N., Fanany, M.I., Arymuthy, A.M.: Bi-directional long short-term memory using quantized data of deep belief networks for sleep stage classification. Procedia Comput. Sci. 116, 530–538 (2017)
Zhao, Y., Yang, R., Chevalier, G., Xu, X., Zhang, Z.: Deep residual bidir-LSTM for human activity recognition using wearable sensors. Math. Probl. Eng. 2018, 7316954, 13 (2018)
Zhu, J., San-segundo, R., Pardo, J.M.: Feature extraction for robust physical activity recognition. Hum. Centric Comput. Inf. Sci. 7(1), 1–16 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Mohmed, G., Adama, D.A., Lotfi, A. (2020). Fuzzy Feature Representation with Bidirectional Long Short-Term Memory for Human Activity Modelling and Recognition. In: Ju, Z., Yang, L., Yang, C., Gegov, A., Zhou, D. (eds) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing, vol 1043. Springer, Cham. https://doi.org/10.1007/978-3-030-29933-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-030-29933-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29932-3
Online ISBN: 978-3-030-29933-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)