Remarks on multimodal nonverbal interface and its application to controlling mobile robot
This paper proposes a multimodal nonverbal interface system using bio-potential signals, such as EOG and EMG, and image processing and investigates its possibility of applying to control a mobile robot. To evaluate the feasibility and the characteristics of the multimodal nonverbal interface system, teleoperation control experiments of the robot are carried out. By comparing the experimental results with those obtained using a conventional keyboard interface, the effectiveness of the proposed interface system is confirmed.
A Supplementary System for a Brain-Machine Interface Based on Jaw Artifacts for the Bidimensional Control of a Robotic Arm
Non-invasive Brain-Machine Interfaces (BMIs) are being used more and more these days to design systems focused on helping people with motor disabilities. Spontaneous BMIs translate user's brain signals into commands to control devices. On these systems, by and large, 2 different mental tasks can be detected with enough accuracy. However, a large training time is required and the system needs to be adjusted on each session. This paper presents a supplementary system that employs BMI sensors, allowing the use of 2 systems (the BMI system and the supplementary system) with the same data acquisition device. This supplementary system is designed to control a robotic arm in two dimensions using electromyographical (EMG) signals extracted from the electroencephalographical (EEG) recordings. These signals are voluntarily produced by users clenching their jaws. EEG signals (with EMG contributions) were registered and analyzed to obtain the electrodes and the range of frequencies which provide the best classification results for 5 different clenching tasks. A training stage, based on the 2-dimensional control of a cursor, was designed and used by the volunteers to get used to this control. Afterwards, the control was extrapolated to a robotic arm in a 2-dimensional workspace. Although the training performed by volunteers requires 70 minutes, the final results suggest that in a shorter period of time (45 min), users should be able to control the robotic arm in 2 dimensions with their jaws. The designed system is compared with a similar 2-dimensional system based on spontaneous BMIs, and our system shows faster and more accurate performance. This is due to the nature of the control signals. Brain potentials are much more difficult to control than the electromyographical signals produced by jaw clenches. Additionally, the presented system also shows an improvement in the results compared with an electrooculographic system in a similar environment.
The Analysis on Fire Smoke Exhausting Robot Vibration Based on Wavelet Transform
Belt relaxation, which affects a lot on operation reliability of the fire smoke exhausting robot, is hard to detect. In this paper, a new method for belt relaxation feature recognition based on continuous wavelet transform (CWT) is proposed, and a vibration model which simplified the robot as a 2-dof forced vibration system under harmonic excitation is established. The vibration acceleration signal has been collected using the IEPE sensor and data acquisition card, experimental results verified the accuracy of the vibration model, and weak impact signal caused by the belt relaxation was distinguished, that testify the practicability of the CWT method in belt relaxation feature recognition.