Mannequin system for the self-training of nurses in the changing of clothes

被引:4
|
作者
Ogata, Taiki [1 ]
Nagata, Ayanori [1 ]
Huang, Zhifeng [2 ]
Katayama, Takahiro [1 ]
Kanai-Pak, Masako [3 ]
Maeda, Jukai [3 ]
Kitajima, Yasuko [3 ]
Nakamura, Mitsuhiro [3 ]
Aida, Kyoko [3 ]
Kuwahara, Noriaki [4 ]
Ota, Jun [1 ]
机构
[1] Univ Tokyo, Ctr Engn RACE, Res Artifacts, Kashiwa, Chiba, Japan
[2] Guangdong Univ Technol, Fac Automat, Guangzhou, Guangdong, Peoples R China
[3] Tokyo Ariake Univ Med & Hlth Sci, Dept Nursing, Tokyo, Japan
[4] Kyoto Inst Technol, Dept Adv Fibrosci, Kyoto, Japan
基金
日本学术振兴会;
关键词
Robotics; Education; MOTION;
D O I
10.1108/K-04-2015-0102
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Purpose - For self-training of nursing students, this paper developed a mannequin to simulate and measure the movement of a patient's arms while nurses changed the patient's clothes on a bed. In addition, using the mannequin the purpose of this paper is to determine the difference in the handling of a patient's arms between nursing teachers and students. Design/methodology/approach - The target patient was an old man with complete paralysis. Three-degrees-of-freedom (DOF) shoulder joints and one-DOF elbow joints were applied to the mannequin. The angles of all joints were measured using a potentiometer, and those angles were transmitted to a computer via Bluetooth. Findings - In a preliminary experiment, the two nursing teachers confirmed that the mannequin arms simulated the motion of the arms of a paralyzed patient. In the experiment, two teachers and six students changed the clothes of the mannequin. The average joint angle of the left elbow and the moving frequency of the left elbow, right shoulder adduction/abduction and right shoulder internal/external rotation were lower in the case of teachers dressing the mannequin than when students were dressing it. Originality/value - The proposed system can simulate a completely paralyzed patient that nursing students would normally be almost unable to train with. Additionally, the proposed approach can reveal differences between skilled and non-skilled people in the treatment of a patient's body.
引用
收藏
页码:839 / 852
页数:14
相关论文
共 50 条
  • [1] Self-Training System of Calligraphy Brushwork
    Morikawa, Ami
    Tsuda, Naoaki
    Nomura, Yoshihiko
    Kato, Norihiko
    [J]. COMPANION OF THE 2017 ACM/IEEE INTERNATIONAL CONFERENCE ON HUMAN-ROBOT INTERACTION (HRI'17), 2017, : 215 - 216
  • [2] A SELF-TRAINING SYSTEM THAT LEARNS THROUGH EXPERIMENTATION
    Braun, S. C.
    Gero, J. S.
    [J]. 9TH INTERNATIONAL DESIGN CONFERENCE - DESIGN 2006, VOLS 1 AND 2, 2006, (36): : 193 - +
  • [3] Posture Study for Self-training System of Patient Transfer
    Huang, Zhifeng
    Nagata, Ayanori
    Kanai-Pak, Masako
    Maeda, Jukai
    Kitajima, Yasuko
    Nakamura, Mitsuhiro
    Aida, Kyoko
    Kuwahara, Noriaki
    Ogata, Taiki
    Ota, Jun
    [J]. 2012 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS (ROBIO 2012), 2012,
  • [4] A practical continuous curvilinear capsulorhexis self-training system
    Dong, Jing
    Wang, Xiaogang
    Wang, Xiaoliang
    Li, Junhong
    [J]. INDIAN JOURNAL OF OPHTHALMOLOGY, 2021, 69 (10) : 2678 - +
  • [5] Badminton Self-Training System Based on Virtual Reality
    Tai, Wei-Shen
    Liu, Kuan-Hsien
    [J]. 2023 ASIA PACIFIC SIGNAL AND INFORMATION PROCESSING ASSOCIATION ANNUAL SUMMIT AND CONFERENCE, APSIPA ASC, 2023, : 1659 - 1663
  • [6] Changing grasp position on a wielded object provides self-training for the perception of length
    Abney, Drew H.
    Wagman, Jeffrey B.
    Schneider, W. Joel
    [J]. ATTENTION PERCEPTION & PSYCHOPHYSICS, 2014, 76 (01) : 247 - 254
  • [7] Changing grasp position on a wielded object provides self-training for the perception of length
    Drew H. Abney
    Jeffrey B. Wagman
    W. Joel Schneider
    [J]. Attention, Perception, & Psychophysics, 2014, 76 : 247 - 254
  • [8] SETRED: Self-training with editing
    Li, M
    Zhou, ZH
    [J]. ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2005, 3518 : 611 - 621
  • [9] Deep Bayesian Self-Training
    Fabio De Sousa Ribeiro
    Francesco Calivá
    Mark Swainson
    Kjartan Gudmundsson
    Georgios Leontidis
    Stefanos Kollias
    [J]. Neural Computing and Applications, 2020, 32 : 4275 - 4291
  • [10] Confidence Regularized Self-Training
    Zou, Yang
    Yu, Zhiding
    Liu, Xiaofeng
    Kumar, B. V. K. Vijaya
    Wang, Jinsong
    [J]. 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2019), 2019, : 5981 - 5990