Ayanori Nagata

Robot Patient for Nursing Self-training in Transferring Patient from Bed to Wheel Chair

In this paper, we proposed a robot patient for the nursing training in patient transfer. The robot patient was developed to reproduce the performance of the patients who are suffering from mobility problems. We targeted on the reproduction of movement of the patient’s limbs (arms and legs) with the consideration of physical and voice interaction between the patient and nurse. The robot patient had 15 joints including 2 active joints installed with motors, 4 passive joints installed with electric brakes and 9 passive joints without any actuators. To realize the physical interaction, potentiometer type angle sensors was utilized to detect the rotation angle of the joints of shoulders, elbows and knees. In addition, follow-up control approach was applied to the shoulder joint. By this way the robot could react accordingly when the trainees moved its limbs. A voice recognition module was applied to enable the robot to interact with the trainee by voice. An experiment was performed by a nursing teacher for examine the robot’s performance. The robot patient successfully reproduced the patient’s movement with physical and voice interaction, including embracing, keeping embracing, standing up, keeping standing and sitting down.

Mannequin system for the self-training of nurses in the changing of clothes

Purpose – For self-training of nursing students, this paper developed a mannequin to simulate and measure the movement of a patient’s arms while nurses changed the patient’s clothes on a bed. In addition, using the mannequin the purpose of this paper is to determine the difference in the handling of a patient’s arms between nursing teachers and students. Design/methodology/approach – The target patient was an old man with complete paralysis. Three-degrees-of-freedom (DOF) shoulder joints and one-DOF elbow joints were applied to the mannequin. The angles of all joints were measured using a potentiometer, and those angles were transmitted to a computer via Bluetooth. Findings – In a preliminary experiment, the two nursing teachers confirmed that the mannequin arms simulated the motion of the arms of a paralyzed patient. In the experiment, two teachers and six students changed the clothes of the mannequin. The average joint angle of the left elbow and the moving frequency of the left elbow, right shoulder ad...

Robotics as a Tool in Fundamental Nursing Education

The main purpose of this study was to investigate whether the use of robotics can contribute to nursing education, using the training for wheelchair transfers. The most common and extensively used method for practical learning is role playing. However the nursing student cannot turn into a patient thoroughly. To solve this problem, we proposed the creation of a robot patient for wheelchair transfer techniques training.

Supporting system for self training of bed-making using image processing with color and distance information

The purpose of this paper was to develop a support system to help nursing trainees learn the skill of bed-making. The system consisted of three steps: measurement, evaluation, and feedback. This paper focused on the first two issues. First, the evaluation points of bed-making were determined. Next, a measurement system was constructed using color and distance information provided by Kinects and image processing. The system extracted specific segments of whole images depicting trainees, a bed, and necessary equipment. Finally, the procedure used by trainees in making beds was quantitatively evaluated using thresholds that were determined in advance by observing teachers and students involved in bed-making. Compared to the evaluation of nursing teachers, the accuracy of the evaluation system was as much as 70%.

Development of a measurement and evaluation system for bed-making activity for self-training

This study proposes a method to automatically measure multiple objects by image processing for constructing a system for nursing trainees of self-training in the skill of bed making. In a previous study, we constructed a system to measure and evaluate trainee performance using three RGB-D (RGB color and depth) sensors. Our previous system had a problem with recognition of equipment such as the bed pad and the sheet because of color change by the light condition, the automatic color correction by the sensors and color variability in one object. In this paper, we used color reduction and cluster selection for equipment recognition. The system reduced the color in images by using k-means clustering and recognized the clusters as separate objects by predetermined thresholds. Compared with the previous method, the recognition accuracy was higher and the accuracy achieved was 70%.