Automatic Evaluation of Trainee Nurses' Patient Transfer Skills Using Multiple Kinect Sensors
Automatic Evaluation of Trainee Nurses' Patient Transfer Skills Using Multiple Kinect Sensors
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DOI：
10.1587/transinf.e97.d.107
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发表时间：
2014
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影响因子：
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通讯作者：
Zhifeng Huang;A. Nagata;M. Kanai-Pak;J. Maeda;Y. Kitajima;Mitsuhiro Nakamura;Kyouko Aida;N. Kuwahara;T. Ogata;J. Ota
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作者：
Zhifeng Huang;A. Nagata;M. Kanai-Pak;J. Maeda;Y. Kitajima;Mitsuhiro Nakamura;Kyouko Aida;N. Kuwahara;T. Ogata;J. Ota
为了帮助学生护士学会将病人从病床转移到轮椅上，本文提出了一个自动技能评估系统，在护士培训这项任务。多个Kinect传感器被采用，与附在受训者和患者的衣服和轮椅上的彩色标记相结合，以测量两个参与者在转移过程中密切互动时的姿势，并评估受训者的动作和设备使用的正确性。测量方法包括通过附着标记的颜色识别身体关节和轮椅的特征，并通过结合来自两个传感器的颜色和深度数据计算它们的3D位置。我们首先开发了一种自动分割方法，通过从原始传感器数据中提取转移每个阶段中两个参与者运动的定义特征，将患者转移过程的连续记录转换为离散步骤。其次，一个检查表的20个评估项目，以评估实习护士的技能，在执行病人转移的定义。将项目分为两类，并提出了两种相应的方法来区分受训者的表现为正确或不正确。一种方法是基于参与者的相关身体部位是否位于在安全性和有效性方面被认为“正确”的预定义空间范围内（例如，脚适当放置以保持平衡）。第二种方法是基于量化指标和阈值的参数描述参与者的姿势和动作，由贝叶斯最小误差方法确定。构建了一个原型系统，并进行了实验，以评估所提出的方法。护士的病人转移技能的评价是成功的，自动进行。自动评估结果与人类教师的评估进行了比较，准确率超过80%。关键词：多人互动，护理技能，自动技能评估，病人转移，Kinect传感器系统
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%. key words: multiperson interaction, nursing skills, automatic skill evaluation, patient transfer, Kinect sensor systems