Citation

Ooi, Shih Yin and Tan, Shing Chiang and Cheah, Wooi Ping (2016) Classifying Human Activities with Temporal Extension of Random Forest. In: Neural Information Processing. Springer Berlin Heidelberg, pp. 3-10.

Text 135.pdf Restricted to Repository staff only Download (802kB)

Abstract

Sensor-Based Human Activity Recognition (HAR) is a study of recognizing the human’s activities by using the data captured from wearable sensors. Avail the temporal information from the sensors, a modified version of random forest is proposed to preserve the temporal information, and harness them in classifying the human activities. The proposed algorithm is tested on 7 public HAR datasets. Promising results are reported, with an average classification accuracy of ~98 %.

Item Type:	Book Section
Uncontrolled Keywords:	Human activity, Classification, Random forest, Temporal sequences, Machine learning
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	02 Aug 2018 09:39
Last Modified:	02 Aug 2018 09:39
URII:	http://shdl.mmu.edu.my/id/eprint/6720

Downloads

Edit (login required)