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The development of body sensor networks (BSNs) with rich multimodal signals has enabled highly accurate fine-grained action detection, which is the cornerstone of many human–computer interaction applications. However, in the case of consecutive fine-grained actions, most existing wearable sensor-based detection methods are constrained by sliding windows because of their limited temporal receptive fields, and existing sequence-to-sequence detection methods cannot effectively leverage the potential of multimodal information of wearable sensors. Herein, to give multimodal signals full play in fine-grained action detection, we propose a novel temporal convolutional network by designing a channel attention-based multistream structure. We apply it to a promising application for correct and incorrect patient transfer nursing action detection. A data set is collected from a BSN on a patient when nurses perform patient transfer. Extensive experiments on our data set and public data set (C-MHAD) demonstrate that the proposed method is superior to the state-of-the-art methods, because it can strengthen the utilization of prediction features from the more convincing modal stream at each time frame. The source code is available at https://github.com/zzh-tech/Continuous-Action-Detection .

To more efficiently enhance the patient transfer skills of nursing students, this study aims to integrate a transfer skills evaluation system and a robot patient. The evaluation parameters, namely, the translational acceleration of the waist, rotational speed of the chest, and joint angles of the shoulder, hip, and knee, were selected on the basis of the pre-experimental results obtained with a simulated patient acted by the human individuals. To measure these parameters, inertial measurement unit (IMU) and angular position sensors were installed on the robot patient. An experiment was conducted with four nursing teachers to verify whether the robot patient could distinguish the incorrect methods of the transfer skills, determined to be a common mistake made by the nurses. According to the results, most transfer steps had the same effect on the simulated patient and the robot patient, which demonstrates that the robot patient is a suitable substitute for an actual patient. However, in certain steps, the robot patient was not able to distinguish between the correct and incorrect methods using the chosen parameters owing to the differences being insignificant. These insignificant differences were mostly attributed to the passive joint design of the robot patient.

With the number of hospital stays increasing, nurses require more training to handle a variety of patients. However, time for training in nursing schools is limited, and students lack the opportuni...

Recently, human patient simulators have been widely developed as substitutes for real patients with the objective of applying them as training tools in nursing education. Such simulated training is perceived as beneficial for imparting the required practical skills to students. Considering the aging world population, this study aimed to develop a robot patient for training nursing students in the sit-to-stand (STS) transfer skill, which is indispensable in caring for elderly people. To assess a student’s skill, the robot patient should be able to access the skill correctness and behave according to whether the skill is correctly or incorrectly implemented. Accordingly, an STS control method was proposed to reproduce the different STS movements during correct and incorrect applications of the skill by the nurses. The lower limbs of a prototype robot were redesigned to provide an active joint with a compliant unit, which enables the measurement of external torque and flexibility of the human joint to be reproduced. An experiment was conducted with four nurse teachers, each of whom was asked to demonstrate both correct and incorrect STS transfer skills. The results of the external torque and joint torque measured in robot’s lower limbs revealed that a significant difference (p &lt, 0.05) between correct and incorrect skills. It also indicates the introduction of the proposed control method for the robot can satisfy the requirement of the assessment of STS skill. Among the various measurements conducted, the external torque of the hip joint exhibited the most significant difference and therefore represented the most robust measure for assessing whether the STS transfer skill was correctly applied.

To improve the patient transfer skill of nursing education students, we developed a robot patient that can simulate three categories of patients: 1) patients whose movements are affected by paralysis; 2) patients whose movements are sensitive to pain with painful expression; and 3) patients whose movements are constrained by medical devices. By practicing with the robot patient, nursing students can learn the skills required for interacting with various patients. To simulate trunk movements of these different patients, novel waist and hip joints with hardware-inherent compliance and force-sensing capability were proposed. In addition, control methods were developed and the parameters were tuned based on actual patient videos. To evaluate the developed robot, nursing teachers performed trials of transferring the robot patient as they would transfer an actual patient. The nursing teachers scored the robot patients based on a checklist. Moreover, subjective evaluations of a questionnaire were performed by the nursing teachers. The results showed that the nursing teachers performed most of the required skills of the checklist and agreed regarding the learning effectiveness of the robot. They recommended training nursing students using the robot patient in the questionnaire. Finally, hugging speed comparison showed that the nurses slow down the speed when dealing with a robot patient with painful expression.

In the past few decades, simulation training has been used to help nurses improve their patient-transfer skills. However, the effectiveness of such training remains limited because it lacks effective ways of simulating patients’ actions realistically. It is difficult for nurses to use the skills learned from simulation training to transfer an actual patient. Therefore, we developed a robot patient that could simulate the behavior of patients’ limbs for patient-transfer training. This study examined the performance of the robot used in training and evaluated its training effectiveness. Four nursing teachers individually transferred the robot patient and then scored the robot patient's ability to simulate patients’ actions and its suitability for skill training. An experiment using pre-post control group design was carried out to examine the robot patient's training effectiveness compared with the human simulated patient. The participants were 20 nursing students and one nursing teacher who was responsible for scoring the students’ skills in the pre-test and post-test. All of the students were assigned to train with either the proposed robot patient or a healthy person simulating the patient. The results show that all four nursing teachers regarded the robot patient's actions as realistic. In addition, all four teachers agreed that the robot patient was suitable for skill training. The results also show that the proposed robot patient is more challenging than the current method, which employs a healthy person to simulate the patient. Significant skill improvement ( p < 0.01) was observed in the experimental group when transferring the robot patient.

Currently, due to shortages in the nursing faculty and low access to actual patients, it is difficult for students to receive feedback from teachers and practice with actual patients to obtain clinic experience. Thus, both evaluation systems and simulated patients have become urgent requirements. Accordingly, this study proposes a method to evaluate the nurse&rsquo, s transfer skill through observation from the patient. After verifying the proposed method, it will be integrated with a robotic patient as a future work. To verify if such an evaluation is practical, a checklist comprising 16 steps with correct and incorrect methods was proposed by the nursing teachers. Further, the evaluation parameters were determined as translational acceleration, rotational speed, and joint angle of patient. Inertial sensors and motion capture were employed to measure the translational acceleration, rotational speed, and joint angle. An experiment was conducted with two nursing teachers, who were asked to carry out both correct and incorrect methods. According to the results, three parameters reveal the difference for a patient under correct/incorrect methods and can further be used to evaluate the nurse&rsquo, s skill once the thresholds are determined. In addition, the applicability of inertial sensors is confirmed for the use of robot development.

Nowadays with the advancement of technology, many evaluation systems have been developed for skill learning. However, those systems are inevitable to set the sensors on trainees or environment (e.g. camera). Therefore this study aims to propose a method to indirectly evaluate the patient transfer skill through only measuring the patient, instead of the nurse. Based on the observation of the patient’s movements during transfer, joint angle and acceleration of patient are determined as two parameters to evaluate the nurse’s skill. Incorrect ways of transfer skill were added into a checklist, proposed in our pre-work, based on clinical experience of nursing teachers. An experiment was conducted by two nursing teachers, and they were asked to carry out the patient transfer skill through both correct and incorrect ways as proposed in the checklist. The results exhibit those two parameters enable to show the difference while the nurses carried out the incorrect way. The angles of hip and knee joints enable to reveal the difference in the steps related to standing up and sitting down; while the acceleration of patient can exhibit the difference of patient’s dynamic movement. According to such results, the indirect evaluation became practical for the future works.

We developed a robot patient for patient transfer training for simulating a patient’s performance during patient transfer and for enabling nurses to practice their nursing skills on it. To realize the robot patient, we focused on addressing the problems of designing its limb actions to enable it to respond to nurses’ operations. RC servos and electromagnetic brakes were installed in the joints to enable the robot to simulate a patient’s limb actions, such as embracing and remaining standing. To enable the robot to automatically respond to nurses’ operations, an identification method for these operations was developed that used voice commands and the features of the limbs’ posture measured by angle sensors installed in the robot’s joints. The robot patient’s performance was examined by a control test in which four experienced nursing teachers performed patient transfer with the robot patient and a human-simulated patient. The results revealed that the robot patient could successfully simulate the actions o...

This research aims to develop a robot to help nursing students learn how to physically transfer paralyzed patients. Our prior robotic prototype had only been designed to imitate a patient with weak lower limbs; it was unable to imitate the imbalance and instability of the trunk. Therefore, we developed and waist joint on the prototype robot and also the control system in order to emulate quadriplegic and hemiplegic patients’ tendency to fall over. The waist was designed with 2 DOF compliant joints. Evaluation of the robot showed that the robot was able to properly imitate the unstable waist movements of paralyzed patients.

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 .

Purpose – This paper aims to construct a recognition system of nursing activities. Design/methodology/approach – The authors used accelerometers and radio frequency identification (RFID) tags to ensure patient privacy in practical nursing care environments. The accelerometers were attached to the body of the nurse, and the RFID was attached to apparatuses and objects. In addition, a pattern classification algorithm using a support vector machine and filtering methodology were applied. Findings – The accuracy using accelerometers and RFID was 73 percent. When the filtering algorithm was applied, the results were 79 percent. The results showed that activities with short execution times or those that resembled others in posture had low recognition accuracy. Research limitations/implications – Activities requiring only a short period of time tend to be misrecognized. Practical implications – It is possible to construct a training system for nursing activities with the system that recognizes the sequence of nursing activities and how much time is spent for individual activities. Originality/value – The originality of the paper is to construct the system that considers the following characteristics of nursing activities: about 13 activities that are fundamental for nurses can be recognized, privacy of the patient is considered, several activities utilizing only part of the body (not whole body) can be recognized, and activities involving and not involving some types of apparatus can be recognized.

Purpose. To determine the effect of hospital work environments on hospital outcomes across multiple countries. Design. Primary survey data using a common instrument were collected from separate cross sections of 98 116 bedside care nurses practising in 1406 hospitals in 9 countries between 1999 and 2009. Main Outcome Measures. Nurse burnout and job dissatisfaction, patient readiness for hospital discharge and quality of patient care. Results. High nurse burnout was found in hospitals in all countries except Germany, and ranged from roughly a third of nurses to about 60% of nurses in South Korea and Japan. Job dissatisfaction among nurses was close to 20% in most countries and as high as 60% in Japan. Close to half or more of nurses in every country lacked confidence that patients could care for themselves following discharge. Quality-of-care rated as fair or poor varied from 11% in Canada to 68% in South Korea. Between one-quarter and one-third of hospitals in each country were judged to have poor work environments. Working in a hospital with a better work environment was associated with significantly lower odds of nurse burnout and job dissatisfaction and with better quality-of-care outcomes. Conclusions. Poor hospital work environments are common and are associated with negative outcomes for nurses and quality of care. Improving work environments holds promise for nurse retention and better quality of patient care.

Aims. To describe nurse burnout, job dissatisfaction and quality of care in Japanese hospitals and to determine how these outcomes are associated with work environment factors. Background. Nurse burnout and job dissatisfaction are associated with poor nurse retention and uneven quality of care in other countries but comprehensive data have been lacking on Japan. Design. Cross-sectional survey of 5956 staff nurses on 302 units in 19 acute hospitals in Japan. Methods. Nurses were provided information about years of experience, completed the Maslach Burnout Inventory and reported on resource adequacy and working relations with doctors using the Nursing Work Index-Revised. Results. Fifty-six per cent of nurses scored high on burnout, 60% were dissatisfied with their jobs and 59% ranked quality of care as only fair or poor. About one-third had fewer than four years of experience and more than two-thirds had less than 10. Only one in five nurses reported there were enough registered nurses to provide quality care and more than half reported that teamwork between nurses and physicians was lacking. The odds on high burnout, job dissatisfaction and poor–fair quality of care were twice as high in hospitals with 50% inexperienced nurses than with 20% inexperienced nurses and 40% higher in hospitals where nurses had less satisfactory relations with physicians. Nurses in poorly staffed hospitals were 50% more likely to exhibit burnout, twice as likely to be dissatisfied and 75% more likely to report poor or fair quality care than nurses in better staffed hospitals. Conclusions. Improved nurse staffing and working relationships with physicians may reduce nurse burnout, job dissatisfaction and low nurse-assessed quality of care. Relevance to clinical practice. Staff nurses should engage supervisors and medical staff in discussions about retaining more experienced nurses at the bedside, implementing strategies to enhance clinical staffing and identifying ways to improve nurse-physician working relations.

The purposes of this study are (1) To develop a chest auscultation self-learning system for nursing students with which both a self-learning tool and an evaluation tool are integrated, and (2) To evaluate the system whether nursing students are able to acquire the chest ausculation skills. We have developed a system using RFID tags and RFID reader (TECCO). Six nursing students used this system for 15 min, and received feedback after each performance from this system. The students’ performance was evaluated. The highest score was 85, and the lowest score was 10. The range of practice time was between 5 and 13. The differences between highest score and lowest score for each examinee ranged from 63 to 25. The results indicated that nursing students can learn the chest auscultation skills using this system even they spend a limited time for practice if they use repeatedly.

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.

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.