Classifying Human Activities with Temporal Extension of Random Forest


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.

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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


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