Kenbu Teramoto

Development of a 3DOF mobile exoskeleton robot for human upper-limb motion assist

In order to assist physically disabled, injured, and/or elderly persons, we have been developing exoskeleton robots for assisting upper-limb motion, since upper-limb motion is involved in a lot of activities of everyday life. This paper proposes a mechanism and control method of a mobile exoskeleton robot for 3DOF upper-limb motion assist (shoulder vertical and horizontal flexion/extension, and elbow flexion/extension motion assist). The exoskeleton robot is mainly controlled by the skin surface electromyogram (EMG) signals, since EMG signals of muscles directly reflect how the user intends to move. The force vector at the end-effector is taken into account to generate the natural and smooth hand trajectory of the user in the proposed control method. An obstacle avoidance algorithm is applied to prevent accidental collision between the users upper-limb and the robot frame. The experiment was performed to evaluate the effectiveness of the proposed exoskeleton robot.

Towards Hybrid EEG-EMG-Based Control Approaches to be Used in Bio-robotics Applications: Current Status, Challenges and Future Directions

Abstract In the last few decades, bio-robotics applications such as exoskeletons, prosthetics and robotic wheelchairs have progressed from machines in science fiction to nearly commercialized products. Though there are still several challenges associated with electromyography (EMG) signals, the advances in use of EMG signals for controlling such bio-robotics applications have been enormous. Similarly, recent trends and attempts in developing electroencephalography- (EEG) based control methods have shown the potential of this area in the modern bio-robotics field. However, the EEG-based control methods are also yet to be perfected. A new approach of combining both these control methods, which take the advantages, and diminish the disadvantages, of each system might therefore be a promising approach. In this paper, we review hybrid fusion of EMG- and EEG-based control approaches in the bio-robotics field which have been attempted or developed to date. We provide a design overview of the method and consider the main features and merits/disadvantagages for the approaches that have been analyzed. We also discuss the current challenges regarding these hybrid EEG-EMG control approaches and propose some potential future directions.

A study on effects of muscle fatigue on EMG-based control for human upper-limb power-assist

It may be difficult task for physically weak elderly, disabled and injured individuals to perform the day to day activities in their life. Therefore, many assistive devices have been developed in order to improve the quality of life of those people. Especially upper-limb power-assist exoskeletons have been developed since the upper limb motions are vital for the daily activities. Electromyography (EMG) signals of the upper limb muscles have sometimes been used as a primary signal to control the power assist exoskeletons since the EMG signals directly reflect the motion intention of the user. But one of the main obstacles for EMG based controller is the muscle fatigue, because the muscle fatigue might change the EMG patterns. It is important for power-assist exoskeleton to correctly assist the user for longer period of time. But it has high probability of user muscles been fatigued because users getting more and more exhausted at the end of the day. Therefore it is necessary to consider the variations of EMG signals due to the effect of muscle fatigue. In this paper it demonstrates the study which was conducted to find out the effects of muscle fatigue on the three EMG features derived from the raw EMG signals of the Bicep brachii, Deltoid-posterior, Deltoid-anterior and Supinator muscles of the upper limb. Shoulder vertical flexion/extension, shoulder abduction/adduction, elbow flexion/extension and forearm pronation/supination motions were carried out before and after a set of muscle fatiguing exercises. The three features computed in this experiment were RMS (Root Mean Square), MPF (Mean Power Frequency) and a spectral feature (FInsm5) which was proposed by Dimitrov. Comparison results of these three features of all muscles before and after the fatiguing exercises showed an percentage increase of the RMS and FInsm5 features whereas MPF showed a percentage decrease with respect to the before fatiguing conditions. The result showed that the EMG RMS may not a reliable feature to use as the only input signal in EMG based control for human upper-limb power assist in the muscle fatiguing conditions. Therefore, it is suggested that a modification method for compensating the effect of muscle fatigue is required on the EMG based control in order to have a long and reliable use of the human upper-limb power assist exoskeletons.

Index of homogeneity for a criterion judging existence of cracks

This paper presents a crack characterization method by spatio-temporal gradient analysis over the Lamb wave field. The guided ultrasound waves that propagate in the direction of the layer are a potential candidate of non-destructive testing (NDT) methodology. The proposed method has an ability to classify the surface acoustic wave field through the rank of the covariance matrix defined over the 4-dimensional vector space which is spanned by following components: a vertical displacement, its vertical velocity, and a pair of shear strains of the surface. The covariance matrix provides the information about cracks. This study, therefore, proposes an index of homogeneity of the object surface based on the determinant of the covariance matrix.

Evaluation of perception-assist with an upper-limb power-assist exoskeleton using EMG and EEG signals

The concept of perception assist has been proposed to assist not only the daily motion but also to safely interact with an environment, by applying a modification force to the users motion in upper-limb power-assist exoskeleton robots. As it is complicated for the exoskeleton to prepare all proper perception-assist for every task, tool, and environment, the exoskeleton robots needs to learn the proper perception-assist for each task, tool and environment by itself. In this process, the evaluation of the performed perception assist is important, because not only can the results be used in the learning process of the perception assist but also it would help to avoid mistaken perception assist commands if there are any. One way of evaluating the performed perception-assist of the exoskeleton is using Electromyography (EMG) signals. However, if the EMG signals do not change adequately for a judgment, the learning of the robot might not succeed. In this context, this paper presents an attempt to use both Electroencephalography (EEG) and EMG signals to evaluate the performed perception assist in the upper-limb power-assist exoskeleton robots. The effectiveness of the proposed method is experimentally evaluated.

Compensation of the effects of muscle fatigue on EMG-based control using fuzzy rules based scheme

Estimation of the correct motion intention of the user is very important for most of the Electromyography (EMG) based control applications such as prosthetics, power-assist exoskeletons, rehabilitation and teleoperation robots. On the other hand, safety and long term reliability are also vital for those applications, as they interact with human users. By considering these requirements, many EMG-based control applications have been proposed and developed. However, there are still many challenges to be addressed in the case of EMG based control systems. One of the challenges that had not been considered in such EMG-based control in common is the muscle fatigue. The muscle fatiguing effects of the user can deteriorate the effectiveness of the EMG-based control in the long run, which makes the EMG-based control to produce less accurate results. Therefore, in this study we attempted to develop a fuzzy rule based scheme to compensate the effects of muscle fatigues on EMG based control. Fuzzy rule based weights have been estimated based on time and frequency domain features of the EMG signals. Eventually, these weights have been used to modify the controller output according with the muscle fatigue condition in the muscles. The effectiveness of the proposed method has been evaluated by experiments.

Acoustical tactile sensor utilizing multiple reflections for principal curvature measurement

It is essential for robotic tactile sensors to provide capability of shape discrimination from static touch. For this purpose, the principal curvatures and corresponding principal directions play the important roles which defines the local shape of the object. This paper proposes a novel acoustic tactile sensing system which has an ability to identify the principal curvatures of the object surface by utilizing reflected acoustical wavefronts. Any smooth surface can be locally approximated by a set of independent parameters: the location of the point of contact, the normal vector of the tangent plane, the principal curvatures and the corresponding principal directions. The major difficulty, however, existing in estimating these parameters is that the wavefront reflected by the paraboloidal surface cannot be described in the linear combination of the plane-waves nor the spherical-waves strictly such that the nonlinearity exists between TOF and the parameters defining the surface. Avoiding the difficulty, the proposed sensing system utilizes TOFs for both single and double reflections.

Blind signal separation with undersampled signals for frequency modulated echoes

Ultrasound technology in medical examinations has developed further in recent years. Above all, the system monitoring the fetal well-being with ultrasonic echoes becomes most popular in obstetrics. However, it retains several problem that the observed echoes may include the information of the maternal body, and acoustic transfer functions are affected by the fetal movement. This paper proposes a novel monitoring system utilizing blind signal separation (BSS), which is a method for recovering a set of source signals from their mixtures without any knowledge about the mixing process.

EEG-based evaluation for perception-assist in upper-limb power-assist exoskeletons

In this paper, we report an attempt to utilize Electroencephalography (EEG) signals for judging the correctness of the performed perception-assist in an upper-limb power-assist exoskeleton. Although Electromyography (EMG) signals can be used for judgments, lack of change in EMG signals and the complexity of the upper-limb motions sometimes make it difficult to evaluate the perception-assist using EMG signals. In this study, we investigate whether EEG signals can alone be used instead of EMG signals to judge the correctness of the perception-assist performed by the exoskeleton. Experiments are carried out with three healthy subjects and the results are presented in this paper. Moreover, we show the potentials and advantages of using EEG signals recorded from brain of the users to judge correctness of the perception-assist in upper-limb power-assist exoskeletons.

Toward EEG control of upper limb power-assist exoskeletons: A preliminary study of decoding elbow joint velocities using EEG signals

It may not an easy task for physically weak elderly, disabled and injured individuals to perform the day to day activities in their life. Therefore, many assistive devices have been developed in order to improve the quality of life of those people in which they may not depend on others. Especially upper-limb power-assist exoskeletons have been developed since the upper limb motions are very important for the daily activities. Electromyography (EMG) signals and/or force sensor based control methods have been identified as the promising methods to control such exoskeleton devices. However, if the user cannot generate sufficient muscle signals or movements, the EMG or force sensor based methods could not be useful to the user. On the other hand, electroencephalography (EEG) signals are also important biological signals to extract the users motion intention. In this study, the users elbow joint motion is estimated based on the EEG signals. The measured EEG signals are pre-processed and input to a time-embedded linear model, which is assumed to decode the elbow joint angular velocities. The genetic algorithm (GA) is used to train the model. A six fold cross validation process was performed for each motion segment of each subject. The experimental results suggest that EEG signals with the tested decoding model can be used to continuously decode the elbow joint velocity.