Abstract
Aiming at improving the low participation and inactive motion intention of patients in traditional gait rehabilitation training, an active gait rehabilitation training system is designed based on Virtual Reality (VR) technology. This project focuses on the design of the gait parameters real-time detecting system and the virtual reality rehabilitation training scene system. Based on an analysis of gait rehabilitation medicine theory, the lower-limb joints range of motion (ROM) and the plantar pressure of the affected limb are selected as the important gait parameters, thus a built-in sensing system is constructed with three inertial measurement units (IMU) and the multi-point force sensing resistors (FSR). Through the wireless Bluetooth communication interface, the lower-limb motor parameters of patients are transmitted into the virtual training games as the motion control signals for character driven in games and the scientific evidence for rehabilitation assessment. Error analysis and compensation method of sampled data are elaborated in this paper. The experiments are carried out about data acquisition, man-machine interaction, and functions of the rehabilitation training scenes. The results show that the active rehabilitation training system is able to assist patients with real-time interaction and immersive sensing and provides better visual feedback information to patients. It improves the training initiative as well as provides an effective means for nerve remodeling.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kawamoto, H.: Pilot study of locomotion improvement using hybrid assistive limb in chronic stroke patients. BMC Neurol. 13, 141–148 (2013). doi:10.1186/1471-2377-13-141
Wang, J.: Application and research progress of lower limb rehabilitation robot in stroke patients with walking disorder. Chin. J. Rehabil. Med. 29(8), 784–788 (2014). doi:10.3969/j.issn.1001-1242.2014.08.025
Chen, B.: Recent developments and challenges of lower extremity exoskeletons. J. Orthop. Transl. 5, 26–37 (2016). doi:10.1016/j.jot.2015.09.007
Guo, X.H.: Active and passive training system of lower limb rehabilitation based on virtual reality. J. Xi’an Jiaotong Univ. 50(2), 124–131 (2016). doi:10.7652/xjtuxb201602021
Meng, W.: Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation. Mechatronics 31, 132–145 (2015). doi:10.1016/j.mechatronics.2015.04.005
Liu, R.S.: Design and simulation of a lower limb rehabilitative robot. China Mech. Eng. 27(20), 2722–2727 (2016). doi:10.3969/j.issn.1004-132X.2016.20.005
Liu, H.L.: Effect of multi-position lower limb rehabilitation robot on motor function in stroke patients with hemiplegia. Chin. J. Rehabil. Theory Pract. 19(8), 722–724 (2013). doi:10.3969/j.issn.1006-9771.2013.08.003
Li, J.Q.: Research on exoskeleton remote rehabilitation system based on virtual reality technology. Mach. Des. Res. 27(4), 35–38 (2011)
Hassan, M.: Wearable gait measurement system with an instrumented cane for exoskeleton control. Sensors 14(1), 1705–1722 (2014). doi:10.3390/s140101705
Bergmann, J.H.: A portable system for collecting anatomical joint angles during stair ascent: a comparison with an optical tracking device. Dyn. Med. 8(3), 1–7 (2009). doi:10.1186/1476-5918-8-3
Zhang, J.F., Chen, Y., Yang, C.J.: Flexible Exoskeleton Human-Robot Intelligent System, 1st edn. Science Press, Beijing (2011)
Deng, X.: Wearable plantar pressure detecting system based on FSR. Transducer Microsyst. Technol. 32(2), 81–86 (2013). doi:10.3969/j.issn.1000-9787.2013.02.024
Cao, H.: Structure optimization analysis for exoskeleton foot. J. Eng. Des. 17(1), 35–39 (2010). doi:10.3785/j.issn.1006-754X.2010.01.006
Jin, Z.H.: Design of intelligent measurement and control system based on Bluetooth wireless interface. J. Donghua Univ. 30(1), 72–75 (2004). doi:10.3969/j.issn.1671-0444.2004.01.017
Vahe, K.: Introduction to Game Programming: Using C# and Unity 3D, 1st edn. Noorcon Inc., Los Angeles (2016)
Brunnstrom, S.: Motor testing procedures in hemiplegia: based on sequential recovery stages. Phys. Ther. 46(4), 357–375 (1966)
Acknowledgments
This study was supported by the “Research on key generic technologies of pneumatic gait rehabilitation training robot” project (172102210036) granted from “Project of science and technology of the Henan Province”, and the “Research on bionic driving mechanism and control strategy of non-skeletal waist power assisted robot” project (162300410082) granted from “Program for the Natural Science Foundation of Henan Province”.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Guo, B., Li, W., Han, J., Li, X., Mao, Y. (2017). Active Gait Rehabilitation Training System Based on Virtual Reality. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds) Intelligent Robotics and Applications. ICIRA 2017. Lecture Notes in Computer Science(), vol 10464. Springer, Cham. https://doi.org/10.1007/978-3-319-65298-6_46
Download citation
DOI: https://doi.org/10.1007/978-3-319-65298-6_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65297-9
Online ISBN: 978-3-319-65298-6
eBook Packages: Computer ScienceComputer Science (R0)