Time Frequency Domain Features Research Articles

Combining discriminative spatiotemporal features for daily life activity recognition using wearable motion sensing suit

Motion sensing plays an important role in the study of human movements, motivated by a wide range of applications in different fields, such as sports, health care, daily activity, action recognition for surveillance, assisted living and the entertainment industry. In this paper, we describe how to classify a set of human movements comprising daily activities using a wearable motion capture suit, denoted as FatoXtract. A probabilistic integration of different classifiers recently proposed is employed herein, considering several spatiotemporal features, in order to classify daily activities. The classification model relies on the computed confidence belief from base classifiers, combining multiple likelihoods from three different classifiers, namely Naive Bayes, artificial neural networks and support vector machines, into a single form, by assigning weights from an uncertainty measure to counterbalance the posterior probability. In order to attain an improved performance on the overall classification accuracy, multiple features in time domain (e.g., velocity) and frequency domain (e.g., fast Fourier transform), combined with geometrical features (joint rotations), were considered. A dataset from five daily activities performed by six participants was acquired using FatoXtract. The dataset provided in this work was designed to be extremely challenging since there are high intra-class variations, the duration of the action clips varies dramatically, and some of the actions are quite similar (e.g., brushing teeth and waving, or walking and step). Reported results, in terms of both precision and recall, remained around 85 %, showing that the proposed framework is able to successfully classify different human activities.

PERFORMANCE ANALYSIS OF MLP AND SVM BASED CLASSIFIERS FOR HUMAN ACTIVITY RECOGNITION USING SMARTPHONE SENSORS DATA

Days have gone when Mobile Phone used to be matter luxury, it has become a significant need for rapidly evolving fast track world. Intelligent aspects of the computing device enhance the importance of the interface development since; efficient collaboration always relies on the good communication between man and machine. The information about mobile device and user activity, environment, other devices, location and time can be utilized in different situations to enhance the interaction between the user and the device. Here, we presented our work in which certain types of human physical activities using accelerometer and gyroscope data generated by a mobile device. The benchmark Human Activity Recognition dataset is considered for this work is acquired from UCI Machine Learning Repository which is available in public domain. MLP and SVM Classifiers were tested using various time domain and frequency domain features. We found the using Multi Layer Perceptron with Processing Element 6, Learning rate 0.05, Momentum 0.1 reach an overall accuracy of 98.11%. Keywords— Context; Framework; Adaptive User Interfaces; Classifier, Multilayer Perceptron, Support Vector

A Space Affine Matching Approach to fMRI Time Series Analysis.

For fMRI time series analysis, an important challenge is to overcome the potential delay between hemodynamic response signal and cognitive stimuli signal, namely the same frequency but different phase (SFDP) problem. In this paper, a novel space affine matching feature is presented by introducing the time domain and frequency domain features. The time domain feature is used to discern different stimuli, while the frequency domain feature to eliminate the delay. And then we propose a space affine matching (SAM) algorithm to match fMRI time series by our affine feature, in which a normal vector is estimated using gradient descent to explore the time series matching optimally. The experimental results illustrate that the SAM algorithm is insensitive to the delay between the hemodynamic response signal and the cognitive stimuli signal. Our approach significantly outperforms GLM method while there exists the delay. The approach can help us solve the SFDP problem in fMRI time series matching and thus of great promise to reveal brain dynamics.

Novel ensemble techniques for classification of rolling element bearing faults

Rolling element bearings are the critical mechanical components in industrial applications. These rotary elements work continuously under different operating and environmental conditions. It leads to generation of various defects over the operating surfaces of bearing components. In this study, four machine learning techniques, two ensemble techniques, i.e., rotation forest and random subspace, and two well-established techniques, i.e., support vector machine and artificial neural network, are utilized for fault severity classification. Time domain, frequency domain and wavelet-based features are extracted from the raw vibration signals of rolling element bearings for the investigations. Four feature ranking techniques are employed to rank extracted features. Comparative study is carried out among the machine learning techniques with and without the ranking of features. Present study not only investigates various machine learning techniques but also examines the performance of various feature ranking techniques for fault severity classification of rolling element bearings. Results show that ensemble techniques have superior classification efficiency and take very less computational time for the analysis in comparison to support vector machine and artificial neural network.

Tool condition monitoring system based on support vector machine and differential evolution optimization

A tool condition monitoring system based on support vector machine and differential evolution is proposed in this article. In this system, support vector machine is used to realize the mapping between the extracted features and the tool wear states. At the same time, two important parameters of the support vector machine which are called penalty parameter C and kernel parameter [Formula: see text] are optimized simultaneously based on differential evolution algorithm. In order to verify the effectiveness of the proposed system, a multi-tooth milling experiment of titanium alloy was carried out. Cutting force signals related to different tool wear states were collected, and several time domain and frequency domain features were extracted to depict the dynamic characteristics of the milling process. Based on the extracted features, the differential evolution-support vector machine classifier is constructed to realize the tool wear classification. Moreover, to make a comparison, empirical selection method and four kinds of grid search algorithms are also used to select the support vector machine parameters. At the same time, cross validation is utilized to improve the robustness of the classifier evaluation. The results of analysis and comparisons show that the classification accuracy of differential evolution-support vector machine is higher than empirical selection-support vector machine. Moreover, the time consumption of differential evolution-support vector machine classifier is 5 to 12 times less than that of grid search-support vector machine.

A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders

Electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. This paper is aim at introducing an effective multi-classifier approach to enhance classification accuracy .The proposed system employs both time domain and time-frequency domain features of motor unit action potentials (MUAPs) extracted from an EMG signal. Different classification strategies including single classifier and multiple classifiers with time domain and time frequency domain features were investigated. Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classifier used predict class label (Myopathic, Neuropathic, or Normal) for a given MUAP. Extensive analysis is performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms overall classification accuracy.

A Novel Method for Mechanical Fault Diagnosis Based on Variational Mode Decomposition and Multikernel Support Vector Machine

A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs) by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE) is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.

A Multi-Classifier Approach of EMG Signal Classification for Diagnosis of Neuromuscular Disorders

Electromyographic (EMG) signal provide a significant source of information for diagnosis, treatment and management of neuromuscular disorders. This paper is aim at introducing an effective multi-classifier approach to enhance classification accuracy .The proposed system employs both time domain and time-frequency domain features of motor unit action potentials (MUAPs) extracted from an EMG signal. Different classification strategies including single classifier and multiple classifiers with time domain and time frequency domain features were investigated. Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classifier used predict class label (Myopathic, Neuropathic, or Normal) for a given MUAP. Extensive analysis is performed on clinical EMG database for the classification of neuromuscular diseases and it is found that the proposed methods provide a very satisfactory performance in terms overall classification accuracy.

Research on Mechanical Fault Diagnosis Scheme Based on Improved Wavelet Total Variation Denoising

Wavelet analysis is a powerful tool for signal processing and mechanical equipment fault diagnosis due to the advantages of multiresolution analysis and excellent local characteristics in time-frequency domain. Wavelet total variation (WATV) was recently developed based on the traditional wavelet analysis method, which combines the advantages of wavelet-domain sparsity and total variation (TV) regularization. In order to guarantee the sparsity and the convexity of the total objective function, nonconvex penalty function is chosen as a new wavelet penalty function in WATV. The actual noise reduction effect of WATV method largely depends on the estimation of the noise signal variance. In this paper, an improved wavelet total variation (IWATV) denoising method was introduced. The local variance analysis on wavelet coefficients obtained from the wavelet decomposition of noisy signals is employed to estimate the noise variance so as to provide a scientific evaluation index. Through the analysis of the numerical simulation signal and real-word failure data, the results demonstrated that the IWATV method has obvious advantages over the traditional wavelet threshold denoising and total variation denoising method in the mechanical fault diagnose.

Multifeatures Fusion and Nonlinear Dimension Reduction for Intelligent Bearing Condition Monitoring

Condition-based maintenance is critical to reduce the costs of maintenance and improve the production efficiency. Data-driven method based on neural network (NN) is one of the most used models for mechanical components condition recognition. In this paper, we introduce a new bearing condition recognition method based on multifeatures extraction and deep neural network (DNN). First, the method calculates time domain, frequency domain, and time-frequency domain features to represent characteristic of vibration signals. Then the nonlinear dimension reduction algorithm based on deep learning is proposed to reduce the redundancy information. Finally, the top-layer classifier of deep neural network outputs the bearing condition. The proposed method is validated using experiment test-bed bearing vibration data. Meanwhile some comparative studies are performed; the results show the advantage of the proposed method in adaptive features selection and superior accuracy in bearing condition recognition.

Spectral Estimation of Noisy Seismogram using Time-Frequency Analyses

Time–Frequency analyses have the advantage of explaining the signal features in both time domain and frequency domain. This paper explores the performance of Time–Frequency analyses on noisy seismograms acquired from seismically active region in NW Himalayan. The Short Term Fourier Transform, Gabor Transform, Wavelet Transform and Wigner-Ville Distribution have been used in the present study to carry out Time-Frequency analyses. Parametric study has been carried out by varying basic parameters viz. sampling, window size and types. Wavelet analysis (Continuous Wavelet Transform) has been studied with different type of wavelets. The seismograms have been stacked in time-frequency domain using Gabor Transform and have been converted using Discrete Gabor Expansion techniques. The Spectrograms reveals better spectral estimation in time-frequency domain than Fourier Transform and hence recommended to estimate dominate frequency components, phase marking and timings of phase. The time of occurrence of frequency component corresponding to maximum energy burst can be identified on seismograms

Residual life prediction for ball bearings based on joint approximate diagonalization of eigen matrices and extreme learning machine

Data-driven approaches have been proved effective for remaining useful life estimation of key components (bearings for example) in rotating machinery. In such approaches, it is important to determine an appropriate degradation indicator from the collected run-to-failure life cycle data. In this paper, a new degradation indicator is introduced based on the joint approximate diagonalization of eigen matrices algorithm. First, a matrix consisting of time domain, frequency domain, and time–frequency domain features extracted from the collected data instances is created. Then a two-layer joint approximate diagonalization of eigen matrices is introduced to transform the matrix to the advanced features (a vector) that represents the behavior of the bearing’s degradation. As an independent component analysis method, the designed two-layer joint approximate diagonalization of eigen matrices is able to eliminate the redundancy of the directly extracted features. Further, the obtained vector is input into an extreme learning machine to train a remaining useful life prediction model. Finally, a set of experimental cases are utilized to verify the presented method. Results show that the two-layer joint approximate diagonalization of eigen matrices is capable of exploring features that reflects the trend of bearing’s degradation state much better. And due to the easy parameter configuration and fast learning speed, the extreme learning machine is capable of training a model that can effectively predict the remaining useful life of the bearings.

An Unsorted Spike-Based Pattern Recognition Method for Real-Time Continuous Sensory Event Detection from Dorsal Root Ganglion Recording.

In functional neuromuscular stimulation systems, sensory information-based closed-loop control can be useful for restoring lost function in patients with hemiplegia or quadriplegia. The goal of this study was to detect sensory events from tactile afferent signals continuously in real time using a novel unsorted spike-based pattern recognition method. The tactile afferent signals were recorded with a 16-channel microelectrode in the dorsal root ganglion, and unsorted spike-based feature vectors were extracted as a novel combination of the time and time-frequency domain features. Principal component analysis was used to reduce the dimensionality of the feature vectors, and a multilayer perceptron classifier was used to detect sensory events. The proposed method showed good performance for classification accuracy, and the processing time delay of sensory event detection was less than 200ms. These results indicated that the proposed method could be applicable for sensory feedback in closed-loop control systems.

A hybrid model for neurological disordered voice classification using time and frequency domain features

Different neurological disorders may lead to speech related problems, due to paralysis in vocal fold or weakness of the related muscles. This may modify the acoustic characteristics of the subject’s voice which may provide important information for detecting certain neurological diseases. The vowel phonation which is acoustically informative and uttered by the patient with not much difficulty is collected and various acoustic features are extracted by time domain and frequency domain techniques. The use of all these features for classification may lead to a large feature space, which may lead to complexity. Hence to avoid this, in the present work experimentation is done by fusing different classifiers which are fed with features extracted from different domains. The time domain and frequency domain features are given to Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) respectively, and the intermediate decision of these classifiers is given to another SVM to identify the voice signal as normal or diseased. It is observed that this hybrid classifier model has shown some improvement with a classification accuracy of 91.43% compared to single GMM classifier with an accuracy of classification of 90% with frequency domain features as input.

Classification of team sport activities using a single wearable tracking device

Wearable tracking devices incorporating accelerometers and gyroscopes are increasingly being used for activity analysis in sports. However, minimal research exists relating to their ability to classify common activities. The purpose of this study was to determine whether data obtained from a single wearable tracking device can be used to classify team sport-related activities. Seventy-six non-elite sporting participants were tested during a simulated team sport circuit (involving stationary, walking, jogging, running, changing direction, counter-movement jumping, jumping for distance and tackling activities) in a laboratory setting. A MinimaxX S4 wearable tracking device was worn below the neck, in-line and dorsal to the first to fifth thoracic vertebrae of the spine, with tri-axial accelerometer and gyroscope data collected at 100Hz. Multiple time domain, frequency domain and custom features were extracted from each sensor using 0.5, 1.0, and 1.5s movement capture durations. Features were further screened using a combination of ANOVA and Lasso methods. Relevant features were used to classify the eight activities performed using the Random Forest (RF), Support Vector Machine (SVM) and Logistic Model Tree (LMT) algorithms. The LMT (79–92% classification accuracy) outperformed RF (32–43%) and SVM algorithms (27–40%), obtaining strongest performance using the full model (accelerometer and gyroscope inputs). Processing time can be reduced through feature selection methods (range 1.5–30.2%), however a trade-off exists between classification accuracy and processing time. Movement capture duration also had little impact on classification accuracy or processing time. In sporting scenarios where wearable tracking devices are employed, it is both possible and feasible to accurately classify team sport-related activities.

Longjing tea quality classification by fusion of features collected from E-nose

Electronic nose (E-nose) was employed for quality classification of Xihu-Longjing tea in this paper. Four grades of Xihu-Longjing tea, which were classified by tea experts, were attempted in the experiment. The aim of this study is to conduct feature fusion method with two kinds of features — time domain features and frequency domain features. Accounting for the redundant information provided by some sensors, Fisher criterion was conducted for sensor selection, and dimensionality reduction algorithms were applied for further feature selection. Experimental results showed that the fused features could better represent signal characteristics compared with the single features. Based on the fused features, the performances of linear and nonlinear dimensionality reduction algorithms were compared. Experimental results indicated that nonlinear algorithms were more effective in feature selection than linear algorithms, and the highest recognition rate could reach to 100% by KLDA. The results achieved in this paper showed the superiority of the fused features in representing signal characteristics, and indicated that E-nose can be successfully used in quality classification of Xihu-Longjing tea.

Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis.

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved.

Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings

Condition-based maintenance, which includes both diagnosis and prognosis of faults, is a topic of growing interest for improving the reliability of electrical drives. Bearings constitute a large portion of failures in rotational machines. Although many techniques have been successfully applied for bearing fault diagnosis, prognosis of faults, particularly predicting the remaining useful life (RUL) of bearings, is a remaining challenge. The main reasons for this are a lack of accurate physical degradation models and limited labeled training data. In this paper, we introduce a data-driven methodology, which relies on both time and time–frequency domain features to track the evolution of bearing faults. Once features are extracted, an analytical function that best approximates the evolution of the fault is determined and used to learn the parameters of an extended Kalman filter (KF). The learned extended KF is applied to testing data to predict the RUL of bearing faults under different operating conditions. The performance of the proposed method is evaluated on PRONOSTIA experimental testbed data.

English

The aim of this study is to analyse and compare the heart rate variability (HRV) of normal and hypertension cases using time domain, frequency domain, and nonlinear methods. For short term HRV analysis, a five-minute electrocardiogram (ECG) of 57 normal and 56 hypertension subjects were recorded with prior verification of their clinical status by a cardiologist. Most time domain features of hypertension cases have clearly reduced values over normal subjects, frequency domain features, like power in different spectral bands, also have the distinguishable decreased values, whereas sympathovagal balance has clear edge over hypertension cases than normal cases. Nonlinear parameters of Poincare plot, approximate entropy and sample entropy, have higher values in normal cases when compared with hypertension cases. Support vector machine-based binary system classifies these two classes with 100 per cent accuracy and 100 per cent sensitivity when all time domain, frequency domain, and nonlinear features were used. It may work as a better predictor for in patients with hypertension. Science Journal, Vol. 64, No. 6, November 2014, pp.542-548, DOI:http://dx.doi.org/10.14429/dsj.64.7867

Modeling of Harmonic Drive System with Low-Frequency Resonance

In the design process of lightweight harmonic drive system, low-frequency resonance generally affects controller design, and restricts system servo performance. In the design stage of system, it has great significance that the resonant frequencies and dynamic performances could be predicted. This paper presents a modeling method of harmonic drive system which has low-frequency resonance. Takes an uniaxial harmonic drive system as example to describe the implementation process of the modeling method. The dynamics model of harmonic drive system has been built. Using Ansys, Recurdyn and Matlab, the rigid-flexible coupling dynamics simulation model of system has been established. The simulation model analysis and the actual system experiment have been done respectively. Comparing the simulation and experiment results, it shows that: the open-loop time-frequency domain characteristics and the structural modals of simulation and experiment results are basically consistent. Using this method, the resonant frequency and dynamic performance of harmonic drive system can be predicted in the system design stage, and improve design efficiency.