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In this paper, we have developed an asynchronous brain-computer interface (BCI)-based lower limb exoskeleton control system based on motor imagery (MI). By decoding electroencephalography (EEG) signals in real-time, users were able to walk forward, sit down, and stand up while wearing the exoskeleton. EEG feature vectors associated with the motor imagery were extracted from the filtered EEG signals...
Robust gait phase prediction is crucial to exoskeleton robots, as it detects the intention of users and improves the lag of motion signals. Therefore, this paper predicts gait phases from two perspectives, including one perspective of spatial features and the other of spatio-temporal features. We employ two machine learning models from the two perspectives to predict. One is support vector machine...
Human Computer Interaction would be much more smooth with the implementation of rapid recognition, the aim of which is to recognize the hand gesture before it is completed. In this paper, a rapid recognition for dynamic hand gestures using leap motion is proposed. The database contains the three-dimensional motion trajectory of the numbers and the alphabet (36 gestures in total) which captured by...
This paper introduces a novel approach to predict human motion for the Non-binding Lower Extremity Exoskeleton (NBLEX). Most of the exoskeletons must be attached to the pilot, which exists potential security problems. In order to solve these problems, the NBLEX is studied and designed to free pilots from the exoskeletons. Rather than applying Electromyography (EMG) and Ground Reaction Force (GFR)...
In this paper, we study the stability region for a set of intelligent controllers developed by learning human expert control skills using support vector machines (SVMs). Based on the discrete-time system Lyapunov theory, a Chebychev points based estimation approach is proposed to evaluate the stability region, a key property of this set of SVM-based human learning controllers. One of such learning...
This article utilizes near infrared reflectance spectroscopy (NIRS) technology to quantificationally analyze protein content of chocolate, using genetic support vector regression (GSVR) to build spectrum calibration model. GSVR first adopts genetic algorithm to select the efficiency wavelength regions, and then applies linear support vector regression (SVR) to establish a calibration model with the...
In this paper we present the use of non-contact near infrared spectroscopy (NIRS) technology employing a diffuse reflection fiber optic probe for discrimination of chocolate varieties. 120 samples of 8 typical varieties of chocolate are selected randomly, and the samples are scanned in diffuse reflectance mode by a cooled InGaAs array spectrometer (950-1700 nm). Partial least squares (PLS) method...
This paper presents an automatic method to detect abnormal crowd density by using texture analysis and learning, which is very important for the intelligent surveillance system in public places. By using the perspective projection model, a series of multi-resolution image cells are generated to make better density estimation in the crowded scene. The cell size is normalized to obtain a uniform representation...
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