A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones

Xu, Shoujiang; Tang, Qingfeng; Jin, Lin‐Pei; Pan, Zhigeng

doi:10.3390/s19102307

Cited by 34 publications

(22 citation statements)

References 38 publications

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“…Random forest is based on decision trees and uses the idea of ensemble learning to classify data. Deep forest adopts cascade structure which combines the characteristics of the neural network to further improve the recognition of random forest, and the cascade layer can automatically adjust the optimal number of classification layers (Xu et al, 2019 ). Deep forest automatically optimizes the structure of deep forest by comparing the classification performance of adjacent layers.…”

Section: Methodsmentioning

confidence: 99%

Multi-Feature Input Deep Forest for EEG-Based Emotion Recognition

et al. 2021

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Due to the rapid development of human–computer interaction, affective computing has attracted more and more attention in recent years. In emotion recognition, Electroencephalogram (EEG) signals are easier to be recorded than other physiological experiments and are not easily camouflaged. Because of the high dimensional nature of EEG data and the diversity of human emotions, it is difficult to extract effective EEG features and recognize the emotion patterns. This paper proposes a multi-feature deep forest (MFDF) model to identify human emotions. The EEG signals are firstly divided into several EEG frequency bands and then extract the power spectral density (PSD) and differential entropy (DE) from each frequency band and the original signal as features. A five-class emotion model is used to mark five emotions, including neutral, angry, sad, happy, and pleasant. With either original features or dimension reduced features as input, the deep forest is constructed to classify the five emotions. These experiments are conducted on a public dataset for emotion analysis using physiological signals (DEAP). The experimental results are compared with traditional classifiers, including K Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM). The MFDF achieves the average recognition accuracy of 71.05%, which is 3.40%, 8.54%, and 19.53% higher than RF, KNN, and SVM, respectively. Besides, the accuracies with the input of features after dimension reduction and raw EEG signal are only 51.30 and 26.71%, respectively. The result of this study shows that the method can effectively contribute to EEG-based emotion classification tasks.

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Section: Methodsmentioning

confidence: 99%

Multi-Feature Input Deep Forest for EEG-Based Emotion Recognition

et al. 2021

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“…To further evaluate the performance of this study, we compared it with some previous studies in HAR, including EEMD+FS+SVM [12], ACELM [17], CELearning [41], tFFT+Convnet [62] and KPCA+DBN [63]. These studies, conducted in recent years, include deep learning, ensemble learning and feature selection for HAR.…”

Section: Resultsmentioning

confidence: 99%

“…Although there are many HAR studies based on ensemble learning technology [39,40,41,42,43,44], to our best knowledge, there is still no work attempting to improve the performance of HAR through a selective ensemble approach. Most of the ensemble learning-based HAR studies [17,30,39] combined all the trained base classifiers for recognition.…”

Section: Introductionmentioning

confidence: 99%

Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition

Tian

Jie

Geng

et al. 2019

Sensors

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Sensor-based human activity recognition (HAR) has attracted interest both in academic and applied fields, and can be utilized in health-related areas, fitness, sports training, etc. With a view to improving the performance of sensor-based HAR and optimizing the generalizability and diversity of the base classifier of the ensemble system, a novel HAR approach (pairwise diversity measure and glowworm swarm optimization-based selective ensemble learning, DMGSOSEN) that utilizes ensemble learning with differentiated extreme learning machines (ELMs) is proposed in this paper. Firstly, the bootstrap sampling method is utilized to independently train multiple base ELMs which make up the initial base classifier pool. Secondly, the initial pool is pre-pruned by calculating the pairwise diversity measure of each base ELM, which can eliminate similar base ELMs and enhance the performance of HAR system by balancing diversity and accuracy. Then, glowworm swarm optimization (GSO) is utilized to search for the optimal sub-ensemble from the base ELMs after pre-pruning. Finally, majority voting is utilized to combine the results of the selected base ELMs. For the evaluation of our proposed method, we collected a dataset from different locations on the body, including chest, waist, left wrist, left ankle and right arm. The experimental results show that, compared with traditional ensemble algorithms such as Bagging, Adaboost, and other state-of-the-art pruning algorithms, the proposed approach is able to achieve better performance (96.7% accuracy and F1 from wrist) with fewer base classifiers.

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“…Studies on activity recognition using ensemble learning have been conducted. Xu et al constructed an ensemble learning model consisting of XGBoost, Random Forest, ExtraTrees, and soft-max Regression [4].…”

Section: Methodsmentioning

confidence: 99%

Ensemble learning for human activity recognition

Sekiguchi

Abe

Yokoyama

et al. 2020

Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of The

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This paper describes a activity recognition method for Sussex-Huawei Locomotion (SHL) Challenge 2020 by team TDU_BSA. The use of ensemble learning, which combines the outputs of multiple classifiers to produce a single estimation result, improved the accuracy of activity recognition. The ensemble model consists of CNN models and a gradient-boosting model. The objective of SHL Challenge 2020 is that the users of SHL test-set are two different from SHL training-set, and the phone location of SHL test-set is not known to the SHL's participants. Therefore, estimating phone location and the user improved accuracy. SHL test-set's phone location was estimated to be Hips. The user can be estimated from SHL validation-set. The ensemble model was made with all SHL training-set (Only Hips) and 70% of SHL validation-set (Only Hips). In the submission phase, the best F-measure obtained for last 30% SHL validation-set was 84.8%. CCS CONCEPTS • Computing methodologies → Activity recognition and understanding.

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A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones

Cited by 34 publications

References 38 publications

Multi-Feature Input Deep Forest for EEG-Based Emotion Recognition

Multi-Feature Input Deep Forest for EEG-Based Emotion Recognition

Selective Ensemble Based on Extreme Learning Machine for Sensor-Based Human Activity Recognition

Ensemble learning for human activity recognition

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