Abstract
In this paper, a clustering-based fuzzy finite state machine approach for human activity modelling and recognition is proposed. It Incorporates the Fuzzy C-means (FCMs) clustering algorithm with a Fuzzy Finite State Machine (FuFSM) in order to generate the state transitions more effectively. This unsupervised approach will overcome the deficiency in identifying the knowledge-base required for FuFSM. To validate the proposed approach, experimental results are presented. The activities of two office workers are modelled/recognised using the proposed method. The approach taken for this research is based on ambient Intelligent sensory data rather than data coming from wearable sensors.
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
Aicha, A.N., Englebienne, G., Kröse, B.: Unsupervised visit detection in smart homes. Pervasive Mob. Comput. 34, 157–167 (2017)
Alvarez-Alvarez, A., Trivino, G., Cordón, O.: Body posture recognition by means of a genetic fuzzy finite state machine. In: 2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS), pp. 60–65. IEEE (2011)
Alvarez-Alvarez, A., Trivino, G., Cordon, O.: Human gait modeling using a genetic fuzzy finite state machine. IEEE Trans. Fuzzy Syst. 20(2), 205–223 (2012)
Atzmueller, M., Hayat, N., Trojahn, M., Kroll, D.: Explicative human activity recognition using adaptive association rule-based classification. In: 2018 IEEE International Conference onFuture IoT Technologies (Future IoT), pp. 1–6. IEEE (2018)
Barsocchi, P., Cimino, M.G., Ferro, E., Lazzeri, A., Palumbo, F., Vaglini, G.: Monitoring elderly behavior via indoor position-based stigmergy. Pervasive Mob Comput 23, 26–42 (2015)
Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: technologies, applications, and opportunities. Pervasive Mob. Comput. 5(4), 277–298 (2009)
Dawadi, P., Cook, D., Parsey, C., Schmitter-Edgecombe, M., Schneider, M.: An approach to cognitive assessment in smart home. In: Proceedings of the 2011 Workshop on Data Mining for Medicine and Healthcare, pp. 56–59. ACM (2011)
Garcia-Ceja, E., Brena, R.F.: An improved three-stage classifier for activity recognition. Int. J. Pattern Recogn. Artif. Intell. 32(01), 1860003 (2018)
He, H., Tan, Y., Zhang, W.: A wavelet tensor fuzzy clustering scheme for multi-sensor human activity recognition. Eng. Appl. Artif. Intell. 70, 109–122 (2018)
Langensiepen, C., Lotfi, A., Puteh, S.: Activities recognition and worker profiling in the intelligent office environment using a fuzzy finite state machine. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 873–880. IEEE (2014)
Lara, O.D., Labrador, M.A.: A survey on human activity recognition using wearable sensors. IEEE Commun. Surv. Tutor. 15(3), 1192–1209 (2013)
Lotfi, A., Howarth, M.: Industrial application of fuzzy systems: adaptive fuzzy control of solder paste stencil printing. Inf. Sci. 107(1–4), 273–285 (1998)
Lu-An, T., Jiawei, H., Guofei, J.: Mining sensor data in cyberphysical systems. Tsinghua Sci. Technol. 19(1), 225–234 (2015)
Mohmed, G., Lotfi, A., Langensiepen, C., Pourabdollah, A.: Unsupervised learning fuzzy finite state machine for human activities recognition. In: The Pervasive Technologies Related to Assistive Environments (PETRA) Conference Proceedings (2018)
Palumbo, F., Ullberg, J., Štimec, A., Furfari, F., Karlsson, L., Coradeschi, S.: Sensor network infrastructure for a home care monitoring system. Sensors 14(3), 3833–3860 (2014)
Panwar, M., Dyuthi, S.R., Prakash, K.C., Biswas, D., Acharyya, A., Maharatna, K., Gautam, A., Naik, G.R.: CNN based approach for activity recognition using a wrist-worn accelerometer. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2438–2441. IEEE (2017)
Rashidi, P., Mihailidis, A.: A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 17(3), 579–590 (2013)
Suryadevara, N.K., Mukhopadhyay, S.C., Wang, R., Rayudu, R.: Forecasting the behavior of an elderly using wireless sensors data in a smart home. Eng. Appl. Artif. Intell. 26(10), 2641–2652 (2013)
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)
Yang, J., Nguyen, M.N., San, P.P., Li, X., Krishnaswamy, S.: Deep convolutional neural networks on multichannel time series for human activity recognition. In: IJCAI, pp. 3995–4001 (2015)
Yin, J., Yang, Q., Pan, J.J.: Sensor-based abnormal human-activity detection. IEEE Trans. Knowl. Data Eng. 20(8), 1082–1090 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Mohmed, G., Lotfi, A., Langensiepen, C., Pourabdollah, A. (2019). Clustering-Based Fuzzy Finite State Machine for Human Activity Recognition. In: Lotfi, A., Bouchachia, H., Gegov, A., Langensiepen, C., McGinnity, M. (eds) Advances in Computational Intelligence Systems. UKCI 2018. Advances in Intelligent Systems and Computing, vol 840. Springer, Cham. https://doi.org/10.1007/978-3-319-97982-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-97982-3_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97981-6
Online ISBN: 978-3-319-97982-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)