Teleoperation has been widely used to perform tasks in dangerous and unreachable environments by replacing humans with controlled agents. The idea of human-robot interaction (HRI) is very important in teleoperation. Conventional HRI input devices include keyboard, mouse and joystick, etc. However, they are not suitable for handicapped users or people with disabilities. These devices also increase the mental workload of normal users due to simultaneous operation of multiple HRI input devices by hand. Hence, HRI based on gaze tracking with an eye tracker is presented in this study. The selection of objects is of great importance and occurs at a high frequency during HRI control. This paper introduces gaze gestures as an object selection strategy into HRI for drone teleoperation. In order to test and validate the performance of gaze gestures selection strategy, we evaluate objective and subjective measurements, respectively. Drone control performance, including mean task completion time and mean error rate, are the objective measurements. The subjective measurement is the analysis of participant perception. The results showed gaze gestures selection strategy has a great potential as an additional HRI for use in agent teleoperation.
Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls. Supplementary Information The online version contains supplementary material available at 10.1007/s11682-022-00739-1.
In fiber Bragg grating (FBG) sensor networks, the highly overlapped spectral signals can lead to considerable errors in center wavelength demodulation. To tackle this problem, we utilize the fully convolutional time-domain audio separation network (Conv-TasNet) model to produce a distinct spectral signal, which is then demodulated using the dual-weight centroid approach to determine the spectral signal's center wavelength. Specifically, we first demonstrate the theoretical feasibility of the Conv-TasNet model on simulated data. Experimental results show that the Conv-TasNet model can separate the signals of three FBG sensors. After that, we collect the spectral data and further train and validate the model based on the pretrained model of the simulated data to see how it performs on the real data. The experiments consistently illustrate superior performance of our Conv-TasNet model that can also separate actual spectrum signals. The same performance can be achieved by applying the pretrained model but with less training data. The model obtains a competitive performance compared to currently available methods. Moreover, the method provides a solution for improving the multiplexing performance of the FBG sensor network.
It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first‐episode drug‐naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big‐Data Center for Depression Research. Twenty large‐scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual‐level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN‐MDD subtypes and their associated neurobiological heterogeneity.
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