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Caloric vestibular stimulation (CVS) and galvanic vestibular stimulation (GVS) act primarily on the peripheral vestibular system. Although the electrical current applied during GVS is thought to flow through peripheral vestibular organs, some current may spread into areas within the central nervous system, particularly when the bilateral galvanic vestibular stimulation (bGVS) method is used. According to Alexander's law, the magnitude of nystagmus increases with eccentric gaze movement, due to the function of the neural integrator (NI); thus, if the information for vestibular stimulation corresponds to Alexander's law, the peripheral vestibular organ is stimulated. Therefore, it would appear that if CVS results comply with Alexander's law, and bGVS results do not, the sites stimulated by bGVS are not perfectly located in the peripheral vestibular area. In our experiments on normal human subjects, the magnitude of nystagmus under CVS increased with rising gaze eccentricity in the direction that the magnitude of the nystagmus increases, and this change was found to follow Alexander's law. However, in the case of nystagmus under bGVS, results did not follow Alexander's law. In addition, study of the influences of bGVS at different current intensities on nystagmus magnitude showed that bGVS at 5 mA distorted nystagmus magnitude more than at 3 mA, which suggests bGVS acts not only on the peripheral vestibular nerves, but also on some areas of the central nervous system, particularly the NI. According to our experiments, bGVS directly affects neural integrator function.

We present a novel GA based feature extractor for network optimal initialization.Our method selects more dominant feature extractor in merge phase using GA.Results show improvements recognition performance in comparison with DBNs.We also suggest a new approach for retraining additional classes as its application.Our approach for retraining can add output classes at lower error rate than DBNs. In this paper, we propose a novel split training and merge algorithm for deep learning. The proposed algorithm improves recognition accuracy and suggests a new approach for retraining. The algorithm is motivated by the genetic algorithm (GA) and is composed of two procedures. The first procedure initializes two individual networks using deep belief networks (DBNs), and the second procedure merges the two networks using the GA. Biases and weights of the network that is trained using DBNs are represented as a matrix between each layer, and each row of this matrix is used as a chromosome in the merge procedure. To evaluate the performance, we conduct two set of experiments. The first set is to recognize accuracy of the proposed algorithm, and the second set is for a new retraining approach. The results show that the proposed algorithm has a lower average error rate (6.84 ? 4.57%) than the DBNs, and it can add classes at a lower average error rate (9.06 ? 6.17% and 10.17 ? 4.51%) without pre-training using the restrict Boltzmann machines (RBMs) for existing classes data.

An enhanced algorithm is proposed to recognize multi-channel electromyography (EMG) patterns using deep belief networks (DBNs). It is difficult to classify the EMG features because an EMG signal has nonlinear and time-varying characteristics. Therefore, in several previous studies, various machine-learning methods have been applied. A DBN is a fast, greedy learning algorithm that can find a fairly good set of weights rapidly, even in deep networks with a large number of parameters and many hidden layers. To evaluate this model, we acquired EMG signals, extracted their features, and then compared the model with the DBN and other conventional classifiers. The accuracy of the DBN is higher than that of the other algorithms. The classification performance of the DBN model designed is approximately 88.60%. It is 7.55% (p=9.82×10−12) higher than linear discriminant analysis (LDA) and 2.89% (p=1.94×10−5) higher than support vector machine (SVM). Further, the DBN is better than shallow learning algorithms or back propagation (BP), and this model is effective for an EMG-based user-interfaced system.

In this paper, the angle of the thighs that appear during walking condition to balance estimation to the left and right leg was occurred during normal walking. Get over to the limitations of gait analysis using image processing or foot pressure that was used a lot in the previous, the angle of the thigh were used for estimation of asymmetric gait. We implemented heathy five adult male to test targeting and gait and obtained cycle data from 10 times. For this research, Thigh-Angle measurement device were developed, and attached to in a position of 20° for flexion and 15° for extension to measure the angle of the thigh. Also, in order to verify the reliability of estimation of asymmetric gait using thigh-angle, it was compared with the result of asymmetric gait estimation using foot pressure. The results of this paper, using the thigh angle is the average of 16.84% higher than using pressure to accuracy of determine the gait imbalance.

In this paper; we introduce an enhanced electromyography (EMG) pattern recognition algorithm based on a split-and-merge deep belief network (SM-DBN). Generally, it is difficult to classify the EMG features because the EMG signal has nonlinear and time-varying characteristics. Therefore, various machine-learning methods have been applied in several previously published studies. A DBN is a fast greedy learning algorithm that can identify a fairly good set of weights rapidly—even in deep networks with a large number of parameters and many hidden layers. To reduce overfitting and to enhance performance, the adopted optimization method was based on genetic algorithms (GA). As a result, the performance of the SM-DBN was 12.06% higher than conventional DBN. Additionally, SM-DBN results in a short convergence time, thereby reducing the training epoch. It is thus efficient in reducing the risk of overfitting. It is verified that the optimization was improved using GA.

Although physical exercise of patients is very important for their quality of life, the guide line for physical movement considering muscle fatigue is not insufficient. In this study, we propose an easy detection algorithm for muscle fatigue estimation. EMG signals of five healthy volunteers were measured at the quadriceps femoris muscle and the tibialis anterio in order to estimate muscle fatigue. RMMV (ratio between maximum and minimum value) of median frequency components of EMG is proposed as a muscle fatigue parameter. With our proposed parameter, muscle fatigue state was detected easily. It is expected that our method could be used for a parameter that recommend exercise intensity during gait rehabilitation.

Photoluminescence and photoreflectance measurements are carried out in order to investigate the effects of annealing and hydrogenation on the defect levels of neutron-transmutation-doped (NTD) GaAs. The damage to the crystallinity of NTD GaAs is recovered by annealing, and the defects in the annealed NTD GaAs are compensated for by hydrogen atoms after hydrogenation. Since the transmutation-doped impurities in GaAs are compensated for by the injection of hydrogen atoms, Franz-Keldysh oscillations appear due to the large variation in the surface electric field.

Photoluminescence measurements are carried out in order to investigate the effects on the defect levels of neutron-transmutation-doped (NTD) GaAs due to annealing and hydrogenation. The peak intensity corresponding to the transition from the conduction band to the Ge acceptor [Ge(B–A)] increases at an annealing temperature of 600°C, and then decreases remarkably around a temperature of 650°C; on the other hand, the peak intensity of the defect complex peak increases at 600°C. After annealing and subsequent hydrogenation, the peak of the transitions between the band and the carbon acceptors [C(B–;A)] is resolved clearly into the C(B–A) and Ge(B–A) peaks, and the intensity of the peak corresponding to the As vacancy and carbon complex center on As sites decreases remarkably. These results indicate that the crystallinity of the NTD GaAs is improved by annealing and hydrogenation and that the defect complex center is passivated by thermal treatment and hydrogenation.

In this paper, we propose an architecture of walking assistant robot for the elderly which is usable at outdoor environment. It equips PXA255 embedded board, motors, laser range finder and GPS receiver. User operates it using smart handle bar. It is useful to the elderly for more comfortable walking and path guidance.

The paradigm of robot which performs specific tasks remotely has changed into intelligent robot to perform public and individual tasks. Especially, in the robot and security industry, security guard robot provides a variety of information to the user and achieves its duty through the Web. The communication technique for these telepresence robots takes charge of the probability of various services in the robot industry. Last year, the interface for telepresence robot has developed over the Web or RF between robots, but these systems have the demerits of limited distance or geographical limit to be established to an internet link. In this paper, we propose the SG-Robot(Security Guard Robot) that can be operated and conduct surveillance of the environment around itself anytime/anywhere using CDMA networking. SG-Robot was able to solve those problems, conduct the surveillance task and communicate between multi SG-Robot and users over the CDMA2000-1x communication network efficiently.

Progression of a mobile robot and communication techniques enables intelligent robot to be developed more versatilely. Through the ubiquitous robot control, paradigm of robot which performs specific tasks remotely has changing into intelligent robot to perform public and individual tasks. Especially, in the robot and security industry, security guard robot provides a variety of information to user and achieves its duty through the Web interface. The communication technique for these telepresence robot takes charge of the probability of various service in the robot industry. Last year, the interface for telepresence robot has developed over the Web or RF between robots, but these system has the demerits of limited distance or geographical limit to be established Internet link. In this paper, we propose the SG-robot (security guard robot) that can be operated and conduct surveillance the environment around itself anytime/anywhere using CDMA networking. SG-robot was able to solve those problems, conduct the surveillance task and communicate with between multi SG-robot and users over the cdma2000-1x communication network efficiently.

In this paper, we propose an architecture of walking assistant robot for the elderly which is usable at outdoor environment. It is equipped with a PXA255 embedded board, motors, a laser range finder, a CCD camera and a GPS receiver. A user operates it using haptic handle bar. It is useful to the elderly for more comfortable walking and path guidance.