Nursing-care Robot Research Articles

Mechanics Model-Based Motion Design for a Piggyback Nursing-Care Robot

To conduct a comfortable lift for the care-receiver, it takes a lot of time and operations to design the motion trajectory for each care-receiver before transfer tasks. To solve this problem, this paper proposed a method to design a lift trajectory for a piggyback transfer robot. The robot, which can lift and move a person from a wheelchair to a bed or a pedestal pan, has been developed. The trajectory obtained by this method could make the robot conduct a comfort lift for the care-receiver, according to the weight and height of the care-receiver. A human-robot mechanics model and the relationship between the comfortable lift trajectory and the care-receiver’s weight and height were also contributed. According to the test results of 20 subjects, the force parameters used for trajectory design were determined, and the trajectory design method was optimized. The results of three subjects demonstrated that this method could conveniently and quickly provide a robot lift trajectory based on the subject’s weight and height, and this trajectory also achieved a similar lift as the trajectory designed by relying on the opinion of the subject. This method can be used for the design of the reference trajectory in the compliant control of the piggyback robot, which realizes the comfortable lifting of the care-receiver.

Accurate and real-time human-joint-position estimation for a patient-transfer robot using a two-level convolutional neutral network

Human-joint-position estimation is crucial for patient-transfer robots. However, high accuracy and real-time property are difficult to achieve simultaneously. To tackle the problem, we develop a new convolutional neural network (CNN), containing two levels of subnetworks, to fuse the information in color and depth images. The first-level subnetwork generates two-dimensional (2D) human joint positions from a color image by the part-affinity-fields method. The second-level subnetwork estimates 3D human-joint positions from 2D ones and corresponding depth images. Here, strong feature-extraction function of the CNN may suppress the negative effect caused by invalid information in depth images. Meanwhile, all the estimations are implemented with the 2D CNNs, which may cause higher time-efficiency than 3D ones (mostly used in previous studies). To assess the validity, first we employed the CNN to estimate human joint positions, and obtained the accuracy and speed of respectively 90.3% and 210 ms (implemented with an affordable processing unit). Then we applied the CNN to a dual-arm nursing-care robot and found that the accuracy and processing speed satisfied the requirements in practical usage; these validated the effectiveness of our proposal and provided a new approach to generate 3D-human-joint positions through information fusion of color and depth images.

Designing One Degree of Freedom Nursing Robot for Stroke Rehabilitation

Rehabilitation training is a method of post-treatment for physically injured part of body especially the stroke patients. The training task such as massage and repetition exercises are needed to help them to have a normal life as before by a physiotherapist. The physiotherapist or physical therapy is a person who has experienced to treat disease or injury by physical methods. Therefore, a main objective of this paper is to develop a nursing-care robot system that can assist the stroke patient in doing daily activities. A one-degree of freedom nursing robot was designed based on the right-hand bicep using CATIA software. This robot was associated with the electromyography (EMG) sensors in order to collect the data from the muscle contractions. Meanwhile the EMG sensor is a muscle sensor that detect an electrical potential between the two-contraction flexion or extension muscle using the polymer Ag/AgCl coated electrodes. Then, the data from the muscle was sent to the Arduino microcontroller to control the servo motor. The different angle of servo motor can give the direction of movement of the nursing-care robot. The nursing robot was activated by electrical potential from the contraction of the muscle. The value of servo angle from 0 to 100 degrees angle made the robot will move up and down. The movement of robot in up and down direction is likely same as the therapist assistance doing to their patient. The size of robot is about 600 mm × 100 mm length and width and then was fabricated using 3D printer with one degree of freedom (DOF). The limitation of this project is focusing only the hand right hand bicep brachii muscle physiotherapy exercise.

Bionic Motion Planning and the Analysis for Human Comfort of a Piggyback Nursing-care Robot for Transfer Tasks

Bionic Motion Planning and the Analysis for Human Comfort of a Piggyback Nursing-care Robot for Transfer Tasks

Development of safety technology for rise assisting robot “Resyone Plus”

Human safety is essential for nursing care robots, which have codependent relationships with human. The international safety standard for personal care robots (ISO 13482) is the key to human safety. In this standard, protective measures for risk reduction, which are implemented using the control system, has to be developed based on functional safety standard. A lot of time is needed to do that. Initial investment for developing nursing care robots has to be held down because they are generally low-volume production and it is difficult to recover the investment. In addition, users demand low-cost products. Therefore, it is very important to achieve the functional safety suitable for "low-volume production" and "low cost product", which are required for nursing care robot. Under such circumstances, we developed the rise assisting robot “Resyone Plus”, based on such a concept of functional safety, which can realize "low-volume production" and "low cost product". And we acquired the certification of ISO 13482 about this robot and realized its commercialization. This paper reports a concrete example of how to develop it.

Approach to Development of a Nursing Care Robot at Okinawa Prefectural Council for Needs-Seeds Cooperation on Nursing Care Robots

Approach to Development of a Nursing Care Robot at Okinawa Prefectural Council for Needs-Seeds Cooperation on Nursing Care Robots

Fault classification with discriminant analysis during sit-to-stand movement assisted by a nursing care robot

Abstract Many nursing care robots have been developed to assist patients with sit-to-stand (STS) movement. However, little research has focused on user’s negative psychological changes during STS movement when assisted by a robot. STS movement accompanied with a negative psychological change is defined as a fault. The main purpose of this study was to propose a method of conveying faults to a nursing care robot through the vertical ground reaction force (vGRF). Experiments on STS movement were executed five times with ten healthy subjects under four conditions: two self-performed STSs with seat heights of 43 and 62 cm, and two robot-assisted STSs with a seat height of 43 cm and end-effector speeds of 2 and 5 s. Subjects answered a questionnaire on how they felt under the four experimental conditions. Time series data on the vGRF were measured with a Wii Balance Board (WBB). A classifier was designed according to the data on the STS smoothness in the frequency domain. The results showed that the proposed classifier had a high probability of discriminating fault classes from others. Furthermore, faults were found to result in larger standard deviations of the peak values of smoothness. The center of mass trajectories of the human body under the same experimental conditions were used to crosscheck the experimental results. Then, the angles and angular velocities of the trunk and ankle were utilized to discuss the synchrony of the body segments. Other works on more advanced signal analysis and superior fault classification approaches were also discussed. It was concluded that faults in the assistance of nursing care robots can be detected from the STS smoothness by measuring the vGRF.

Deformation Properties of 3D Printed Shape Memory Polymer

Recently, the 3D printer which can make products in a short time without cutting or casting has been attracted worldwide attention. If we use the 3D printer, it is possible that a customized product which is well suited to the individual is fabricated with low cost and in a short time. On the other hand, in the intelligent materials, shape memory polymer (SMP) has been practically used. In SMP, shape fixity and shape recovery appear based on the difference of properties of molecular motion between above and below the glass transition temperature in temperature variation. The thermomechanical property of SMP is close to that of the human body around glass the transition temperature. Since SMP has these characteristics, it can be applied to the elements coming into contact with body as a nursing-care robot in the medical field. Hence, if we make a product with SMP using the 3D printer, the new device which is well suited to the individual can be developed. In the present paper, the deformation properties of SMP made by the fused deposition modeling (FDM) 3D printer were investigated. The results obtained are as follows. (1) The deformation resistance and recovery strain in unloading of the 3D printed SMP under a low printing rate are higher and larger than these of the high printing rate. (2) If we heat the 3D printed SMP under a high printing rate, it does not recover the original shape perfectly since the residual stress appears during printing.

Evaluation of Influence of Assisting Speed of Nursing Care Robot on Sit-to-Stand Movement through Vertical Ground Reaction Force

Evaluation of Influence of Assisting Speed of Nursing Care Robot on Sit-to-Stand Movement through Vertical Ground Reaction Force

1P2-B04 介護支援ロボットによる抱き上げ動作を利用した被介護者の位置推定における経過時間の影響(福祉ロボティクス・メカトロニクス(3))

1P2-B04 介護支援ロボットによる抱き上げ動作を利用した被介護者の位置推定における経過時間の影響(福祉ロボティクス・メカトロニクス(3))

Guidance and Control of Nursing Care Robot Using Gaze Point Detector and Linear Visual Servoing

The gaze guidance and control we propose for a nursing robot uses a gaze point detector (GPD) and linear visual servoing (LVS). The robot captures stereo camera images, presents them via a head-mounted display (HMD) to the user, calculates the user’s gaze tracked by the camera, and moves to gaze of the LVS. Since in the proposal, persons requiring nursing share the robot’s field of view via the GPD, the closer they get to the target, the more accurate control becomes. The GPD, on the user’s head, has an HMD and a CCD camera.