An alternative human interface enabling the handicapped with severe motor disabilities to control an assistive system is presented. Since this interface relies on the biosignals originating from the contraction of muscles on the face during particular movements, even individuals with a paralyzed limb can use it with ease. For real-world application, a dedicated hardware module employing a general-purpose DSP was implemented and its validity tested on an electrically powered wheelchair. Furthermore, an additional attempt to reduce error rates to a minimum for stable operation was also made based on the entropy information inherent in the signals during the classification phase. In the experiments in which 11 subjects participated, it was found most of them could control the target system at their own will, and thus the proposed interface could be considered a potential alternative for the interaction of the severely handicapped with electronic systems.

In this study, we achieved predictable control of a wheelchair by changing the existing mapping method of the joystick, which considers the consecutive operations of a motor of a wheelchair, to a new mapping method that corresponds to the internal model of a human being. Since the existing method uses the polar coordinate system, it is not easy at all to use this method to predict either the direction of motion or the operating order for changing the position of the wheelchair according to the requirements of an operator. In order to improve the embodiment, we divided the existing joystick mapping method into two degrees of freedom-one in the vertical axis that can control the velocity and the other, in the horizontal axis for direction control. Based on this division, we implemented a wheelchair model that can be controlled by the electromyography (EMG) signal from the neck and the arm muscles of an operator. This was achieved by mapping the divided degrees of freedom onto the degrees of freedom of the neck and arm of the operator. In this case, since the operator controls the direction of motion by the joint of his/her neck, he/she can move the wheelchair in the desired direction; thus, a more intuitive human interface is implemented.

To use the voluntary electromyogram (EMG) as a control signal of the EMG controlled functional electrical stimulator (FES), it is required to reduce the stimulation artifact and non-voluntary contribution (M-wave). In this study, a Gram-Schmidt (GS) prediction error filter (PEF) that can effectively eliminates the M-wave from voluntary EMG is presented. Also, the presented GS PEF is implemented on the field the programmable gate array (FPGA) for real-time processing and the performance is tested with simulated and real signals. Experimental results showed that GS-PEF was effective in reducing M-wave and preserving voluntary EMG.

This study was designed to evaluate localized muscular fatigue induced during mouse operation in a VDT task. Ten male undergraduates from 19 to 23 years old participated in the experiment. The subject performed a pointing task with a PC mouse for about 4 hours. The EMG measurements and psychological rating of fatigue were conducted before the experimental task and after each 30-minutes block during the experimental task. The changes in the Mean Power Frequency (MPF) and Percentage Maximum Voluntary Contraction (%MVC)-shift for the constant cumulative probability in the Amplitude Probability Distribution Function (APDF) with time were explored. The correspondence between the index (MPF or APDF) and the subjective rating of localized muscular fatigue was also examined. The performance was nearly constant across all blocks. The psychological rating of fatigue tended to increase with time. The MPF tended to increase with time, although the main effect of block (time) was not statistically significant. The %MVC-shift tended to increase with time. The correspondence with the perceived sensation of localized muscular fatigue was higher when using the %MVC-shift than when using the MPF. Based on the results, the effectiveness of the indexes used for evaluating localized muscular fatigue was discussed. The %MVC-shift obtained from the APDF was found to be a sensitive index of localized muscular fatigue and corresponded well with the subjective rating of localized muscular fatigue.

Most of the current research is focused on the row-column scanning keyboard interface for English letter and number input. At the present time, there are insufficient methods to control the computer mouse effectively. In this study, a categorized row-column scanning computer interface is developed to improve the conventional single key-in row-column scanning method. The beneficial developments include: speed enhancement by categorizing radicals of keyboard, input control of mouse, and multiple selection of input methods such as surface electromyographic (SEMG) control, breath pressure sensibility control with puff, force sensibility control, infrared sensibility control and single key-in control. Meanwhile, an enhancement software package is developed to increase the row-column scanning keyboard capabilities and to upgrade the completeness of the computer mouse for the disabled persons to control the operation of data entry and the associated implementation better.

The aim of this study is to measure and quantify muscle fatigue of low back, caused by sitting on the vehicle seat for a long period of time. The authors proposed a new objective muscle fatigue index based on Principle Component Analysis utilizing the measured muscle viscoelasticity and EMG. The new index suggests an adequate correlation with the subjective fatigue.

A novel method of multichannel surface EMG processing has been developed to compensate for the distortion in bipolar surface EMG signals due to the movement of innervation zones. The distortion of bipolar surface EMG signals was mathematically described as a filtering function. A compensating technique for such distorted bipolar surface EMG signals was developed for the brachial biceps during dynamic contractions in which the muscle length and tension change. The technique is based on multichannel surface EMG measurement, a method for estimating the movement of an innervation zone, and the inverse filtering technique. As a result, the distorted EMG signals were compensated and transformed into nearly identical waveforms, independent of the movement of the innervation zone.

We have aimed at constructing a forward dynamics model (FDM) of the human arm in the form of an artificial neural network while recordings of EMG and movement trajectories. We succeeded in: (1) estimating the joint torques under isometric conditions and (2) estimating trajectories from surface EMG signals in the horizontal plane. The human arm has seven degrees of freedom: the shoulder has three, the elbow has one and the wrist has three. Only two degrees of freedom were considered in the previous work. Moreover, the arm was supported horizontally. So, free movement in 3D space is still a necessity. And for 3D movements or posture control, compensation for gravity has to be considered. In this papre, four joint angles, one at the elbow and three at the shoulder were estimated from surface EMG signals of 12 flexor and extensor muscles during posture control in 3D space.

This paper focuses on two areas in our effort to synthesize speech from neuromotor input using neural network models that effect transforms between cognitive intentions to speak, their physiological effects on vocal tract structures, and subsequent realization as acoustic signals. The first area concerns the biomechanical transform between motor commands to muscles and the ensuing articulator behavior. Using physiological data of muscle EMG (electromyography) and articulator movements during natural English speech utterances, three articulator-specific neural networks learn the forward dynamics that relate motor commands to the muscles and motion of the tongue, jaw, ant lips. Compared to a fully-connected network, mapping muscle EMG and motion for all three sets of articulators at once, this modular approach has improved performance by reducing network complexity and has eliminated some of the confounding influence of functional coupling among articulators. Network independence has also allowed us to identify and assess the effects of technical and empirical limitations on an articulator-by-articulator basis. This is particularly important for modeling the tongue whose complex structure is very difficult to examine empirically. The second area of progress concerns the transform between articulator motion and the speech acoustics. From the articulatory movement trajectories, a second neural network generates PARCOR (partial correlation) coefficients which are then used to synthesize the speech acoustics. In the current implementation, articulator velocities have been added as the inputs to the network. As a result, the model now follows the fast changes of the coefficients for consonants generated by relatively slow articulatory movements during natural English utterances. Although much work still needs to be done, progress in these areas brings us closer to our goal of emulating speech production processes computationally.