Pleasant Stroke Touch on Human Back by a Human and a Robot.

Pleasant touching is an important aspect of social interactions that is widely used as a caregiving technique. To address the problems resulting from a lack of available human caregivers, previous research has attempted to develop robots that can perform this kind of pleasant touch. However, it remains unclear whether robots can provide such a pleasant touch in a manner similar to humans. To investigate this issue, we compared the effect of the speed of gentle strokes on the back between human and robot agents on the emotional responses of human participants (n = 28). A robot or a human stroked on the participants' back at two different speeds (i.e., 2.6 and 8.5 cm/s). The participants' subjective (valence and arousal ratings) and physiological (facial electromyography (EMG) recorded from the corrugator supercilii and zygomatic major muscles and skin conductance response) emotional reactions were measured. The subjective ratings demonstrated that the speed of 8.5 cm/s was more pleasant and arousing than the speed of 2.6 cm/s for both human and robot strokes. The corrugator supercilii EMG showed that the speed of 8.5 cm/s resulted in reduced activity in response to both human and robot strokes. These results demonstrate similar speed-dependent modulations of stroke on subjective and physiological positive emotional responses across human and robot agents and suggest that robots can provide a pleasant touch similar to that of humans.

Delivery of pleasant stroke touch via robot in older adults.

Touch care has clinically positive effects on older adults. Touch can be delivered using robots, addressing the lack of caregivers. A recent study of younger participants showed that stroke touch delivered via robot produced subjective and physiologically positive emotional responses similar to those evoked by human touch. However, whether robotic touch can elicit similar responses in older adults remains unknown. We investigated this topic by assessing subjective rating (valence and arousal) and physiological signals [corrugator and zygomatic electromyography (EMG) and skin conductance response (SCR)] to gentle stroking motions delivered to the backs of older participants by robot and human agents at two different speeds: 2.6 and 8.5 cm/s. Following the recent study, the participants were informed that only the robot strokes them. We compared the difference between the younger (their data from the previous study) and the older participants in their responses when the two agents (a robot and a human) stroked them. Subjectively, data from both younger and older participants showed that 8.5 cm/s stroking was more positive and arousing than 2.6 cm/s stroking for both human and robot agents. Physiologically, data from both younger and older participants showed that 8.5 cm/s stroking induced weaker corrugator EMG activity and stronger SCR activity than the 2.6 cm/s stroking for both agents. These results demonstrate that the overall patterns of the older groups responses were similar to those of the younger group, and suggest that robot-delivered stroke touch can elicit pleasant emotional responses in older adults.

Analysis of Skin Deformation Differences on the Upper Arm Between Active and Passive Movements During Elbow Flexion and Extension.

The motion ability of patients in the acute phase of stroke is difficult to define with existing indexes such as the Brunnstrom stage. Hence, for designing a novel evaluation index for stroke rehabilitation in the acute phase, we focused on the differences between the skin deformations in active and passive movements. Skin deformation reflects the activities of body tissues that are related to motion ability. We measured skin deformations on the upper arm in active and passive movements during elbow flexion and extension and extracted features from these deformations. For practical rehabilitation applications, we developed a novel flexible distance sensor array to reduce the time needed for attaching sensors to patients. Using principal component analysis (PCA), the skin deformation could be decomposed into joint movements and activeness of movements as the first two components (PC1 and PC2). The joint angle and PC1 exhibited a high correlation, and the standard deviation (SD) of PC2 indicated a significant difference in the types of movements. From the above results, we concluded that the SD ratio between PC2 and PC1 may be used to evaluate motion ability considering the inherent biomechanical characteristics.

A Quantitative Measurement of Hand Scaling Motion for Dental Hygienist Training.

In dental hygienist education, many skills are taught that cannot be acquired without repeated training. To make this training more efficient, we need to measure the students' skills and show correction points in real-time. In this research, we focus on hand scaling work, which is one of the most important tasks of dental hygienists. We developed a measurement system to measure both the motion and force exerted during hand scaling work. This measured data can be used to quantitatively evaluate students' skills. In the experiment, we measured the hand scaling motion of several participants with different levels of job experience, including dental hygienist teachers, dental hygienists, and dental hygienist students. We showed that it is possible to extract from the measured results a quantitative index for discriminating different individual skills.

Prediction of Plantar Forces During Gait Using Wearable Sensors and Deep Neural Networks.

To enable on-time and high-fidelity lower-limb exoskeleton control, it is effective to predict the future human motion from the observed status. In this research, we propose a novel method to predict future plantar force during the gait using IMU and plantar sensors. Deep neural networks (DNN) are used to learn the non-linear relationship between the measured sensor data and the future plantar force data. Using the trained network, we can predict the plantar force not only during walking but also at the start and end of walking. In the experiments, the performance of the proposed method is confirmed for different prediction time.

Estimation of the Operating Force From the Human Motion.

In this research, we propose a new method to estimate the operating force exerted when moving an object with the hands by observing the human motion only. The required force for moving the object can be estimated without equipping any sensors on the object or on the human body. From the measured joint angles, we calculate the center of mass and the hand's position using a human model. Then, the operating forces of the hands can be estimated by considering the balance of forces and moments using a dynamic model. In the experiments, we estimated the operating force of the hands for three representative movements of operating a box: pushing, pulling and lifting. By comparing the estimated and measured operating force, we verified the effectiveness of the proposed method with human subjects.

Hand motion recognition based on forearm deformation measured with a distance sensor array.

Studies of upper limb motion analysis using surface electromyogram (sEMG) signals measured from the forearm plays an important role in various applications, such as human interfaces for controlling robotic exoskeletons, prosthetic hands, and evaluation of body functions. Though the sEMG signals have a lot of information about the activities of the muscles, the signals do not have the activities of the deep layer muscles. We focused on forearm deformation, since hand motion brings the muscles, tendons, and skeletons under the skin. The reason why we focus is that we believe the forearm deformation delivers information about the activities of deep layer muscles. In this paper, we propose a hand motion recognition method based on the forearm deformation measured with a distance sensor array. The method uses the support vector machine. Our method achieved a mean accuracy of 92.6% for seven hand motions. Because the accuracy of the pronation and the supination are high, the distance sensor array has the potential to estimate the activities of deep layer muscles.

Rehand: Realistic electric prosthetic hand created with a 3D printer.

Myoelectric prosthetic hands provide an appearance with five fingers and a grasping function to forearm amputees. However, they have problems in weight, appearance, and cost. This paper reports on the Rehand, a realistic electric prosthetic hand created with a 3D printer. It provides a realistic appearance that is same as the cosmetic prosthetic hand and a grasping function. A simple link mechanism with one linear actuator for grasping and 3D printed parts achieve low cost, light weight, and ease of maintenance. An operating system based on a distance sensor provides a natural operability equivalent to the myoelectric control system. A supporter socket allows them to wear the prosthetic hand easily. An evaluation using the Southampton Hand Assessment Procedure (SHAP) demonstrated that an amputee was able to operate various objects and do everyday activities with the Rehand.

Individual muscle control using an exoskeleton robot for muscle function testing.

Healthy individuals modulate muscle activation patterns according to their intended movement and external environment. Persons with neurological disorders (e.g., stroke and spinal cord injury), however, have problems in movement control due primarily to their inability to modulate their muscle activation pattern in an appropriate manner. A functionality test at the level of individual muscles that investigates the activity of a muscle of interest on various motor tasks may enable muscle-level force grading. To date there is no extant work that focuses on the application of exoskeleton robots to induce specific muscle activation in a systematic manner. This paper proposes a new method, named "individual muscle-force control" using a wearable robot (an exoskeleton robot, or a power-assisting device) to obtain a wider variety of muscle activity data than standard motor tasks, e.g., pushing a handle by hand. A computational algorithm systematically computes control commands to a wearable robot so that a desired muscle activation pattern for target muscle forces is induced. It also computes an adequate amount and direction of a force that a subject needs to exert against a handle by his/her hand. This individual muscle control method enables users (e.g., therapists) to efficiently conduct neuromuscular function tests on target muscles by arbitrarily inducing muscle activation patterns. This paper presents a basic concept, mathematical formulation, and solution of the individual muscle-force control and its implementation to a muscle control system with an exoskeleton-type robot for upper extremity. Simulation and experimental results in healthy individuals justify the use of an exoskeleton robot for future muscle function testing in terms of the variety of muscle activity data.

Effects of S-adenosylmethionine decarboxylase, polyamines, amino acids, and weak bases (amines and ammonia) on development and ribosomal RNA synthesis in Xenopus embryos.

We have been studying control mechanisms of gene expression in early embryogenesis in a South African clawed toad Xenopus laevis, especially during the period of midblastula transition (MBT), or the transition from the phase of active cell division (cleavage stage) to the phase of extensive morphogenesis (post-blastular stages). We first found that ribosomal RNA synthesis is initiated shortly after MBT in Xenopus embryos and those weak bases, such as amines and ammonium ion, selectively inhibit the initiation and subsequent activation of rRNA synthesis. We then found that rapidly labeled heterogeneous mRNA-like RNA is synthesized in embryos at pre-MBT stage. We then performed cloning and expression studies of several genes, such as those for activin receptors, follistatin and aldolases, and then reached the studies of S-adenosylmethionine decarboxylase (SAMDC), a key enzyme in polyamine metabolism. Here, we cloned a Xenopus SAMDC cDNA and performed experiments to overexpress the in vitro-synthesized SAMDC mRNA in Xenopus early embryos, and found that the maternally preset program of apoptosis occurs in cleavage stage embryos, which is executed when embryos reach the stage of MBT. In the present article, we first summarize results on SAMDC and the maternal program of apoptosis, and then describe our studies on small-molecular-weight substances like polyamines, amino acids, and amines in Xenopus embryos. Finally, we summarize our studies on weak bases, especially on ammonium ion, as the specific inhibitor of ribosomal RNA synthesis in Xenopus embryonic cells.

Biomechanical analysis of subjective pinching effort based on tendon-skeletal model.

In this paper, the influence of the finger posture on the subjective effort during pinching motion is investigated by using a tendon-skeletal finger model. The experimental results show that the subjective effort human feels is affected by the size of the object he/she pinches, and the subjective effort correlates with the finger length. The simulation results show that the pattern of the tendon forces is similar to that of the EMG activity measured in the experiment, and the positive correlation was observed between the finger length and the object size where the summation of the tendon forces becomes the minimum. These results suggest that the reason why subjective pinching effort is influenced by the finger posture is the difference in the efficiency of the force transmission from the muscles.

Estimation of physical constraint condition by analyzing walking motions.

In this paper, the estimation system of physical constraint conditions is proposed using motion data during walks. Walking motions of subjects who are fixed braces on their bodies were measured by a motion capturing system. We focused on the acceleration data on the body and found which parts in the body are influenced by the physical constraint. The estimation of the physical constraint condition was done by Hidden Markov Model (HMM) based on the reliability map created from the recognition rate. Experimental result shows that we can efficiently estimate the constraint condition by using data of the particular body parts.