Shijie Guo

Development of a nursing-care assistant robot RIBA that can lift a human in its arms

In aging societies, there is a strong demand for robotics to tackle problems caused by the aging population. Patient transfer, such as lifting and moving a bedridden patient from a bed to a wheelchair and back, is one of the most physically challenging tasks in nursing care, the burden of which should be reduced by the introduction of robot technologies. We have developed a new prototype robot named RIBA with human-type arms that is designed to perform heavy physical tasks requiring human contact, and we succeeded in transferring a human from a bed to a wheelchair and back. To use RIBA in changeable and realistic environments, cooperation between the caregiver and the robot is required. The caregiver takes responsibility for monitoring the environment and determining suitable actions, while the robot undertakes hard physical tasks. The instructions can be intuitively given by the caregiver to RIBA through tactile sensors using a newly proposed method named tactile guidance. In the present paper, we describe RIBAs design concept, its basic specifications, and the tactile guidance method. Experiments including the transfer of humans are also reported.

Tactile-based motion adjustment for the nursing-care assistant robot RIBA

In aging societies, there is a strong demand for robotics to tackle problems resulting from the aging population. Patient transfer, such as lifting and moving a bedridden patient from a bed to a wheelchair and back, is one of the most physically challenging tasks in nursing care. We have developed a prototype nursing-care assistant robot, RIBA, that can conduct patient transfer using human-type arms. The basic robot motion trajectories are created by interpolating several postures designated in advance. To accomplish more flexible and suitable motion, adjustment using sensor information is necessary, because the patients posture and positions in contact with the robot may differ slightly in each trial. In this paper, we propose a motion adjustment method in patient lifting using tactile sensors mounted on the robot arms. The results of experiments using a lifesize dummy are also presented.

Whole-body contact manipulation using tactile information for the nursing-care assistant robot RIBA

In aging societies, there is a strong demand for robotics to tackle problems resulting from the aging population. We have developed a prototype nursing-care assistant robot, RIBA, which was designed to come in direct contact with patients and conduct physically challenging tasks. RIBA interacts with its object, typically a human, through multiple and distributed contact regions on its arms and body. To obtain information on such whole-body contact, RIBA has tactile sensors on a wide area of its arms. The regions where hard contact with the manipulated person may occur have almost flat surfaces, leading to surface contact involving a finite area, in order to reduce contact pressure and not to cause the persons pain. When controlling the position and orientation of the person, the relative positions and orientations of the distributed contacting surfaces should be preserved as far as possible to maintain stable contact and not to graze the persons skin. Preserving the force and the pressure pattern of each contact region using tactile feedback is also important to provide stable and comfortable human-robot physical interaction. In this paper, we propose a whole-body contact manipulation method using tactile information to meet these requirements.

Curved Type Pneumatic Artificial Rubber Muscle Using Shape-Memory Polymer

A conventional curved type pneumatic rubber artificial muscle is composed of an internal bladder covered with a bellows sleeve extending axially. By inhibiting the extension of one side with a fiber reinforcement, bending motion occurs when air is supplied to the bladder. In this study, we developed a new actuator by replacing the fiber reinforcement with a shape-memory polymer (SMP). The SMP can be deformed above its glass transition temperature (Tg) and maintains a rigid shape after it is cooled below Tg. When next heated above Tg, it returns to its initial shape. When only part of our actuator is warmed above Tg, only that portion of the SMP is soft and can actuate. Therefore, the direction of the motion can be controlled by heating. Moreover, our actuator can be deformed by an external force above Tg and fixed when its initial shape is below Tg.

A Two-Ply Polymer-Based Flexible Tactile Sensor Sheet Using Electric Capacitance

Traditional capacitive tactile sensor sheets usually have a three-layered structure, with a dielectric layer sandwiched by two electrode layers. Each electrode layer has a number of parallel ribbon-like electrodes. The electrodes on the two electrode layers are oriented orthogonally and each crossing point of the two perpendicular electrode arrays makes up a capacitive sensor cell on the sheet. It is well known that compatibility between measuring precision and resolution is difficult, since decreasing the width of the electrodes is required to obtain a high resolution, however, this may lead to reduction of the area of the sensor cells, and as a result, lead to a low Signal/Noise (S/N) ratio. To overcome this problem, a new multilayered structure and related calculation procedure are proposed. This new structure stacks two or more sensor sheets with shifts in position. Both a high precision and a high resolution can be obtained by combining the signals of the stacked sensor sheets. Trial production was made and the effect was confirmed.

Determination of locations on a tactile sensor suitable for respiration and heartbeat measurement of a person on a bed.

Sleep monitoring systems that can be used in daily life for the assessment of personal health and early detection of diseases are needed. To this end, we are developing a system for unconstrained measurement of the lying posture, respiration and heartbeat of a person on a soft rubber-based tactile sensor sheet. The respiration and heartbeat signals can be detected from only particular locations on the tactile sensor, and the locations depend on the lying location and posture of the measured person. In this paper, we describe how to determine the measurement locations on the sensor. We also report a realtime program that detects the respiration rate and the heart rate by using this method.

MEASUREMENT OF RESPIRATION AND HEARTBEAT USING A FLEXIBLE TACTILE SENSOR SHEET ON A BED

We describe a measurement method of respiration and heartbeat using a Smart Rubber sensor, a rubber-based flexible tactile sensor sheet that we developed. This method is useful for unconstrained recording of a person sleeping soundly, sleeping lightly, lying down, sitting on a bed, and so on. Our goal is to monitor those who require nursing care. The proposed method measures respiration and heartbeat as follows. First, we measure body pressure by using the Smart Rubber sensor placed on a bed. Then, the method applies a frequency analysis to the time series data of body pressure. Finally, respiration and heartbeat are obtained by extracting suitable frequency bands. In the experiments, we show that respiration and heartbeat are successfully measured.

LYING POSTURE DETECTION FOR UNCONSTRAINED MEASUREMENT OF RESPIRATION AND HEARTBEAT ON A BED

Daily monitoring of respiration and heartbeat while sleeping provides basic data for the assessment of personal health and early detection of diseases. The monitoring should not interfere with natural sleep, and it is desirable that the sensor be imperceptible to the person being measured. We propose a method for non-invasive and unconstrained measurement of the lying posture, respiration and heartbeat of a person on a rubber-based tactile sensor sheet. The tactile sensor is soft, flexible, and thin, and is not uncomfortable for the person lying on it. To extract faint heartbeat signals from pressure changes detected by the sensor, precision measurement based on improvement of the S/N ratio by averaging oversampled data is needed. This process takes some time and can be performed at only a limited number of locations on the sensor. To determine the locations, we detect the lying location and posture of the measured personon thesensor by usingpatternrecognition based on machine learning. In this paper, we describe the measurement method and report the experimental results.