ALBERT

Description: Sign language recognition (SLR) is an important communication tool between the deaf and the external world. It is highly necessary to develop a worldwide continuous and large-vocabulary-scale SLR system for practical usage. In this paper, we propose a novel phonology- and radical-coded Chinese SLR framework to demonstrate the feasibility of continuous SLR using accelerometer (ACC) and surface electromyography (sEMG) sensors. The continuous Chinese characters, consisting of coded sign gestures, are first segmented into active segments using EMG signals by means of moving average algorithm. Then, features of each component are extracted from both ACC and sEMG signals of active segments (i.e., palm orientation represented by the mean and variance of ACC signals, hand movement represented by the fixed-point ACC sequence, and hand shape represented by both the mean absolute value (MAV) and autoregressive model coefficients (ARs)). Afterwards, palm orientation is first classified, distinguishing “Palm Downward” sign gestures from “Palm Inward” ones. Only the “Palm Inward” gestures are sent for further hand movement and hand shape recognition by dynamic time warping (DTW) algorithm and hidden Markov models (HMM) respectively. Finally, component recognition results are integrated to identify one certain coded gesture. Experimental results demonstrate that the proposed SLR framework with a vocabulary scale of 223 characters can achieve an averaged recognition Sensors 2015, 15 23304 accuracy of 96.01% ± 0.83% for coded gesture recognition tasks and 92.73% ± 1.47% for character recognition tasks. Besides, it demonstrated that sEMG signals were rather consistent for a given hand shape independent of hand movements. Hence, the number of training samples will not be significantly increased when the vocabulary scale increases, since not only the number of the completely new proposed coded gestures is constant and limited, but also the transition movement which connects successive signs needs no training samples to model even though the same coded gesture performed in different characters. This work opens up a possible new way to realize a practical Chinese SLR system.

Description: Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For practical reasons, a single EEG channel system must be used in these situations. Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings. To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case. This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques. We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications. The proposed method is shown to significantly outperform all other methods. It can successfully remove muscular artifacts without altering the underlying EEG activity. It is thus a promising tool for use in ambulatory healthcare systems.

Description: The newly released MODIS Collection 6 aerosol products have been widely used to evaluate fine particulate matter with a 10 km Dark Target aerosol optic depth (DT AOD) product, a new 3 km DT AOD product and an enhanced Deep Blue (DB) AOD product. However, the representativeness of MODIS AOD products under different air quality conditions remains unclear. In this study, we obtained all three types of MODIS Terra AOD from 2001 to 2015 and Aqua AOD from 2003 to 2015 for the Beijing region to study the performance of the different AOD products (Collection 6) under different air quality situations. The validation of three MODIS AOD products suggests that DB AOD has the highest accuracy with an expected error (EE) envelope (containing at least 67% of the matchups on a scatter plot) of 0.05 + 0.15τ, followed by 10 km DT AOD (0.08 + 0.2τ) and 3 km DT AOD (0.35 + 0.15τ), specifically for Beijing. Near-surface PM2.5 concentrations during the passage of MODIS from 2013 to 2015 were also obtained to categorize air quality as unpolluted, moderately, and heavily polluted, as well as to analyze the performance of the different AOD products under different air quality conditions. Very few MODIS 3 km DT retrievals appeared on heavily polluted days, making it almost impossible to play an effective role in air quality applications in Beijing. While the DB AOD allowed for considerable retrievals under all air quality conditions, it had a coarse spatial resolution. These results demonstrate that the MODIS 3 km DT AOD product may not be the appropriate proxy to be used in the satellite retrieval of surface PM2.5, especially for those areas with frequent haze-fog events like Beijing.