Empirical Mode Decomposition Based Multi-Modal Activity Recognition

Hu, Lingyue; Zhao, Kailong; Zhou, Xueling; Ling, Bingo Wing‐Kuen; Liao, Guozhao

doi:10.3390/s20216055

Cited by 3 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The random forest is a bagging and a decision tree-based machine learning method [ 26 ]. Assume that there are N samples in the training set.…”

Section: Our Proposed Methodsmentioning

confidence: 99%

Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms

Ling

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed via a classification approach. First, the single-channel electroencephalograms (EEGs) are acquired from various participants when they perform various activities. Then, fast Fourier transform (FFT) is applied to the acquired EEGs, and the high-frequency components are discarded for performing denoising. Next, empirical mode decomposition (EMD) is applied to remove the underlying trend of the signals. In order to extract more features, singular spectrum analysis (SSA) is employed to increase the total number of the components. Finally, some typical models such as the random forest-based classifier, the support vector machine (SVM)-based classifier, and the back-propagation (BP) neural network-based classifier are used for performing the classifications. Here, the percentages of the classification accuracies are employed as the attention scores. The computer numerical simulation results show that our proposed method yields a higher classification performance compared to the traditional methods without performing the EMD and SSA.

show abstract

“…The random forest is a bagging and a decision tree-based machine learning method [ 26 ]. Assume that there are N samples in the training set.…”

Section: Our Proposed Methodsmentioning

confidence: 99%

Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms

Ling

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…EMD has been widely used in production practice. Hu et al [20] decomposed the children's EEG into various brain wave components by EMD, and 11 different physical quantities are extracted as features in the intrinsic mode function (IMF). Finally, the random forest is used for activity recognition.…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Diagnosis Method of Electro‐Hydrostatic Actuator

Lei

Lvjun

et al. 2021

Shock and Vibration

View full text Add to dashboard Cite

Electrohydrostatic actuator is a type of actuator that uses hydraulic energy as the energy transmission carrier, which has the advantages of small size and high power. Since it is commonly used in harsh conditions such as strong vibration, high pressure, and heavy loads, condition monitoring and fault diagnosis of its hydraulic system are particularly important. This paper proposed a novel fault feature extraction method and applied to fault diagnosis of electrohydrostatic actuator. Firstly, the pressure signal of the hydraulic system is decomposed at multiple scales to obtain the center frequency of its maximum energy intrinsic mode component, and the feature data set is constructed based on the statistical features of the time domain. Then, a fault identification model of hydraulic system based on support vector machine is established. Finally, the fault classification and identification results of the hydraulic system are outputted. After a variety of method comparisons, the method proposed in this paper has a fault time ratio accuracy of 96.7%, which provides a basis and a new way for the fault diagnosis of the hydraulic system.

show abstract