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Robotic compliant manipulation is a very challenging but urgent research spot in the domain of robotics. One difficulty lies in the lack of a unified representation for encoding and learning of compliant profiles. This article aims to introduce a novel learning and control framework to address this problem: 1) we provide a parametric representation that enables a compliant skill to be encoded in a parametric space and allows a robot to learn compliant manipulation skills based on motion and force information collected from human demonstrations; and 2) the updating laws of the compliant profiles, including impedance and force profiles, are derived from a biomimetic control strategy based on the human motor learning principles. Our approach enables the simultaneous adaptation of impedance and feedforward force online during robot’s reproduction of the demonstrated tasks to deal with task dynamics and external interferences. The proposed approach is verified based on both simulation and real-world task scenarios. [ABSTRACT FROM AUTHOR]

With the aim of enabling the aged to obtain timely nursing care and improve their quality of life, a novel method of motion planning for a six-degree-of-freedom (6-DoF) anthropomorphic robot arm to perform bandaging tasks is proposed. The motion planning of bandaging tasks is challenging because the collision avoidance problem and maintaining tension in the bandage should be considered simultaneously. To solve such problems, a new planning strategy is proposed, including: 1) collision detection criterion to detect abnormal posture beforehand, 2) a collision avoidance strategy by extending the bandage along the tangential direction (EBATD) to adjust the end effector's trajectory, enabling the robot to avoid collision and singularity posture, and 3) velocity planning of the end effector to keep the bandage tight during application. In addition, a new end effector that can be passively rotated for releasing the bandage was designed. The simulation experimental results in MATLAB demonstrated that the proposed strategies allowed the designed 6-DoF robot to avoid collision with an injured arm and complete the bandaging task. Compared with planning without EBATD, the proposed method effectively enlarges the bandaging space of the robot arms (approximately 1.5 times in a human forearm bandaging task). In addition, the proposed method was successfully tested using a prototype robot arm to perform bandaging on a human arm. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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 $(p < 0.05)$ . [ABSTRACT FROM PUBLISHER]