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Locomotor Sub-functions for Control of Assistive Wearable Robots

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Date 2017 Sep 21
PMID 28928650
Citations 4
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Abstract

A primary goal of comparative biomechanics is to understand the fundamental physics of locomotion within an evolutionary context. Such an understanding of legged locomotion results in a transition from copying nature to borrowing strategies for interacting with the physical world regarding design and control of bio-inspired legged robots or robotic assistive devices. Inspired from nature, legged locomotion can be composed of three locomotor sub-functions, which are intrinsically interrelated: : redirecting the center of mass by exerting forces on the ground. : cycling the legs between ground contacts. : maintaining body posture. With these three sub-functions, one can understand, design and control legged locomotory systems with formulating them in simpler separated tasks. Coordination between locomotor sub-functions in a harmonized manner appears then as an additional problem when considering legged locomotion. However, biological locomotion shows that appropriate design and control of each sub-function simplifies coordination. It means that only limited exchange of sensory information between the different locomotor sub-function controllers is required enabling the envisioned modular architecture of the locomotion control system. In this paper, we present different studies on implementing different locomotor sub-function controllers on models, robots, and an exoskeleton in addition to demonstrating their abilities in explaining humans' control strategies.

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References
1.
Maus H, Lipfert S, Gross M, Rummel J, Seyfarth A . Upright human gait did not provide a major mechanical challenge for our ancestors. Nat Commun. 2010; 1:70. DOI: 10.1038/ncomms1073. View

2.
Gruben K, Boehm W . Force direction pattern stabilizes sagittal plane mechanics of human walking. Hum Mov Sci. 2011; 31(3):649-59. DOI: 10.1016/j.humov.2011.07.006. View

3.
Nilsson J, Thorstensson A, Halbertsma J . Changes in leg movements and muscle activity with speed of locomotion and mode of progression in humans. Acta Physiol Scand. 1985; 123(4):457-75. DOI: 10.1111/j.1748-1716.1985.tb07612.x. View

4.
Zhao G, Sharbafi M, Vlutters M, van Asseldonk E, Seyfarth A . Template model inspired leg force feedback based control can assist human walking. IEEE Int Conf Rehabil Robot. 2017; 2017:473-478. DOI: 10.1109/ICORR.2017.8009293. View

5.
Riese S, Seyfarth A, Grimmer S . Linear center-of-mass dynamics emerge from non-linear leg-spring properties in human hopping. J Biomech. 2013; 46(13):2207-12. DOI: 10.1016/j.jbiomech.2013.06.019. View