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Conventional brain-computer interface (BCI) systems can control a robot to execute a certain task according to human intention. However, in the actual application, it is still difficult for users to control multiple robots to execute the cooperative service tasks, including mobility, manipulation and delivery. At the same time, using BCI to control robots for a long time may bring users inevitable fatigue. To solve these dilemmas, we design an SSVEP-based BCI system for a manipulator and a mobile robot to work cooperatively. To reduce the user's fatigue, a ceiling feature based vision assisted navigation module is developed to facilitate the robot moving process. Considering characteristics of electroencephalograph signals, steady-state visual evoked potentials (SSVEP) are employed as input signals for the BCI system, and seven visual stimuli are modulated as control commands. Consequently, this SSVEP-based BCI system can generate four commands to control the movement of Turtlebot mobile robot, one switch command for the visual assisted navigation module, and two commands for the grasp task of UR5 manipulator. Due to the ceiling feature based vision assisted navigation module, the mobile robot can move effectively without human interference, which may reduce the user's fatigue. To evaluate the performance of this system, three subjects were recruited to participate in the experiments. All the subjects succeeded to execute the cooperative control of multiple robots with the vision assisted navigation module. According to the experimental results, the developed BCI system can effectively realize the cooperative control of multiple robots without much fatigue.

Surface electromyogram (sEMG) signals have been used to control multifunctional prosthetic hands. Researchers usually focused on the use of several channels with sEMG signals to identify more hand motions without limiting the number of sEMG sensors. However, the residual muscles of an amputee are limited. Therefore, the point of a successful recognition system is to decrease the channels of sEMG signals to classify more hand motions. To achieve this goal, we proposed a hand motion recognition system, in which three channels of sEMG signals can classify nine hand motions. In this recognition system, the time domain features, root mean square ratio (RMSR) and autoregressive (AR) model, were selected to extract the features of the sEMG signals as compared with the time-frequency domain features. Furthermore, the linear discriminant analysis (LDA) was adopted as the classifier. Consequently, the average accuracy rate of the presented system was 91.46%. Therefore, the proposed algorithms in this paper can be reasonably feasible for prosthetic hands.

The behavior analysis of animal has a key role in biology and robotics. In this paper, we choose the rat as analysis object. For understanding and grasping the behavior of rat, we set up an automatic motion tracking and data analysis system for a rat. This system includes three subsystems: inertial data acquisition, image tracking and real-time data analysis. The inertial subsystem uses one CC2530 development board and one small modular development board which integrates MPU6050 and electrical stimulator, and the inertial data collected by MPU6050 and the controls parameter sent by experimenter communicate by Zig-Bee wireless transmission module to realize the two-way transmission. The image tracking subsystem recognizes and tracks the rat by the camera. Besides, the inertial data and image tracking are time-synchronized. Finally, we program a data analysis interface by Matlab to conduct real-time data analysis. According to the tests, the automatic motion tracking and data analysis system can recognize the rat with a successful rate of 97%, combined with the inertial data. At the same time, we can conduct the real-time data analysis to simulate the attitude of the rat in the interface.

This paper intends to design a system which acquires the trainer's motion and force information in order to manipulate a robot arm applied for rehabilitations. Patients who suffering physical disability also can receive the professorial guiding and cheirapsis even excellent trainers are very busy and insufficient. The key point of this article is data acquisition and reconstruction of the movement of the upper limb by controlling the UR5 robot arm. Upper limb's postures are sensed by Inertial Measurement Unit (IMU) and transferred to STM32 microcontroller using I2C communication protocol. We employed the STM32 microcontroller to calculate attitude angles of both the upper arm and the forearm. And the method with using quaternions to calculate attitude angles is detailedly expounded in this paper. Besides, we employed the MYO armband to acquire upper limb's surface electromyography (sEMG) signals for estimating the muscle force of the upper limb. To verify the feasibility of the proposed system, we make three experiments including analyzing fluctuation range of the attitude angles from IMU signals, classifying muscle force using sEMG signals, and evaluating the effect of motion reconstruction. And the results show that the fluctuation range of acquired data are less than 1 degree, 4 typical motions of upper limb can be reconstructed. The proposed system can be used to reconstruct some upper limb's movement.

The aim of this study is to develop a three-dimensional touch interface for mobile devices, specifically a touch interface for detecting fingertip force. This interface consists of a conventional touch interface and an electromyogram (EMG) amplifier. The fingertip force during manipulation of the touch interface is estimated from the EMG measurement. We develop a method for obtaining fingertip force information using an EMG, while the two-dimensional position of the finger is measured using the conventional touch interface found in mobile devices. Further, we evaluate the validity of our newly developed interface by comparing the fingertip force estimated using our proposed method with the fingertip force measured using a force sensor. Lastly, we develop an application using our interface.

In this study, the effect of the transcranial magnetic stimulation (TMS) on the continuous voluntary movement of the finger was investigated. We measured the trajectory of the thumb tip and electromyogram (EMG) of the hand and forearm muscles after the TMS of the primary motor cortex area (M1) during continuous voluntary rotation of the thumb. The results showed that the velocity of the thumb tip was accelerated right after the TMS followed by the deceleration. It was indicated that enhance and suppression of the muscle activity by the TMS resulted in the change of the finger velocity in the voluntary movement.

We developed a precise coil positioning system for determining stimulated sites in transcranial magnetic stimulation (TMS) by using a multi-articular mechanical arm. A figure-of-eight coil used in TMS can induce a focal electric field beneath the coils center in the cortex; therefore, the point on the cortex beneath the center of the figure-of-eight coil has been conventionally regarded as a stimulating site. However, a stimulating site determined in this manner is not believed to be plausible, because the electric field induced in TMS is dispersed over the brain vectorially. Our system was developed on the basis of the cortical structure of the brain cortex and the direction of the induced electric field. In order to verify the system accuracy, Motor Evoked Potentials (MEPs) elicited by TMS at the hand muscles innervated by the primary motor cortex (M1) were measured as electromyogram (EMG). And functional vectorial map of the excitability of M1 in the resting state was obtained by rotating the stimulation coil at a constant location.

We proposed an artificial neural network to confirm the location of stimulating coil in transcranial magnetic stimulation (TMS) from data of finger force and electromyography (EMG). In experiments, finger forces of the right index finger and motor evoked potentials (MEPs) of the muscles involved in the generation of the finger forces were measured when the primary motor cortex in the left hemisphere of the cerebrum was stimulated by TMS. The measured finger forces and MEPs varied trial by trial although the stimulating coil and subject's head were fixed. The neural network learned the mapping from the MEPs and/or the finger forces to the coil location. After sufficient learning, the neural network was able to classify unlearned MEPs and finger forces into corresponding coil locations.

Electrical stimulation has been reported to have good efficacy in treating urinary incontinence. Recently, magnetic stimulation was also reported to be effective for the treatment of urinary incontinence. The current distributions in the living body induced by these stimulation methods have not yet been shown. Therefore, we attempted to develop computer and experimental models of the female abdomen and to simulate the distribution of induced current based on the tissue conductivity. The present paper describes the developed experimental and computer models of the female abdomen and presents an analysis of the distributions of the respective induced currents. Finally, the authors compared the current distributions in the body produced by the electrical and magnetic stimulation methods in order to determine which method of stimulation is more efficient for the treatment of urinary incontinence.

Wavelet analysis of the finger-tip photo-plethysmogram (PTG) was performed in order to quantify the stress stage. From the result of the wavelet transform of the PTG, we proposed stress index, with which quantification of the mental stress level was easy. In order to confirm the validity of this index, we compared the stress indexes between the stress and resting stages. The relationship between the stress index and the heart rate variability (HRV) was also investigated. The obtained results indicated that using the wavelet transform in analysis showed a remarkable change in the stress index from the resting stage to the stress stage. This change appears in the tidal wave area of the PTG.

Summary form only given. It is reported that magnetic stimulation has good efficacy in treating urinary incontinence. Magnetic stimulation has two main advantages over electrical stimulation: it can be applied without removal of clothes, and it involves little pain. However, the distribution of current in the living body is not clearly known. We therefore attempted to make a computer simulation model of the female abdomen, and to simulate the distribution of induced current on the basis of the tissue conductivity. This report compares electrical with magnetic stimulation, and finally considers which method of stimulation is the more efficient.