Yuya Matsumoto

Development of robotic upper limb orthosis with tremor suppressiblity and elbow joint movability

Essential Tremor (ET) refers to involuntary movements of a part of the body. ET patients have serious difficulties in performing daily living activities. We have been developing a myoelectric controlled exoskeletal robot to suppress tremor. However, the EMG signal of ET patients contains not only signals from voluntary movements but also noise from involuntary tremors. Then, we have developed a signal processing method to suppress tremor noise present in the surface EMG signal. The proposed filter is based on the hypothesis that tremor noise could be approximate to powered sine wave. In this paper, we have integrated tremor canceling filter and neural network (NN) to recognize the tremor patients movement. According to the result, it was confirmed that the proposal filter increased accuracy of recognition, especially stable phase on elbow flexed position as “OFF”.

Analysis of EMG signals of patients with essential tremor focusing on the change of tremor frequency

Essential tremor is a disorder that causes involuntary oscillations in patients while engaging in actions or while maintaining a posture. ET patients have serious difficulties in performing activities of daily living such as eating food, drinking water, and writing. We have thus been developing an EMG-controlled exoskeletal robot to suppress tremors. The EMG signal of ET patients involves a mix of voluntary movement and tremor signals. To control the exoskeletal robot accurately, tremor signals must be removed from the patients EMG signal. To date, we have been developing a filter to remove tremor signals from the patients EMG. The design of this filter was based on the hypothesis that the rectified tremor signals are able to be approximated by a powered sine wave. This filter was found to have a large effect on removing tremor signals. However, tremor signals are generated both while performing voluntary movement and while maintaining a posture, and the filter was attenuating both signals. To control this robot accurately, the signal generated while performing voluntary movement is expected not to be attenuated. To accomplish this, we try to use a parameter that reflects a state of the patients movement, performing a voluntary movement or maintaining a posture, as a switch to activate the powered sine filter. This paper provides an analysis of the favorable parameters. We focus on two parameters: the peak-to-peak interval of the rectified EMG signal, and the interval of the flat and low amplitude area of the rectified EMG signal. Through evaluation, it is affirmed that both parameters change with the state of the patients movement. However, the latter parameter is superior to the former in terms of variability, which indicates that the interval of the flat and low amplitude area of the rectified EMG signal is a more favorable parameter to promote control of the exoskeletal robot. As a future work, we will mount the parameter to the algorithm and evaluate the robotic system.

The weight load inconsistency effect on voluntary movement recognition of essential tremor patient

Essential Tremor (ET) refers to involuntary movements of a part of the body. ET patients have serious difficulties in performing their daily living activities. Our ultimate goal is to develop a system that can enable ET patients to perform their daily living activities. We are in the process of developing an exoskeletal robot for ET patients. This robot is controlled by estimation of voluntary movement using surface electromyogram (EMG) signal input and a Neural Network (NN) learning algorithm. However, the EMG signal of ET patients contains not only signals from voluntary movements but also noise from involuntary tremors. We have therefore developed a signal processing method to suppress tremor noise present in the surface EMG signal. The proposed filter is based on the hypothesis that tremor noise can be approximated to powered sine wave. It have been confirmed that the proposed filter increases the accuracy of recognition. In this paper, we have focused on the effect of inconsistency of weight load between instruction signal and input signal. When the instruction signal comprised unloaded motion, our voluntary movement estimation method worked stably with the loaded motions EMG input.

Estimating a joint angle by means of muscle bulge movement along longitudinal direction of the forearm

Bio-signal processing is a major topic in the field of robotics. Especially, prostheses have been studied for a long time to detect amputees intentions from bio-signals because they do not have their limbs. However, controlling prostheses using bio-signals such as from the brain and surface electromyograms (sEMG), is complex and nonlinear because these signals are noisy and varied. It is not easy to determine the extent of motion such as a joint angle, and previous research used complex models or machine learning based on bio-signals. To estimate a joint angle easily and accurately, we propose a new bio-signal derived from the muscle bulge movements measured on the skin. We hypothesized a simple relationship between the muscle bulge movement longitudinally along a muscle and the corresponding joint angle, because the muscle contraction causes the change in the joint angle. We estimated a wrist joint angle as a function of the muscle bulge movement longitudinally along the extensor carpi radialis longus that is the agonist muscle of wrist extension. From the experimental results, the following three achievements were obtained. First, we extracted a linear relationship from the raw data with a high determination coefficient. Second, we showed that the estimation error using the proposed method was not much different from that of using sEMG in related work. Third, we established that the proposed method could estimate the joint angle robustly for the external loads on a limb.

Development of an elbow-forearm interlock joint mechanism toward an exoskeleton for patients with essential tremor

An essential tremor (ET) is a disorder that causes involuntary oscillations. ET patients face serious difficulties in performing such daily living activities as eating, drinking, and writing. We have been developing an exoskeleton to suppress tremors and support the eating movements of ET patients. The objective of this study is to propose a passive mechanism that prevents the appearance of the compensatory shoulder movement without using an actuator. The basic concept of this study is developing the mechanism to coordinate two DoF movement of elbow joint. Two DoF movement of elbow joint is constrained to passive one DoF by our wearable robot. The mechanism of our robot and constrain mechanism is optimally designed to reduce the compensatory movement during eating movement. To develop such mechanism, we first analysed the eating movement to derive the required specification of the mechanism. Then, we proposed a prototype based on the requirement. Finally, we evaluate the effect of the prototype to reduce the compensatory movement. It is confirmed that the proposed prototype had great effect on the reduction. As a future work, we will optimize the structure and the material of the mechanism to reduce the weight of the mechanism.

Filtering Essential Tremor noise on surface EMG based on squared sine wave approximation

Essential Tremor (ET) refers to involuntary movements of a part of the body. ET patients have serious difficulties in performing daily living activities. Our ultimate goal is to develop a system that can enable ET patients to perform daily living activities. We have been developing an exoskeleton robot for ET patients. We make use of the electromyogram (EMG) signal to control this robot. However, the EMG signal of ET patients contains not only signals from voluntary movements but also noise from involuntary tremors. In this paper, we focus on developing a signal processing method to suppress tremor noise present in the surface EMG signal. The proposed filter detected attenuation ratio by the correlation between the last EMG data and one period squared sine wave. The filtered EMG signals indicated that essential tremor noise of the elbow flexed posture while holding a water-filled bottle was suppressed. In addition, voluntary information was less affected by the filter. Welchs t-value test confirmed that ease of extraction of voluntary movement was increased by the proposed filter.

Wrist joint angle estimation by means of muscle bulge based on deformation of the forearm skin surface

The objective of this paper is to develop a method to estimate the wrist joint angle based on the deformation of the forearm skin surface during muscle contraction. We have focused on the longitudinal movement of the muscle bulge along the forearm. We previously confirmed a one-to-one relationship between the movement of the muscle bulge and the wrist joint angle, and validated the feasibility of the estimation of the joint angle using this relationship. However, the relationship between the movement of the muscle bulge and the wrist joint was previously difficult to perform because of the misalignment of the sensor on the muscle. Here we use a tactile sensor that can measure three-dimensional data on the forearm skin surface to map the muscle bulge location. We measured a large 32 × 96 mm area on the forearm skin with 48 skin distance sensors. We calculated x and y components of the barycentric coordinates from the measured data. We observed a one-to-one relationship between the y-component of the barycentric coordinate from the distribution of the movement of the muscle bulge on the forearm skin surface. We then calculated the RMSE between the measured and estimated wrist joint angle using our joint angle estimation algorithm. We found that the RMSE from our technique was greater than from the conventional method. While we validated the feasibility of our estimation method further research is required to reduce our estimation error by improving the extraction and interpretation of our sensor data.

Ultrathin epidermal strain sensor based on an elastomer nanosheet with an inkjet-printed conductive polymer

To minimize the interference that skin-contact strain sensors cause natural skin deformation, physical conformability to the epidermal structure is critical. Here, we developed an ultrathin strain sensor made from poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) inkjet-printed on a polystyrene–polybutadiene–polystyrene (SBS) nanosheet. The sensor, whose total thickness and gauge factor were ~1 µm and 0.73 ± 0.10, respectively, deeply conformed to the epidermal structure and successfully detected the small skin strain (~2%) while interfering minimally with the natural deformation of the skin. Such an epidermal strain sensor will open a new avenue for precisely detecting the motion of human skin and artificial soft-robotic skin.

Joint angle estimation using the distribution of the muscle bulge on the forearm skin surface of an upper limb amputee

A novel joint angle estimation method is proposed using a new bio-signal for an amputee subject. We used the muscle bulge movement on the forearm skin surface as a new bio-signal for estimating the extent of motion in a previous study. We found that it is feasible to estimate the intended wrist joint angle using the distribution of the muscle bulge for intact subjects. Thus, in the present paper, we validate the feasibility of our method for an amputee. In applying our method to an amputee subject, we improved our distance sensor device so that it can accommodate the position of the muscle, which is variable for an amputee subject. In addition, we improved the algorithm that estimates the wrist joint angle using linear multiple regression for calculating the relationship between the intended wrist joint angle and the distribution of the muscle bulge. As a result, we found that the distribution of the muscle bulge changes for the amputee as for intact subjects. The movement of the position of the muscle bulge on the forearm skin corresponded to the extent of the intended wrist joint angle. According to the result of the estimation of the wrist joint angle, the root-mean-square error of the estimated angle with respect to the measured angle for the amputee was slightly larger than the error for intact subjects. Nevertheless, the root-mean-square error for the amputee was smaller than that when employing the previous method for intact subjects. Finally, it is feasible to use the muscle bulge movement on the forearm skin to estimate the intended wrist joint angle for the upper limb amputee.

Visual and somatic sensory feedback of brain activity for intuitive surgical robot manipulation

This paper presents a method to evaluate the hand-eye coordination of the master-slave surgical robot by measuring the activation of the intraparietal sulcus in users brain activity during controlling virtual manipulation. The objective is to examine the changes in activity of the intraparietal sulcus when the users visual or somatic feedback is passed through or intercepted. The hypothesis is that the intraparietal sulcus activates significantly when both the visual and somatic sense pass feedback, but deactivates when either visual or somatic is intercepted. The brain activity of three subjects was measured by the functional near-infrared spectroscopic-topography brain imaging while they used a hand controller to move a virtual arm of a surgical simulator. The experiment was performed several times with three conditions: (i) the user controlled the virtual arm naturally under both visual and somatic feedback passed, (ii) the user moved with closed eyes under only somatic feedback passed, (iii) the user only gazed at the screen under only visual feedback passed. Brain activity showed significantly better control of the virtual arm naturally (p<;0.05) when compared with moving with closed eyes or only gazing among all participants. In conclusion, the brain can activate according to visual and somatic sensory feedback agreement.