%0 Conference Paper %1 6491073 %A Huang, Zhifeng %A Nagata, A. %A Kanai-Pak, M. %A Maeda, J. %A Kitajima, Y. %A Nakamura, M. %A Aida, K. %A Kuwahara, N. %A Ogata, T. %A Ota, J. %B Robotics and Biomimetics (ROBIO), 2012 IEEE International Conference on %D 2012 %K human-behavior-sensing sensors %P 842-847 %R 10.1109/ROBIO.2012.6491073 %T Posture study for self-training system of patient transfer %U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6491073 %X 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.

@inproceedings{6491073, abstract = {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.}, added-at = {2013-05-30T14:32:18.000+0200}, author = {Huang, Zhifeng and Nagata, A. and Kanai-Pak, M. and Maeda, J. and Kitajima, Y. and Nakamura, M. and Aida, K. and Kuwahara, N. and Ogata, T. and Ota, J.}, biburl = {https://www.bibsonomy.org/bibtex/28ae5dc33ac12a7e01b66c3c516b59c0d/tfukuhara2013}, booktitle = {Robotics and Biomimetics (ROBIO), 2012 IEEE International Conference on}, description = {IEEE Xplore - Posture study for self-training system of patient transfer}, doi = {10.1109/ROBIO.2012.6491073}, interhash = {fdb5d003c249361d3eaa653629ae244b}, intrahash = {8ae5dc33ac12a7e01b66c3c516b59c0d}, keywords = {human-behavior-sensing sensors}, pages = {842-847}, timestamp = {2013-05-30T14:32:18.000+0200}, title = {Posture study for self-training system of patient transfer}, url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6491073}, year = 2012 }