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XoSoft Connected Monitor (XCM) Unsupervised Monitoring and Feedback in Soft Exoskeletons of 3D Kinematics, Kinetics, Behavioral Context and Control System Status

  • Chris T. M. Baten
  • Wiebe de Vries
  • Leendert Schaake
  • Juryt Witteveen
  • Daniel Scherly
  • Konrad Stadler
  • Andres Hidalgo Sanchez
  • Eduardo Rocon
  • Danny Plass-Oude Bos
  • Jeroen Linssen
Conference paper
Part of the Biosystems & Biorobotics book series (BIOSYSROB, volume 22)

Abstract

Intelligent soft exoskeletons are developed to be used unsupervised and continuously on a large scale in normal daily situations. As they miss the stiffness of the structural components of traditional robotic devices, traditional robotic movement assessment are rendered useless, as they assume structural segment rigidity. This all requires a radical different approach towards (remote) monitoring and feedback of data relevant to a host of different type users: clinicians and therapists responsible for training and well-being of patient, caregivers, maintenance technicians and even the exoskeleton’s control system. This paper proposes such a system, one implementation of which is developed and tested within the XoSoft soft exoskeleton project. It provides continuous remote (partly IMMU based) assessment of 3D kinematics and kinetics, control system activity, subject activity pattern and derived movement pattern parameters. It also is structured in a maximally flexible way facilitating the ever-shifting, optimal distribution of functional software modules over more peripheral and central hardware to accommodate for fast changes in specifications and technical and practical constraints.

References

  1. 1.
    Baten, C.T.M., van der Aa, R., Verkuyl, A.: Effect of wearable trunk support for working in sustained stooped posture on low back net extension moments. In: International Conference on ISB, Glasgow (2015)Google Scholar
  2. 2.
    Baten, C.T.M.: Advancements in sensor-based ambulatory 3D motion analysis. J. Biomech. 40, S422–S422 (2007)CrossRefGoogle Scholar
  3. 3.
    Recher, F., Banos, O., Nikamp, C., Schaake, L., Baten, C., Buurke, J.: Optimizing activity recognition in stroke survivors for wearable exoskeletons. In: 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (2018)Google Scholar
  4. 4.
    Wassink, R.G.V., Baten, C.T.M.: Classifying human lifting activities automatically by applying Hidden Markov Modeling technology. J. Biomech. 40, S428 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chris T. M. Baten
    • 1
  • Wiebe de Vries
    • 1
  • Leendert Schaake
    • 1
  • Juryt Witteveen
    • 1
  • Daniel Scherly
    • 2
  • Konrad Stadler
    • 1
  • Andres Hidalgo Sanchez
    • 3
  • Eduardo Rocon
    • 3
  • Danny Plass-Oude Bos
    • 4
  • Jeroen Linssen
    • 4
  1. 1.Roessingh Research and DevelopmentEnschedeThe Netherlands
  2. 2.ZHAWWinterthurSwitzerland
  3. 3.CSICMadridSpain
  4. 4.Saxion University of Applied SciencesEnschedeThe Netherlands

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