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A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones

1
Virtual Reality and Intelligent Systems Research Institute, Hangzhou Normal University, Hangzhou 311121, China
2
School of Information Engineering, Jiangsu Food and Pharmaceutical Science College, Huaian 223001, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(10), 2307; https://doi.org/10.3390/s19102307
Received: 21 April 2019 / Revised: 9 May 2019 / Accepted: 16 May 2019 / Published: 19 May 2019
(This article belongs to the Special Issue Intelligent Sensor Signal in Machine Learning)
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Abstract

Human activity recognition (HAR) has gained lots of attention in recent years due to its high demand in different domains. In this paper, a novel HAR system based on a cascade ensemble learning (CELearning) model is proposed. Each layer of the proposed model is comprised of Extremely Gradient Boosting Trees (XGBoost), Random Forest, Extremely Randomized Trees (ExtraTrees) and Softmax Regression, and the model goes deeper layer by layer. The initial input vectors sampled from smartphone accelerometer and gyroscope sensor are trained separately by four different classifiers in the first layer, and the probability vectors representing different classes to which each sample belongs are obtained. Both the initial input data and the probability vectors are concatenated together and considered as input to the next layer’s classifiers, and eventually the final prediction is obtained according to the classifiers of the last layer. This system achieved satisfying classification accuracy on two public datasets of HAR based on smartphone accelerometer and gyroscope sensor. The experimental results show that the proposed approach has gained better classification accuracy for HAR compared to existing state-of-the-art methods, and the training process of the model is simple and efficient. View Full-Text
Keywords: human activity recognition; cascade ensemble learning model; smartphone; sensor; extremely gradient boosting trees; Random Forest; extremely randomized trees; Softmax Regression human activity recognition; cascade ensemble learning model; smartphone; sensor; extremely gradient boosting trees; Random Forest; extremely randomized trees; Softmax Regression
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Xu, S.; Tang, Q.; Jin, L.; Pan, Z. A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones. Sensors 2019, 19, 2307.

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