This paper examines whether the robot’s face-turning behavior toward humans during handover can be a behavior cue that supports the human action plan in accomplishing a task. A series of behavior cues observed in interactions, such as Joint action, are understood as social signals, which can be used as cues to help each other make decisions about their own action plans. We hope that constructing a inference model of empathetic behavior will enable humans and robots to share the intention of their behavioral goals in joint action. We conducted temporal analysis and subjective evaluation of the receiver’s behavior in an object handover experiment. The results showed that the subjective experience did not significantly change when the robot turned its head toward the receiver, but the time to receive the participant significantly increased. This suggests that the robot’s face-turning behavior may have altered the receiver’s human prediction and action plan, causing the receiver to hesitate to respond. This shows the potential for the robot face-turning to a human which could change the human inference and response behavior plan.

Live Performances have been conducted online in recent years due to the influence of the COVID-19 pandemic. This generated a novel problem of losing eye contact with the performers, which deteriorates the viewing experience in the online setting. This study proposes a feedback system based on gaze modeling to augment the perceptual experience of being faced by the performer during an online live performance viewing. This system aims to improve the experience of viewing online live performances by recreating a perceptual experience of interaction between the performer and the audience. The proposed system consists of a gaze recognition unit that judges whether the performer is looking at the camera based on gaze modeling in the video, and a stimulus presentation unit that presents the judgement result to the audience through vibration stimulus. This paper describes the gaze recognition model, experiments to determine the parameters to be used in the model, and the evaluation of live performance viewing experiments using the proposed system.

We propose a method for fabricating custom passive finger prostheses with compliant fingertips. The prosthetic devices detailed in this study are intended to mimic the compliance of biological fingertips: improving the capacity and comfort of keyboard typing, the manipulation of small objects, and instrumental activities of daily living in general. The proposed method utilizes 3D scanning, 3D printing, and computer-aided design to realize the compliant fingertips feature and to create custom-fit prostheses. We present the clinical application of the proposed prostheses on two end users and the evaluation results of typing speed and gross finger dexterity. The results showed successful fitting of the developed prostheses on both participants. Performance tests showed comparable performance in typing speed and accuracy with and without prostheses, and improved comfort with the prostheses. Gross finger dexterity and pinch force did not show improvement with the prostheses. The participants were able to utilize all fingers in keyboard typing and other iADLs instead of only the intact fingers, which indicates the potential for further functional improvement after habituation to the prostheses. In addition to improved comfort, the participants also reported reduced pain in sensitive sites, and aesthetic satisfaction with the prostheses.

The amount of force and the timing of force application are necessary training skills in basketball free-throw practice. Coaches cannot readily assess the shooter's motion and provide instructions because the throwing motion is performed in a short time, and because the applied force cannot be directly observed by a second person. In this work we propose a wearable device that measures the wearer's force control during throwing based on electrical impedance and acceleration measurement of the forearm, and provides feedback such that the wearer can adjust their motion appropriately. Temporal features of the electrical impedance and acceleration measured during throwing are determined experimentally to differentiate between experts and beginners. Using these features, we propose a training system for force control using the proposed device and verify its effectiveness in free-throw training.

Wearable robots suffer from issues of usability, cost-performance, and battery life. These drawbacks hinder the development of their market and their implementation in healthcare and industry. Back support exoskeletons in particular rely heavily on mechanical storing of energy but are still mostly using traditional mechanical elements and actuators to achieve the task. The core technology of our work, named MRLink, uses a smart fluid in combination with a compression spring to produce a unique energy store-and-release functionality. With only 5 Watts MRLink can produce over 500N of braking force. The braking force, variable viscosity, can be continuously controlled through the supply current. With appropriate selection of the spring and control method, the required assist energy can be acquired from body weight and movement dynamics, stored momentarily and, released at the appropriate timing to assist the body motion. The MRLink does not require an external power source like pneumatic actuators, does not require gearboxes and large batteries like DC motors, and comes in a compact package. Thus, this technology has the potential to significantly contribute to wearable exoskeletons in general, and back support exoskeletons in particular. In this work we present the development of a back support exoskeleton prototype based on MRLink, we describe its functional details, and a pilot test partially verifying the proposed functions.

Several methods have been investigated and realized for operation of exoskeleton robots for assistance of human gait. These systems perform motion intention estimation using the bioelectrical signals of muscle activation, body gestures and kinesiological information, or a mixed combination in a hybrid system. For motion intention estimation of the lower limb(s), information of the lower limbs is usually utilized. However, human gait is not only the function of the lower limbs, but also coordination between upper and lower limbs, adding to balance and cognitive functions as well. In this study, we investigate on how to utilize the synergies of upper and lower limbs of human walking in exoskeleton robot control by using the cane (walking aid). We analyse the synergies of human gait with cane in healthy subjects by means of Principal Component Analysis (PCA) in order to investigate the usability of cane for robot-assisted motor rehabilitation. We also implement a semi autonomous control for an exoskeleton robot, single leg version of HAL (Hybrid Assistive Limb) suit, based on the cane and body joint synergies.