A Novel Methodology for Magnetic Hand Motion Tracking in Human-Machine Interfaces

被引:7
|
作者
Meier, Phil [1 ]
Rohrmann, Kris [1 ]
Sandner, Marvin [1 ]
Prochaska, Marcus [1 ]
机构
[1] Ostfalia Univ Appl Sci, Fac Elect Engn, Wolfenbuettel, Germany
关键词
hand tracking; human-machine interface; wireless hand motion capturing; GLOVE; SYSTEMS; SENSORS;
D O I
10.1109/SMC.2018.00073
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Hand motion tracking represents one of the most widely used human-computer interfaces. It plays a decisive role in many application areas such as virtual reality systems, diagnostic and treatment of a range of diseases as well as robotic hand training with human hand skills. Oftentimes magnetic field sensors combined with permanent or electric magnets are used for hand motion tracking. Typically, simple magnet models are used, that require additional devices such as acceleration sensors as well as a mathematical model of the anatomic functions of a human hand. In contrast, a sensing methodology is presented in the following, which is based only on magnetic field sensing. Thus, our methodology allows the use of magnetosensitive e-skins for hand motion tracking, whereby all of their advantages are preserved such as compact dimensions or the robustness against harsh environmental conditions. Furthermore, calculations show an outstanding sensing accuracy of the presented hand motion tacking method.
引用
收藏
页码:372 / 378
页数:7
相关论文
共 50 条
  • [1] Magnetic hand motion tracking system for human-machine interaction
    Ma, Y.
    Jia, W.
    Li, C.
    Yang, J.
    Mao, Z. -H.
    Sun, M.
    ELECTRONICS LETTERS, 2010, 46 (09) : 621 - U37
  • [2] Noncontact Hand Motion Classification Technique for Application to Human-Machine Interfaces
    Kurita, Koichi
    IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, 2014, 50 (03) : 2213 - 2218
  • [3] A Review on Magnetic Smart Skin as Human-Machine Interfaces
    Zhang, Junjie
    Chen, Guangyuan
    Jin, Zhenhu
    Chen, Jiamin
    ADVANCED ELECTRONIC MATERIALS, 2024, 10 (05)
  • [4] Improved Formulation of the IMU and MARG Orientation Gradient Descent Algorithm for Motion Tracking in Human-Machine Interfaces
    Admiraal, Marcel
    Wilson, Samuel
    Vaidyanathan, Ravi
    2017 IEEE INTERNATIONAL CONFERENCE ON MULTISENSOR FUSION AND INTEGRATION FOR INTELLIGENT SYSTEMS (MFI), 2017, : 403 - 410
  • [5] Conformable skill automatisation methodology for human-centred human-machine interfaces
    Sakai, Y
    CAD/CAM ROBOTICS AND FACTORIES OF THE FUTURE, 1996, : 432 - 437
  • [6] Effect of hand dynamics on virtual fixtures for compliant human-machine interfaces*
    Marayong, Panadda
    Hager, Gregory D.
    Okamura, Allison M.
    SYMPOSIUM ON HAPTICS INTERFACES FOR VIRTUAL ENVIRONMENT AND TELEOPERATOR SYSTEMS 2006, PROCEEDINGS, 2006, : 109 - 115
  • [7] IMU Sensor-Based Hand Gesture Recognition for Human-Machine Interfaces
    Kim, Minwoo
    Cho, Jaechan
    Lee, Seongjoo
    Jung, Yunho
    SENSORS, 2019, 19 (18)
  • [8] Combining eye movement and hand movement measures for evaluating human-machine interfaces
    Lin, Y
    Zhang, WJ
    2003 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOLS 1-5, CONFERENCE PROCEEDINGS, 2003, : 1089 - 1096
  • [9] A Novel Approach for Human-Machine Interaction Using Hand Gesture
    Revathi, K.
    Chellasamy, C.
    Babu, Ramesh R.
    SenthilKumar, C.
    2013 INTERNATIONAL CONFERENCE ON HUMAN COMPUTER INTERACTIONS (ICHCI), 2013,
  • [10] Gesture-based human–machine interfaces: a novel approach for robust hand and face tracking
    Farhad Dadgostar
    Abdolhossein Sarrafzadeh
    Iran Journal of Computer Science, 2018, 1 (1) : 47 - 64