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 adduction/abduction and right shoulder internal/external rotation were lower in the case of teachers dressing the mannequin than when students were dressing it. Originality/value – The proposed system can simulate a completely paralyzed patient that nursing students would normally be almost unable to train with. Additionally, the proposed approach can reveal differences between skilled and non-skilled people in the treatment of a patient’s body.

This paper describes the construction and evaluation of a self-help skill training system for assisting student nurses in learning skills involving the transfer of patients from beds to wheelchairs. We have proposed a feedback method that is based on a checklist and video demonstrations. To help trainees efficiently check their performance and correct errors, the checklist was prepared with items specific to the performance of tasks related to individual body parts (e.g., the height of the waist). In this system, two Kinect RGB-D sensors were used for measuring the posture of the trainees and patients. An automatic skill evaluation method was used to designate the trainees' performance against each evaluation item as correct or incorrect. Furthermore, the system's operation interface was designed to enable self-operation by trainees. Control tests were performed to measure the training effectiveness of the system. The results of the tests on a control group (n = 5) that used only a textbook and demonstration video but did not receive feedback were compared with those of the experimental group (n = 5) that used the proposed system. The results of both subjective and objective evaluation demonstrated that the experimental group showed greater improvement in performing patient transfer than the control group (p < 0.05).

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. The experiment was performed by a nurse. The nurse attempted to assist the robot patient by helping it to stand up from the wheelchair, utilizing the basic techniques and checklist found in the fundamental nursing education textbook. As a result of this study, we have determined that the utilization of a robot could contribute to the teaching material for nursing education, and created an opportunity to reconsider what is accepted as basics or fundamental techniques.

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.

The purpose of this study was to investigate the relationship between learning effects of the self-learning tool for nursing students and types of teaching materials. Ten nursing students were asked to perform transfer a patient from bed to wheelchair after watching the demo video and practicing 20 minutes. The students' performance was evaluated before and after practicing. The students were also asked to choose teaching materials that would be developed in the future. Out of nine teaching materials, the students chose seven of them. Correspondence analysis was conducted between the results of the evaluation of students' transfer technique and their preference of teaching materials. The results indicated that there was no relationship between the preference of teaching materials and the scores of transfer techniques. The authors concluded that the self-learning was not affected by the preference of teaching materials.

To help student nurses learn to transfer patients from a bed to a wheelchair, this paper proposes a system for automatic skill evaluation in nurses' training for this task. Multiple Kinect sensors were employed, in conjunction with colored markers attached to the trainee's and patient's clothing and to the wheelchair, in order to measure both participants' postures as they interacted closely during the transfer and to assess the correctness of the trainee's movements and use of equipment. The measurement method involved identifying body joints, and features of the wheelchair, via the colors of the attached markers and calculating their 3D positions by combining color and depth data from two sensors. We first developed an automatic segmentation method to convert a continuous recording of the patient transfer process into discrete steps, by extracting from the raw sensor data the defining features of the movements of both participants during each stage of the transfer. Next, a checklist of 20 evaluation items was defined in order to evaluate the trainee nurses' skills in performing the patient transfer. The items were divided into two types, and two corresponding methods were proposed for classifying trainee performance as correct or incorrect. One method was based on whether the participants' relevant body parts were positioned in a predefined spatial range that was considered ‘correct’ in terms of safety and efficacy (e.g., feet placed appropriately for balance). The second method was based on quantitative indexes and thresholds for parameters describing the participants' postures and movements, as determined by a Bayesian minimum-error method. A prototype system was constructed and experiments were performed to assess the proposed approach. The evaluation of nurses' patient transfer skills was performed successfully and automatically. The automatic evaluation results were compared with evaluation by human teachers and achieved an accuracy exceeding 80%.

In this paper, we propose a self-training system to assist nursing students to learn nursing skills. The system focuses on the task of transferring a patient from a bed to a wheelchair. In the system, two Kinect sensors were applied to measure the posture of the trainees and patients and an automatic evaluation method was used to classify the trainees' performance in each skill as correct or incorrect. A feedback interface based on a checklist was designed to help the trainees check whether they performed correctly. The system is designed for the trainees to operate by themselves. A control test was performed to measure the training effects of the system. The results show that the growth rate of the group that trained with feedback (79%) was higher than the group that trained without feedback (48%).

In this paper, a robot patient was proposed to help nursing students improve their skills in transferring a patient from a bed to a wheelchair. The robot was used to reproduce the patients movements during the transfer process. We used a mannequin as the base and designed mechanisms for the robots arm and knee joints. There were 15 joints in the robot patient, including 2 active joints driven by motors, 4 passive joints attached with electric brakes, and 9 passive joints without actuators. Through these mechanisms, the robot could reproduce various body movements of patients during patient transfer, such as embracing a nurses shoulder, standing with assistance, and remaining in a standing position.

The purpose of this study is to clarify the relationship between nursing students' attitudes towards learning and effects of Kinect self-learning system for skill acquisition. Five students received feedback after each performance from the Kinect self-learning system. The students' performance was evaluated before (pre-test) and after (post-test) using 21 checkpoints. In order to investigate the students' attitudes towards learning, a survey questionnaire was distributed before the study. Based on the score, each student's attitudes towards learning were identified as either "active" or "passive". The difference between the pre-test and post-test scores for each student was calculated. Pearson product-moment correlation coefficients were calculated of the difference in the number of learning characteristics. There was a strong negative correlation between learning characteristic (Passive learning attitude) and the difference of score (r=-.80 p=.11). From this study, it is recommended that the Kinect self-learning system is not effective for skill acquisition for students whose attitudes were passive.

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%.

In this paper, we propose self-training system for nursing care students by using the 3D depth sensor and the camera. Our proposed system focuses on the nursing care skills of patient transfer from a bed to a wheelchair and bed making because these skills requires proper use of body mechanics in order to prevent a lower-back injury of nurses and careers. In order to implement such self-training system, the automatic evaluation method of skills is one of the essential functions. Firstly, we discussed the design the checklists for evaluating above-mentioned skills with teachers of the nursing school. Next, we prototyped the system that monitors the trainee's motion during trainee's practice and by using the recognition algorithm, the system automatically evaluates trainee's performance based on above-mentioned checklists. We conducted the preliminary experiment for comparing evaluation results of the system to those of teachers, and examined that the system could evaluate trainee's performance with almost same precision as teachers did. Also, we confirmed the effectiveness of the feedback from our proposed system.

This paper aims to construct a nursing self-training system of bed-making in which nurses must perform some skills for handling equipment (a bed, a bed pad, and a sheet) and for avoiding bodily injury. Eight evaluation points of the bed-making task, related to trainees’ posture and states of equipment, were identified through nursing textbooks and discussed with nursing teachers. To recognize and evaluate the points by image processing, we developed a system using three RGB-D (RGB color and depth) sensors. To increase the recognition rate, we clustered the color information by the K-means clustering method and then divided the bed-making procedure into three segments using color and depth information of the whole images. The average accuracy achieved by the proposed system in the evaluation experiment where 15 trainees participated was 80 %, an average consistent with that achieved by a human nursing teacher.

Sufficient training with feedback was important for nursing students to learn the techniques. In view of this, we studied the method for measuring and evaluating the performance of nursing students in order to develop a self-training system. Focusing on the training of transferring a patient from a bed to a wheelchair, we defined seven evaluation items related with the postures. In addition, evaluation indexes of each item were determined. Then, we established a prototype system based on two Kinect range cameras. Using the system, first, we recognized the body parts and joints through the color of the markers attached on the bodies. After that, the body joints' spatial locations and body parts' inclination angles were measured via the combination of color and depth information in order to calculate the indexes. We applied Bayes minimum error decision to classify nursing students' performance of each items as correct or incorrect. Ten inexperienced nursing students and five experienced nurses were asked to transfer patient from a bed to a wheelchair at least twice. Every time the patient was transferred, the nursing teacher evaluated the trainee's performance. In addition, proposed system measured and recorded the data. The significant difference between correct and incorrect performance of each item was observed through the determined indexes (P<0.01). Accuracy of performance classification was examined by the leave one-out cross-validation. The average of accuracy was up to 80%. These results suggested that the defined index was effective and the proposed classification approach could classify the performance of the nursing students as almost the same as the nursing teacher did.

In this paper, a self-training system was developed for assisting the student nurses to improve their skills of transferring patients from a bed to a wheelchair. The twenty evaluation items corresponding to the skills were defined through the theory of nursing care and the experience of nursing teachers. In addition, the evaluation parameters of each item were defined. The system employed two depth camera sensors, Kinect, to measure the movement of trainee and patient. Several color marks were attached on their joints and the 3D position of these color marks were calculated by the proposed method which uses the depth data and the RGB color data. Furthermore, to automatically evaluate, a motion recognition method based on time sequence and the features of the 3D trajectory of patient's head were proposed. Using this system, we determined the thresholds of the evaluation items' parameters by a preliminary experiment in which nursing teachers and student nurses participated. Finally, an experiment to validate the system was conducted. Performance of 5 student nurses was evaluated by the teachers and system, respectively. Using the teacher's evaluation as the benchmark, the accuracy of the system is up to 85 %.

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%.