Comparison of Algorithm Selection to Analyze Elderly Activity Recognition Based on Sensor Data Using R Program

This paper presents a comparison of algorithms used to classify human activity of elderly by sensor data from UCI Machine Learning Repository. We compare three popular algorithms used to classify activities as Artificial Neural Networks, Support Vector Machine and C4.5 Decision Tree to find the most efficient algorithm for classifying human activities. This research used data set “Activity recognition with healthy older people using a batteryless wearable sensor Data Set” of 6000 records. The result is that Artificial Neural Networks has the highest classification accuracy of 96.5%, followed by the Support Vector Machine 96.41% and the C4.5 Decision Tree 95.75%. These experimental data can be applied to the system of detecting or monitoring the activities of the elderly.

[1]  V. Rodriguez-Galiano,et al.  Machine learning predictive models for mineral prospectivity: an evaluation of neural networks, random forest, regression trees and support vector machines , 2015 .

[2]  Andrey Ignatov,et al.  Real-time human activity recognition from accelerometer data using Convolutional Neural Networks , 2018, Appl. Soft Comput..

[3]  Davide Anguita,et al.  Transition-Aware Human Activity Recognition Using Smartphones , 2016, Neurocomputing.

[4]  Kemal Polat,et al.  A novel hybrid intelligent method based on C4.5 decision tree classifier and one-against-all approach for multi-class classification problems , 2009, Expert Syst. Appl..

[5]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[6]  Amutha Prabakar Muniyandi,et al.  Network Anomaly Detection by Cascading K-Means Clustering and C4.5 Decision Tree algorithm , 2012 .

[7]  Sung-Bae Cho,et al.  Human activity recognition with smartphone sensors using deep learning neural networks , 2016, Expert Syst. Appl..

[8]  Thomas M. Cover,et al.  Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..

[9]  Faicel Chamroukhi,et al.  Physical Human Activity Recognition Using Wearable Sensors , 2015, Sensors.

[10]  Jian Liu,et al.  Classification of Daily Activities for the Elderly Using Wearable Sensors , 2017, Journal of healthcare engineering.

[11]  Qinfeng Shi,et al.  Sensor enabled wearable RFID technology for mitigating the risk of falls near beds , 2013, 2013 IEEE International Conference on RFID (RFID).

[12]  Robert Gentleman,et al.  R Programming for Bioinformatics , 2008 .

[13]  James Cannady,et al.  Artificial Neural Networks for Misuse Detection , 1998 .

[14]  Abbas Bahroudi,et al.  Support vector machine for multi-classification of mineral prospectivity areas , 2012, Comput. Geosci..

[15]  Wei Dai,et al.  A MapReduce Implementation of C4.5 Decision Tree Algorithm , 2014 .