• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.157-166
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• 1995

The Electroencephalogram (EEG) and evoked potential (EP) t;ave widely been used for study of brain functions. The EEG and EP signals acquired from multi-channel electrodes placed on the head surface are often interfered by other relatively large physiological signals such as electromyogram (EMG) or electroculogram (EOG). Since these artifact-affected EEG signals degrade EEG mapping, the removal of the artifact-affected EEGs is one of the key elements in neuro-functional mapping. Conventionally this task has been carried out by human experts spending lots of examination time. In this paper a neural-network based classification is proposed to replace or to reduce human expert's efforts and time. From experiments, the neural-network based classification performs as good as human experts : variation of decisions between the neural network and human expert appears even smaller than that between human experts.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.852-855
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• 1995

In modern plant lndustry, dignosis system is an essential implement because a human operator cannot check the state of system all the time. The recent facility needs a computer system which is able to replace and extense the function of the human expert. Checking the state of the plant system, the computer system uses signals form sensors attached to the plant systems. But, It is difficult to predict the cause of the failure from the sensing signals. Because the relationship among the signals cannot be easily represented by mathematical models. So expert system based on a fuzzy rule and Neural network method is sugguested. Expert system decide whether aa state of the system is ordinary of failure by the evaluation of the signals. If the state of the system is unstable, expert system preprocess the signals. When fault is occurred in the machine, the expert system dignoses the state of the system and find the cause as a primary tool. If the expert system dose not find the adequate cause successfully, neural network system uses the preprocessed signals as an input and propose a cause of the failure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1076-1094
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• 2022

Technology for emotion recognition is an essential part of human personality analysis. To define human personality characteristics, the existing method used the survey method. However, there are many cases where communication cannot make without considering emotions. Hence, emotional recognition technology is an essential element for communication but has also been adopted in many other fields. A person's emotions are revealed in various ways, typically including facial, speech, and biometric responses. Therefore, various methods can recognize emotions, e.g., images, voice signals, and physiological signals. Physiological signals are measured with biological sensors and analyzed to identify emotions. This study employed two sensor types. First, the existing method, the binary arousal-valence method, was subdivided into four levels to classify emotions in more detail. Then, based on the current techniques classified as High/Low, the model was further subdivided into multi-levels. Finally, signal characteristics were extracted using a 1-D Convolution Neural Network (CNN) and classified sixteen feelings. Although CNN was used to learn images in 2D, sensor data in 1D was used as the input in this paper. Finally, the proposed emotional recognition system was evaluated by measuring actual sensors.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.845-848
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• 2003

There are many researches on using human neuro-biological signals for various problems such as controlling a mechanical object and/or interfacing human with the computer. It is one of very interesting topics that human can use various instruments without learning specific knowledge if the instruments can be controlled as human intends. In this paper, we proposed an intelligent motion planner for a redundant manipulator, which is controlled by humans neuro-biological signals, especially, EOG (Electrooculogram). We found the optimal motion planner for the redundant manipulator that can move to the desired point. We used neural networks to find the inverse kinematics solution of the manipulator. We also showed the performance of the proposed motion planner with several simulations.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.9-15
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• 2008

Unlike robotic systems, humans excel at a variety of tasks by utilizing their intrinsic impedance, force sensation, and tactile contact clues. By examining human strategy in arm impedance control, we may be able to teach robotic manipulators human''s superior motor skills in contact tasks. This paper develops a novel method for estimating and predicting the human joint impedance using the electromyogram(EMG) signals and limb position measurements. The EMG signal is the summation of MUAPs (motor unit action potentials). Determination of the relationship between the EMG signals and joint stiffness is difficult, due to irregularities and uncertainties of the EMG signals. In this research, an artificial neural network(ANN) model was developed to model the relation between the EMG and joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. The feasibility of the developed model was confirmed by experiments and simulations.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.612-618
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• 2014

A pattern classifier-based approach for recognizing internal states of human participants in interactions is presented along with its experimental results. The approach includes a step for collecting video recordings of human-human interactions or humanrobot interactions and subsequently analyzing the videos based on human coded annotations. The annotation includes social signals directly observed in the video recordings and the internal states of human participants indirectly inferred from those observed social signals. Then, a pattern classifier is trained using the annotation data, and tested. In our experiments on human-robot interaction, 7 video recordings were collected and annotated with 20 social signals and 7 internal states. Several experiments were performed to obtain an 84.83% recall rate for interaction engagement, 93% for concentration intention, and 81% for task comprehension level using a C4.5 based decision tree classifier.

• Journal of the Ergonomics Society of Korea
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• v.32 no.4
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• pp.371-380
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• 2013

Objective: The aim of this study is to investigate the effects of design parameters of vibrotactile signals on semantic association with transmitted information conveying different meanings. Background: As information communication relying on human visual channel becomes excessive, the utility of vibrotactile signals is being interested as a substitute measure of delivering information. Properly designed hapticons may relieve burden of visual communication by rendering distinct and meaningfully compatible haptic sensations. Method: A typical Kansei engineering approach was adopted in this study. Ten most distinctive hapticons were selected among those having different frequencies and amplitudes. Associations between the hapticons and twenty four pairs of adjectives used to describe the state of automobile in control were gathered from thirty subjects using semantic differential scales. Results: The selected pairs of adjectives were summarized by factor analysis into two semantic dimensions named 'Awareness' and 'Directionality'. The experimental hapticons matched with the semantic dimensions were presented as a haptic emotion map. Conclusion: The results from this study support that frequencies and amplitudes of haptic signals play important roles in arousing different human perceptions regarding the two haptic emotional dimensions. Application: Properly designed hapticons with respect to the contents of transmitted information will increase human operator's situation awareness as well as system performance. The result from this study can be used to develop standardized hapticons for active haptic communication.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.11
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• pp.5527-5545
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• 2019

Through-wall ultra-wide band (UWB) radar has been considered as one of the preferred and non-contact technologies for the targets detection owing to the better time resolution and stronger penetration. The high time resolution is a result of a larger of bandwidth of the employed UWB pulses from the radar system, which is a useful tool to separate multiple targets in complex environment. The article emphasised on human subject localization and detection. Human subject usually can be detected via extracting the weak respiratory signals of human subjects remotely. Meanwhile, the range between the detection object and radar is also acquired from the 2D range-frequency matrix. However, it is a challenging task to extract human respiratory signals owing to the low signal to clutter ratio. To improve the feasibility of human respiratory signals detection, a new method is developed via analysing the standard deviation based kurtosis of the collected pulses, which are modulated by human respiratory movements in slow time. The range between radar and the detection target is estimated using joint time-frequency analysis (JTFA) of the analysed characteristics, which provides a novel preliminary signature for life detection. The breathing rates are obtained using the proposed accumulation method in time and frequency domain, respectively. The proposed method is validated and proved numerically and experimentally.

• Journal of the Ergonomics Society of Korea
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• v.23 no.1
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• pp.1-12
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• 2004

The purpose of this study was to develop an automatic expert system for the evaluation of human sensibility, where human sensibility can be inferred from objective physiological signals. The study aim was also to develop an algorithm in which human arousal and pleasant level can be judged by using measured physiological signals. Fuzzy theory was applied for mathematical handling of the ambiguity related to evaluation of human sensibility. and the degree of belonging to a certain sensibility dimension was quantified by membership function through which the sensibility evaluation was able to be done. Determining membership function was achieved using results from a physiological signal database of arousal/relaxation and pleasant/unpleasant that was generated from imagination. To induce one final result (arousal and pleasant level) based on measuring the results of more than 2 physiological signals and the membership function of each physiological signal. Dempster-Shafer's rule of combination in evidence was applied, through which the final arousal and pleasant level was inferred.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.223-236
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• 2012

We develop a video processing method that allows the more advanced human perception oriented video coding. The proposed method necessarily reflects all influences by the rate-distortion based optimization and the human visual perception that is affected by the visual saliency, the limited space-time resolution and the regional moving history. For reflecting the human perceptual effects, we devise an online moving pattern classifier using the Hedge algorithm. Then, we embed the existing visual saliency into the proposed moving patterns so as to establish a human visual perception model. In order to realize the proposed human visual perception model, we extend the conventional foveation filtering method. Compared to the conventional foveation filter only smoothing less stimulus video signals, the developed foveation filter can locally smooth and enhance signals according to the human visual perception without causing any artifacts. Due to signal enhancement, the developed foveation filter more efficiently transfers the bandwidth saved at smoothed signals to the enhanced signals. Performance evaluation verifies that the proposed video processing method satisfies the overall video quality, while improving the perceptual quality by 12%~44%.