Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier

Critical exponent analysis applied to surface EMG signals for multifunction myoelectric control

Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855∼2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications

Evaluating Feature Extraction Methods of Electrooculography (EOG) Signal for Human-Computer Interface

AbstractElectrooculography (EOG) signal is a widely and successfully used to detect activities of human eye. Use of the EOG signals as a control signal for human-computer interface (HCI) plays a central role in the understanding, characterization and classification of eye movements which can be applied to a wide variety of applications consisting virtual mouse and keyboard control, electric power wheelchairs and industrial assistive robots. The advantages of the EOG-based interface over other conventional interfaces have been presented in the last two decades; however, due to a lot of information in EOG signals, the extraction of useful features should be done before the classification task. In this study, fourteen useful features extracted from two directional EOG signals: vertical (V) and horizontal (H) signals have been presented and evaluated. There are the maximum peak and valley amplitude values (PAV and VAV), the maximum peak and valley position values (PAP and VAP), the area under curve value (AUC), the number of threshold crossing value (TCV), and EOG variance (VAR), which are derived from both V and H signals. In the experiments, EOG signals obtained from three healthy subjects with eight directional eye movements were employed: up, down, right, left, up-right, up-left, down-right and down-left. The mean feature values and their standard deviations have been reported. Most features show the difference between the mean feature values. Using the analysis-of-variation test, the differences in mean features between the movements are statistically significant for ten features (p < 0.0001), particularly for the VAV, VAP, AUC, TCV and VAR of V signal, and the PAV, VAV, AUC, TCV and VAR of H signal. The combination of these features may be useful for the classification of EOG signals in both class separability and robustness point of views. Using multiple features with sufficient classifiers or threshold techniques is recommended to be evaluated in further analysis. These features can be useful for various advanced HCI applications in future researches, notably eye-exercise and eye-writing recognitions

Evaluation of feature extraction techniques and classifiers for finger movement recognition using surface electromyography signal

© 2018, International Federation for Medical and Biological Engineering. Electromyography (EMG) in a bio-driven system is used as a control signal, for driving a hand prosthesis or other wearable assistive devices. Processing to get informative drive signals involves three main modules: preprocessing, dimensionality reduction, and classification. This paper proposes a system for classifying a six-channel EMG signal from 14 finger movements. A feature vector of 66 elements was determined from the six-channel EMG signal for each finger movement. Subsequently, various feature extraction techniques and classifiers were tested and evaluated. We compared the performance of six feature extraction techniques, namely principal component analysis (PCA), linear discriminant analysis (LDA), uncorrelated linear discriminant analysis (ULDA), orthogonal fuzzy neighborhood discriminant analysis (OFNDA), spectral regression linear discriminant analysis (SRLDA), and spectral regression extreme learning machine (SRELM). In addition, we also evaluated the performance of seven classifiers consisting of support vector machine (SVM), linear classifier (LC), naive Bayes (NB), k-nearest neighbors (KNN), radial basis function extreme learning machine (RBF-ELM), adaptive wavelet extreme learning machine (AW-ELM), and neural network (NN). The results showed that the combination of SRELM as the feature extraction technique and NN as the classifier yielded the best classification accuracy of 99%, which was significantly higher than those from the other combinations tested. [Figure not available: see fulltext.]

A skilful seasonal prediction for wintertime rainfall in southern Thailand

Year-to-year variations of southern Thailand rainfall (STR) in boreal winter exert profound social and economic impacts, whereas current multimodel ensemble prediction systems have low skills to predict the STR. This study proposes a physical-based seasonal prediction model for the winter STR 1 month in advance using the outputs from the dynamic models. The prediction model is constructed using linear regression, with the tropical western Pacific (TWP) sea surface temperature (SST) anomaly in preceding October as a predictor. Its prediction skill in the leave-five-out cross-validation is significantly higher than that of the multimodel ensemble mean. The mechanism behind this model is also discussed. In October, the warm TWP SST anomalies can trigger anomalous low-level convergence surrounding the South China Sea in terms of the Matsuno-Gill mechanism and persist into the following winter, causing above-than-normal STR. This information is essential and may provide another perspective to improve the model prediction on the winter STR

Developing an ICF Core Set for adults with cerebral palsy: A Global expert survey of relevant functions and contextual factors

OBJECTIVE: To identify areas of functioning in adults with cerebral palsy that are considered relevant by experts, in order to develop an International Classification of Functioning, Disability and Health (ICF) Core Set for adults with cerebral palsy. PARTICIPANTS: Experts from various professional backgrounds worldwide who had experience working with adults with cerebral palsy for ≥2 years and were able to complete the survey in the English language. METHODS: A cross-sectional study using an international internet-based survey. The experts were asked to address relevant areas of functioning in adults with cerebral palsy. These areas of functioning were then linked to the ICF and the frequencies analysed. RESULTS: A total of 126 experts from 32 countries completed the survey. From the responses, 217 unique second-level ICF categories were identified. The three most frequently mentioned categories were "design, construction and building products and technology of buildings for public use (e150, 77%) and private use" (e155, 67%), followed by "sensation of pain" (b280, 62%). CONCLUSION: The broad diversity of ICF categories reported by the experts emphasize the known heterogeneity of cerebral palsy and the variety of func-tioning in adulthood. They also reported on many environmental factors, illustrating the importance of person-environment interactions. These findings provide information about relevant issues for use in developing an ICF Core Set for adults with cerebral palsy