Abstract
The daily activities recognition is one of the most important areas that attract the attention of researchers. Automatic classification of activities of daily living (ADL) can be used to promote healthier lifestyle, though it can be challenging when it comes to intellectual disability personals, the elderly, or children. Thus developing a technique to recognize activities with high quality is critical for such applications. In this work, seven algorithms are developed and evaluated for classification of everyday activities like climbing the stairs, drinking water, getting up from bed, pouring water, sitting down on a chair, standing up from a chair, and walking. Algorithms of concern are K-nearest Neighbor, Artificial Neural Network, and Naïve Bayes, Dynamic Time Warping, $1 recognizer, Support Vector Machine, and a novel classifier (D$1). We explore different algorithm activities with regard to recognizing everyday activities. We also present a technique based on $1 and DTW to enhance the recognition accuracy of ADL. Our result show that we can achieve up to 83 % accuracy for seven different 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
Parkk, J., Ermes, M., Korpipaa, P., Mantyjarvi, J., Peltola, J., Korhonen, I.: Activity classification using realistic data from wearable sensors. IEEE Trans. Inf. Technol. Biomed. 10(1), 119–128 (2006)
Suryadevara, N.K., Mukhopadhyay, S.C.: ADLs recognition of an elderly person and wellness determination, Smart Sensors, Measurement and Instrumentation, vol. 14, pp. 111–137. Springer (2015)
Yang, J.Y., Wang, J.S., Chen, Y.P.: Using acceleration measurements for activity recognition: an effective learning algorithm for constructing neural classifiers. Pattern Recognit. Lett. 29(16), 2213–2220 (2008) (Elsevier)
Atallah, L., Lo, B., King, R., Yang, G.Z.: Sensor positioning for activity recognition using wearable accelerometers. IEEE Trans. Biomed. Circuits Syst. 5(4), 320-329 (2011)
Min, C.H., Tewfik, A.H.: Automatic characterization and detection of behavioral patterns using linear predictive coding of accelerometer sensor data. In: The Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 220–223 (2010)
Zouba, N., Brémond, F., Thonnat, M., Anfosso, A., Pascual, E., Mallea, P., Guerin, O.: A computer system to monitor older adults at home: preliminary results. Gerontechnology J. 8(3), 129–139 (2009)
Vaswani, N., Roy-Chowdhury, A.K., Chellappa, R.: “Shape Activity”: a continuous-state HMM for moving/deforming shapes with application to abnormal activity detection. IEEE Trans. Image Process. 14(10), 1603–1616 (2005)
Pradeep, T.R., Karthikamani, R.: Abnormal behavior detection in intelligent transport system for intelligent driving. Int. J. Softw. Hardw. Res. Eng. (IJSHRE). 2(5) (2014)
Bhavsar, H., Ganatra, A.: A comparative study of training algorithms for supervised machine learning. Int. J. Soft Comput. Eng. (IJSCE). 2(4), 2231–2307 (2012)
Avilés-Arriaga, H., Sucar-Succar, L., Mendoza-Durán, C., Pineda-Cortés, L.: A comparison of dynamic naive bayesian classifiers and hidden markov models for gesture recognition. J. Appl. Res. Technol. 9(1), 81–102 (2011)
Trigueiros, P., Ribeiro, F., Reis, L.P.: A comparison of machine learning algorithms applied to hand gesture recognition. In: Proceedings of the 7th Iberian IEEE Conference on Information 273 Systems and Technologies (CISTI), pp. 1–6 (2012)
Mannini, A., Sabatini, A.: Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10(2), 1154–75 (2010)
Moiz, F., Natoo, P., Derakhshani, R., Leon-Salas, W.D.: A comparative study of classification methods for gesture recognition using a 3-axis accelerometer. In: The 2011 International Joint Conference on IEEE Neural Networks (IJCNN), pp. 2479–2486 (2011)
Ayu, M.A., Ismail, S.A., Abdul Marin, A.F., Mantoro, T.: A comparison study of classifier algorithms for mobile-phone’s accelerometer based activity recognition. Elsevier-International Symposium on Robotics and Intelligent Sensors, vol. 41, pp. 224–229 (2012)
Gao, L., Bourke, A.K., Nelson, J.: A comparison of classifiers for activity recognition using multiple accelerometer-based sensors. In: 2012 IEEE 11th International Conference on Cybernetic Intelligent Systems (CIS), pp. 149–153 (2012)
Wobbrock, J.O., Wilson, A.D., Li, Y.: Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes. In: Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology, pp. 159–168 (2007)
Bruno, B., Mastrogiovanni, F., Sgorbissa, A., Vernazza, T.: Analysis of human behavior recognition algorithms based on acceleration data. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 1602–1607 (2013)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Noaman, H.M., Elmougy, S., Ghoneim, A., Hamza, T.: Naive Bayes classifier based Arabic document categorization. In: The Proceedings of the 7th International Conference on Informatics and Systems (INFOS), pp. 1–5 (2010)
Förster, K., Monteleone, S., Calatroni, A., Roggen, D., Tröster, G.: Incremental KNN classifier exploiting correct-error teacher for activity recognition. In: 9th International Conference on IEEE-Machine Learning and Applications (ICMLA), pp. 445–450 (2010)
Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(1), 131–156 (1997)
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29, 131–163 (1997)
Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Discov. 2(2), 121–167 (1998)
Jeong, Y.S., Jeong, M.K., Omitaomu, O.A.: Weighted dynamic time warping for time series classification. Pattern Recognit. 44(9), 2231–2240 (2011)
ADL recognition with wrist-worm accelerometer data set. https://archiveics.uci.edu/ml/datasets/dataset+for+ADL+Recognition+with+Wrist-worm+Accelerometer
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Slim, S.O., Atia, A., Mostafa, MS.M. (2016). An Experimental Comparison Between Seven Classification Algorithms for Activity Recognition. In: Gaber, T., Hassanien, A., El-Bendary, N., Dey, N. (eds) The 1st International Conference on Advanced Intelligent System and Informatics (AISI2015), November 28-30, 2015, Beni Suef, Egypt. Advances in Intelligent Systems and Computing, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-319-26690-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-26690-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26688-6
Online ISBN: 978-3-319-26690-9
eBook Packages: Computer ScienceComputer Science (R0)