Effect of practice on similar and dissimilar skills in patient transfer through training with a 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 opportunity to practice on a diverse variety of patients. Using a robot to simulate actual patients, this study observes the learning transfer effect of practice on practice-similar and practice-dissimilar skills from one patient to another, and investigates which types of practice suit which kinds of training. An experiment was conducted by administering a pre-test, practice, a post-test, and a transfer test to two groups (N?=?8), each with different practice-related skills. The evaluation used a checklist covering required skills that were either similar or dissimilar across groups, depending on their practice. The effect of practice can be observed through a comparison of skills similar to one group but dissimilar to the other. The results show that practice facilitates learning transfer on similar skills but not, or to a lesser degree, on dissimilar skills. Furthermore, if skills needed to handle given symptoms are unfamiliar or inaccessible to students, practice related to those symptoms should be emphasized through simulated training with robots.     

Nursing staff's communication modes in patient transfer before and after an educational intervention

The objective was to explore and describe nursing staff's body awareness and communication in patient transfers and evaluate any changes made after an educational intervention to promote staff competence in guiding patients to move independently. In total, 63 nursing staff from two hospitals wrote weekly notes before and after the intervention. The topics were: A) reflect on a transfer during the last week that you consider was good and one that was poor; B) reflect on how your body felt during a good and a poor transfer. The notes were analysed with content analysis. The results showed five different communication modes connected with nursing staff's physical and verbal communication. These communication modes changed after 1 year to a more verbal communication, focusing on the patient's mobility. The use of instructions indicated a new or different understanding of patient transfer, which may contribute to a development of nursing staff's competence.

Statement of Relevance: The present findings indicate that patient transfer consists of communication. Therefore, verbal and bodily communication can have an integral part of training in patient transfer; furthermore, the educational design of such programmes is important to reach the goal of developing new understanding and enhancing nursing staff's competence in patient transfer.     

机器人辅助上肢关节运动控制与临床实验研究

针对机器人辅助患肢被动康复训练过程中关节活动度(ROM)及运动控制参数不能随患肢病情实时调整的问题,提出一种新的模糊自适应关节被动运动闭环监督控制方法.该方法首先根据患肢关节活动恢复程度设计上层监督控制器,得到符合患肢病情的关节期望运动范围;再通过设计下层闭环位置跟踪控制器,控制机器人平稳地牵引患肢关节沿目标轨迹进行训练.临床实验结果验证了所提算法的有效性.     

Simulated crepitus and its reality-based specification using wearable patient dummy

Physical therapists are trained in manual examination techniques to test the impaired motor functions of patients. In this study, we have introduced a wearable robotic dummy joint to simulate disordered joint resistances or behaviors to support physical therapists in learning such techniques. We developed a discontinuous joint friction model based on a stick-slip phenomenon to simulate knee joint resistances caused by crepitus, a typical symptom accompanied by osteoarthritis. Practicing therapists participated in a reality-based evaluation test and specified acceptable parameter sets to adjust the simulated crepitus for the exoskeletal patient robot. The simulated crepitus and wearable dummy joint are expected to support the training of physical therapists.     

双足机器人HEUBR-1 样机研制与实验研究

模拟人的肌肉驱动方式,为双足机器人HEUBR-1 设计了二自由度的空间并联机构,并将其应用于双 足机器人HEUBR-1 下肢关节,实现了一种新的串并混联的仿人下肢结构．在HEUBR-1 的足部增加了足趾关节,使 机器人能够模拟人的行走方式,实现真正的拟人步态行走．阐述了双足机器人HEUBR-1 稳定拟人行走的关键性技 术,提出了综合稳定性判据,分析了拟人的多种步态．通过拟人行走步态实验分析,验证了双足机器人HEUBR-1 串 并混联的仿人结构的设计合理性及拟人步态分析的准确性．     

Little effect of transfer technique instruction and physical fitness training in reducing low back pain among nurses: a cluster randomised intervention study

The aim of this study was to evaluate the effect of a transfer technique education programme (TT) alone or in combination with physical fitness training (TTPT) compared with a control group, who followed their usual routine. Eleven clinical hospital wards were cluster randomised to either intervention (six wards) or to control (five wards). The intervention cluster was individually randomised to TT (55 nurses) and TTPT (50 nurses), control (76 nurses). The transfer technique programme was a 4-d course of train-the-trainers to teach transfer technique to their colleagues. The physical training consisted of supervised physical fitness training 1 h twice per week for 8 weeks. Implementing transfer technique alone or in combination with physical fitness training among a hospital nursing staff did not, when compared to a control group, show any statistical differences according to self-reported low back pain (LBP), pain level, disability and sick leave at a 12-month follow-up. However, the individual randomised intervention subgroup (transfer technique/physical training) significantly improved the LBP-disability (p = 0.001). Although weakened by a high withdrawal rate, teaching transfer technique to nurses in a hospital setting needs to be thoroughly considered. Other priorities such as physical training may be taken into consideration. The current study supports the findings of other studies that introducing transfer technique alone has no effect in targeting LBP. However, physical training seems to have an influence in minimising the LBP consequences and may be important in the discussion of how to prevent LBP or the recurrence of LBP among nursing personnel.     

Effect of robot’s gaze behaviors for attracting and controlling human attention

Controlling someone’s attention can be defined as shifting his/her attention from the existing direction to another. To shift someone’s attention, gaining attention and meeting gaze are two most important prerequisites. If a robot would like to communicate a particular person, it should turn its gaze to him/her for eye contact. However, it is not an easy task for the robot to make eye contact because such a turning action alone may not be effective in all situations, especially when the robot and the human are not facing each other or the human is intensely attending to his/her task. Therefore, the robot should perform some actions so that it can attract the target person and make him/her respond to the robot to meet gaze. In this paper, we present a robot that can attract a target person’s attention by moving its head, make eye contact through showing gaze awareness by blinking its eyes, and directs his/her attention by repeating its eyes and head turns from the person to the target object. Experiments using 20 human participants confirm the effectiveness of the robot actions to control human attention.     

Learning proactive behavior for interactive social robots

Learning human–robot interaction logic from example interaction data has the potential to leverage “big data” to reduce the effort and time spent on designing interaction logic or crafting interaction content. Previous work has demonstrated techniques by which a robot can learn motion and speech behaviors from non-annotated human–human interaction data, but these techniques only enable a robot to respond to human-initiated inputs, and do not enable the robot to proactively initiate interaction. In this work, we propose a method for learning both human-initiated and robot-initiated behavior for a social robot from human–human example interactions, which we demonstrate for a shopkeeper interacting with a customer in a camera shop scenario. This was achieved by extending an existing technique by (1) introducing a concept of a customer yield action, (2) incorporating interaction history, represented by sequences of discretized actions, as inputs for training and generating robot behavior, and (3) using an “attention mechanism” in our learning system for training robot behaviors, that learns which parts of the interaction history are more important for generating robot behaviors. The proposed method trains a robot to generate multimodal actions, consisting of speech and locomotion behaviors. We compared this study with the previous technique in two ways. Cross-validation on the training data showed higher social appropriateness of predicted behaviors using the proposed technique, and a user study of live interaction with a robot showed that participants perceived the proposed technique to produce behaviors that were more proactive, socially-appropriate, and better in overall quality.     

Inverse Kinematics Based Human Mimicking System using Skeletal Tracking Technology

Humanoid robots needs to have human-like motions and appearance in order to be well-accepted by humans. Mimicking is a fast and user-friendly way to teach them human-like motions. However, direct assignment of observed human motions to robot’s joints is not possible due to their physical differences. This paper presents a real-time inverse kinematics based human mimicking system to map human upper limbs motions to robot’s joints safely and smoothly. It considers both main definitions of motion similarity, between end-effector motions and between angular configurations. Microsoft Kinect sensor is used for natural perceiving of human motions. Additional constraints are proposed and solved in the projected null space of the Jacobian matrix. They consider not only the workspace and the valid motion ranges of the robot’s joints to avoid self-collisions, but also the similarity between the end-effector motions and the angular configurations to bring highly human-like motions to the robot. Performance of the proposed human mimicking system is quantitatively and qualitatively assessed and compared with the state-of-the-art methods in a human-robot interaction task using Nao humanoid robot. The results confirm applicability and ability of the proposed human mimicking system to properly mimic various human motions.     

基于Kinect的下肢体康复动作评估系统

针对中风患者下肢体的训练需求,设计了一个康复训练评估系统.利用Kinect 2.0体感设备实时采集下肢体康复动作的序列信息,提取相关关节的角度序列特征,与动作库中的标准动作序列特征对比后,计算相关度,再加入时间参量,综合得出评估结果.试验结果表明:该系统能准确地识别和引导受试者的康复动作;并且可以通过组建不同的姿势库来评估更多的康复动作.     

Motion teaching method for complex robot links using motor current

Conventional robot motion teaching methods use a teaching pendant or a motion capture device and are not the most convenient or intuitive ways to teach a robot sophisticated and fluid movements such as martial arts motions. Ideally, a robot could be set up as if it were a clothing mannequin that has light limbs and flexible yet frictional joints which can be positioned at desirable shape and hold all the positions. To do the same with a robot, an operator could pull or push the links with minor forces until the desired robot posture is attained. For this, a robot should measure the applied external force by using torque sensors at the robot joints. However, torque sensors are bulky and expensive to install in every DOF joints while keeping a compact design, which is essential to humanoid robots. In this paper, we use only motor current readings to acquire joint torques. The equations used to compensate for the effect of gravity on the joint torques and the self-calibration method to earn link parameters are presented. Additionally, kinematic restrictions can be imposed on the robot’s arms to simplify the motion teaching. Here, we teach the Kendo training robot with this method and the robot’s learnt martial art motions are demonstrated.     

Bionic control of exoskeleton robot based on motion intention for rehabilitation training

As the complexity of obtaining irregular daily motion trajectory during upper limb rehabilitation training, this paper proposes the bionic control method for the presented exoskeleton robot arm based on motion intention. Firstly, the collected motion signal is pre-processing by filtering. Then the motion intention and motion mode of the processed signal are classified by using the hierarchical multi-classification support vector machine. Meanwhile, the adaptive Hopf oscillator network based on dynamic learning is used for offline learning of joint motion trajectory, and the parameters of the reproduced signal in different motion modes are obtained. Finally, the corresponding parameters are transferred according to the user’s intention, and the oscillator network is reconstructed to realize the periodic motion control of rehabilitation training. With experimental verification, the proposed method can follow the human body’s motion intention and reproduce the daily motion trajectory of the upper limb. The results show it can be used to conduct rehabilitation training for the patient.     

Teaching robots to cooperate with humans in dynamic manipulation tasks based on multi-modal human-in-the-loop approach

We propose an approach to efficiently teach robots how to perform dynamic manipulation tasks in cooperation with a human partner. The approach utilises human sensorimotor learning ability where the human tutor controls the robot through a multi-modal interface to make it perform the desired task. During the tutoring, the robot simultaneously learns the action policy of the tutor and through time gains full autonomy. We demonstrate our approach by an experiment where we taught a robot how to perform a wood sawing task with a human partner using a two-person cross-cut saw. The challenge of this experiment is that it requires precise coordination of the robot’s motion and compliance according to the partner’s actions. To transfer the sawing skill from the tutor to the robot we used Locally Weighted Regression for trajectory generalisation, and adaptive oscillators for adaptation of the robot to the partner’s motion.     

基于阻抗模型的下肢康复机器人交互控制系统设计

为帮助下肢功能障碍患者进行康复训练,设计了下肢康复机器人。对于该机器人的控制,采用传统系统无法柔顺控制,导致机器人运动轨迹偏离预设轨迹。针对该现象,提出了基于阻抗模型的下肢康复机器人交互控制系统设计。通过分析总体控制方案,设计系统硬件结构框图。采用L型二维力传感器,确定两个方向的人机交互力。使用绝对值编码器安装在各个关节处,其输出值作为髋关节、膝关节、踝关节电机的转动位置,增量编码器安装在电机轴上,测量值用来作为后期控制方法的输入参数。构建阻抗控制模型,能够调节机器人位置和速度,具有消除力误差功能。依据此力矩对参考运动轨迹进行设计,实时获取患者康复训练的跟踪、主动柔顺和接近状态信息。在柔顺训练实验测出人机交互力,通过实验结果知,在检测到人体主动力矩异常时,系统能够重新优化轨迹,具有良好柔顺控制效果。     

Bayesian modeling of the risk of non-repeatability for the networked robotic system

This study addresses one of the most important performance criteria, namely the robot’s repeatability, from the e-quality for manufacture (EQM) point of view within the framework of networked robotic system. Real-time monitoring and control of remotely located robots allow the operators to continuously assess the risk of robot’s non-repeatability. The elaborated methodology of predictive modeling on the risk of robot’s non-repeatability consists of three stages: (1) regression analysis on the association between the disturbing factors and the key performance variables that influence the robot repeatability; (2) probabilistic assessment on the admissible deviations of key performance variables that simulate the robot operations as probabilities of job service without failures in the queueing system; and (3) Bayesian assessment on the risk of non-repeatability for the robot operations. The proposed methodology is expected to reduce the risk of robot’s non-repeatability, which is better suited for today’s networked, distributed production environment, where quality standards are stringent and customer expectations are high.     

康复机器人患肢参数的在线辨识

根据患肢训练时力和位置等信息反馈,提出用解超定方程组的方法在线辨识患肢的动力学参数,实现患肢动力学模型的在线辨识,为远程康复训练机器人系统的实时控制提供较为准确的依据.仿真实验验证,该方法能较好地改善系统的动态性能,使系统具有较好的稳定性和鲁棒性.     

Remote control of space robots

A method for the remote control of a space robot is proposed for the case of large delays in the transmission of control signals from the Earth to the local robot control system and in feedback signals. The method involves the use of the model of the space robot and its current environment with the simulation of gravity conditions at the ground control center. In this model environment, the operator should carry out the required actions by controlling the space robot model in the master-slave mode using an arm with six degrees of freedom capable of reflecting the interaction force of a model robot working tool with models of the objects of the environment. The arm movement trajectory and the law of time variation of the reflected interaction force vector are program-based for the local space robot control system and should be executed by it upon reception from the ground control center. The robot’s possible erroneous actions generated by the inevitable inaccuracy of the environment model are compensated by the proposed method of programmed trajectory correction. In accordance with it, in order to generate correction signals, additional information received from different sensors is used. These sensors can be installed on both the model and space robot itself. This information includes data on the mutual position of a robot’s working tool and models of the objects of the environment, as well as on the interaction forces between them. The paper presents a detailed theoretical justification of the proposed approach and experimental results that confirm the theoretical conclusions.     

Modeling and Verification of a Bimanual-Coordinated Training System

Recently, much attention has been paid to the development of robots that support bilateral arm training in various patterns. However, traditional bimanual rehabilitation robots usually realized different training modes with the robot providing a corresponding force for the impaired limb or else achieved an active-assisted mode with the healthy limb providing an assisted force for the impaired one. This paper proposes a robot to support bimanual-coordinated training. Different training modes are realized with one limb providing a corresponding force for the other limb. Two upper limbs accomplish symmetric movements in each training mode. Motion tracking training in active-resisted and active-assisted modes was performed on 11 healthy subjects. After bimanual-coordinated training, position tracking precision was significantly improved. The preliminarily results confirmed the feasibility of the system for supporting healthy subjects in performing bimanual-coordinated training tasks and demonstrated the effectiveness of the system in improving bimanual-coordinated performance of healthy subjects. Such a system could be potentially useful for patients who are in need of motor function rehabilitation after incidents such as stroke.     

全方位轮式下肢康复训练机器人轨迹跟踪控制

全方位轮式下肢康复训练机器人需要跟踪医生给受训者设定的轨迹.机器人自身的不确定性和患者在康复训练时对机器人运行产生的干扰会影响其跟踪性能.本文设计了一种非线性L<,2>鲁棒控制器解决上述扰动抑制问题.给出了下肢康复训练机器人的运动学模型,并由此建立了位置误差系统的动态模型.将机器人的跟踪和不确定干扰抑制归结为L<,2>...     

A versatile interaction framework for robot programming based on hand gestures and poses

This paper proposes a framework for industrial and collaborative robot programming based on the integration of hand gestures and poses. The framework allows operators to control the robot via both End-Effector (EE) and joint movements and to transfer compound shapes accurately to the robot. Seventeen hand gestures, which cover the position and orientation controls of the robotic EE and other auxiliary operations, are designed according to cognitive psychology. Gestures are classified by a deep neural network, which is pre-trained for two-hand pose estimation and fine-tuned on a custom dataset, achieving a test accuracy of 99%. The index finger’s pointing direction and the hand’s orientation are extracted via 3D hand pose estimation to indicate the robotic EE’s moving direction and orientation, respectively. The number of stretched fingers is detected via two-hand pose estimation to represent decimal digits for selecting robot joints and inputting numbers. Finally, we integrate these three manners seamlessly to form a programming framework.We conducted two interaction experiments. The reaction time of the proposed hand gestures in indicating randomly given instructions is significantly less than that of other gesture sets, such as American Sign Language (ASL). The accuracy of our method in compound shape reconstruction is much better than that of hand movement trajectory-based methods, and the operating time is comparable with that of teach pendants.