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.

Unsupervised learning with a physics-based autoencoder for estimating the thickness and mixing ratio of pigments.

Layered surface objects represented by decorated tomb murals and watercolors are in danger of deterioration and damage. To address these dangers, it is necessary to analyze the pigments' thickness and mixing ratio and record the current status. This paper proposes an unsupervised autoencoder model for thickness and mixing ratio estimation. The input of our autoencoder is spectral data of layered surface objects. Our autoencoder is unique, to our knowledge, in that the decoder part uses a physical model, the Kubelka-Munk model. Since we use the Kubelka-Munk model for the decoder, latent variables in the middle layer can be interpretable as the pigment thickness and mixing ratio. We conducted a quantitative evaluation using synthetic data and confirmed that our autoencoder provides a highly accurate estimation. We measured an object with layered surface pigments for qualitative evaluation and confirmed that our method is valid in an actual environment. We also present the superiority of our unsupervised autoencoder over supervised learning.

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.

An adaptive and stable method for fitting implicit polynomial curves and surfaces.

Representing 2D and 3D data sets with implicit polynomials (IPs) has been attractive because of its applicability to various computer vision issues. Therefore, many IP fitting methods have already been proposed. However, the existing fitting methods can be and need to be improved with respect to computational cost for deciding on the appropriate degree of the IP representation and to fitting accuracy, while still maintaining the stability of the fit. We propose a stable method for accurate fitting that automatically determines the moderate degree required. Our method increases the degree of IP until a satisfactory fitting result is obtained. The incrementability of QR decomposition with Gram-Schmidt orthogonalization gives our method computational efficiency. Furthermore, since the decomposition detects the instability element precisely, our method can selectively apply ridge regression-based constraints to that element only. As a result, our method achieves computational stability while maintaining fitting accuracy. Experimental results demonstrate the effectiveness of our method compared with prior methods.

Color constancy from blackbody illumination.

We present a theoretical analysis of what we believe to be a new color constancy method that inputs two chromaticities of an identical surface taken under two blackbody illuminations. By using the Planck formula for modeling spectra of outdoor illumination and by assuming that a narrowband camera sensitivity function is sufficiently narrow, surface colors can be estimated mathematically. Experiments with simulation and real data have been conducted to evaluate the effectiveness of the method. The results showed that although this method is a perfect vehicle for simulation data, it produces significant errors with real data. A thorough investigation of the cause of errors indicates how important the assumptions on both blackbody illuminations and narrowband camera sensitivities are to the method. Finally, we discuss the robustness of our method and the limitation of solving color constancy using the illumination constraint.

[Quantitative assessment of pressure relief at the sacral area in adults lying supine on the operating room table].

BACKGROUND: It is important to prevent development of the pressure ulcers in patients undergoing lengthy surgery, particularly at areas of skin overlying bony prominences. This study was designed to investigate distribution of the interface pressure (IP) over the body area (from the head to pelvic area) in supine adults and also evaluate the ability of a polyurethane-made cushion to reduce the IP at their sacral area. METHODS: Utilizing a recently developed device to measure the IP (ERGO-CHECK, ABW Co., Germany), we evaluated distribution of the IP (estimated per 3 x 4 cm2 area) over the body area in healthy volunteers (n=31) and patients under general anesthesia (n=6) lying supine on the operating room (OR) table. RESULTS: In all the subjects, the highest IP was generated at the sacrum; 62.5 +/- 23.8 (mean +/- SD) and 35.7 +/- 5.5 mmHg in the volunteers and patients, respectively. The polyurethane-made, "doughnut" cushion (5 cm in thickness) inserted between the pelvic area and the OR table significantly reduced (P < 0.05) the IP at the sacrum in both groups: the IPs after the insertion in the volunteers and patients were 35.1 +/- 11.1 and 25.6 +/- 6.5 mmHg, respectively. In addition, the insertion significantly reduced (P < 0.05) the high-risk area (i.e., area of IP > 32 mmHg) in both groups. CONCLUSIONS: Quantitative assessment of the IP would be useful in evaluating precisely the effectiveness of various types of pillows, cushions, or mattresses designed to reduce the IP.

[Usefulness of monitoring forehead deep-tissue temperature as an index of core temperature in adult patients undergoing laparotomies under general anesthesia--investigation in operating rooms with air-movement control system using vertical flow].

BACKGROUND: Acute changes in air temperature in the vicinity of the patents' forehead may impair clinical usefulness of the forehead deep-tissue thermometry. We thus investigated usefulness of monitoring the forehead deep-tissue temperature as an index of core temperature in 12 adult patients undergoing laparotomies in operating rooms with air-movement control system using vertical flow. METHODS: Nasopharyngeal, forehead deep-tissue, palm deep-tissue, and fingertip skin-surface temperatures were recorded during surgery every 5 minutes in operating rooms where room temperature was thermostatically controlled at approximately 25 degrees C. The patients were not actively warmed with forced-air warmers, but covered with cotton blankets where possible. The deep-tissue and fingertip skin-surface temperatures were compared with the nasopharyngeal temperature using regression and Bland and Altman's analyses. RESULTS: The four temperatures continued decreasing during surgery, and the nasopharyngeal temperature decreased to below 36 degrees C 2 hours after induction of anesthesia. Only the forehead deep-tissue temperature satisfactorily correlated with the nasopharyngeal temperature (r = 0.76, n = 300, P < 0.0001). The difference between nasopharyngeal and forehead temperatures was +0.26 degree C, and its standard deviation was 0.34 degree C. CONCLUSIONS: The forehead deep-tissue temperature has sufficient accuracy and precision for clinical use in operating rooms with air-movement control system using vertical flow. However, the core temperature appears to be slightly underestimated with the forehead deep-tissue thermometry.