Applying Data Mining Techniques on Continuous Sensed Data for Daily Living Activity Recognition

In this paper, several data mining techniques were discussed and analyzed in order to achieve the objective of human daily activities recognition based on a continuous sensing data set. The data mining techniques of decision tree, Naïve Bayes and Neural Network were successfully applied to the data set. The paper also proposed an idea of combining the Neural Network with the Decision Tree, the result shows that it works much better than the typical Neural Network and the typical Decision Tree model.

[1]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[2]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[3]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[4]  Ig-Jae Kim,et al.  Activity Recognition Using Wearable Sensors for Elder Care , 2008, 2008 Second International Conference on Future Generation Communication and Networking.

[5]  Ke Shi,et al.  Data Mining Techniques for Wireless Sensor Networks: A Survey , 2013, Int. J. Distributed Sens. Networks.

[6]  Rong Liu,et al.  Recognizing Human Activities Based on Multi-Sensors Fusion , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[7]  Wan-Young Chung,et al.  High Accuracy Human Activity Monitoring Using Neural Network , 2008, 2008 Third International Conference on Convergence and Hybrid Information Technology.

[8]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[9]  Lian-Wen Jin,et al.  Activity recognition from acceleration data using AR model representation and SVM , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[10]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[11]  Gamze Uslu,et al.  A Bayesian approach for indoor human activity monitoring , 2011, 2011 11th International Conference on Hybrid Intelligent Systems (HIS).

[12]  Lasitha Piyathilaka,et al.  Gaussian mixture based HMM for human daily activity recognition using 3D skeleton features , 2013, 2013 IEEE 8th Conference on Industrial Electronics and Applications (ICIEA).

[13]  Paul Lukowicz,et al.  Collecting complex activity datasets in highly rich networked sensor environments , 2010, 2010 Seventh International Conference on Networked Sensing Systems (INSS).

[14]  Xi Long,et al.  Single-accelerometer-based daily physical activity classification , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Tae-Seong Kim,et al.  Real-Time Recognition of Daily Human Activities Using a Single Tri-Axial Accelerometer , 2010, 2010 5th International Conference on Embedded and Multimedia Computing.

[16]  M. Shaw,et al.  Induction of fuzzy decision trees , 1995 .

[17]  Paul Lukowicz,et al.  Recording a Complex, Multi Modal Activity Data Set for Context Recognition , 2011, ARCS Workshops.

[18]  Hongnian Yu,et al.  Activity classification using a single wrist-worn accelerometer , 2011, 2011 5th International Conference on Software, Knowledge Information, Industrial Management and Applications (SKIMA) Proceedings.

[19]  Jong-Hwan Kim,et al.  Classification of long-term motions using a two-layered hidden Markov model in a wearable sensor system , 2011, 2011 IEEE International Conference on Robotics and Biomimetics.