2021
DOI: 10.1088/2516-1091/abe3e0
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Closing the loop between wearable technology and human biology: A new paradigm for steering neuromuscular form and function

Abstract: Wearable technologies such as bionic limbs, robotic exoskeletons and neuromodulation devices have long been designed with the goal of enhancing human movement. However, current technologies have shown only modest results in healthy individuals and limited clinical impact. A central element hampering progress is that wearable technologies do not interact directly with tissues in the composite neuromuscular system. That is, current wearable systems do not take into account how biological targets (e.g., joints, t… Show more

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Cited by 15 publications
(12 citation statements)
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References 129 publications
(250 reference statements)
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“…We disruptive AI-driven companies taking similar marketing-heavy paths [218,283,545,730]. [673,707,758,890], but with collaborative interest from sports scientists, the models developed e.g. with OpenSIM-RL [404] could be easily retrained for athlete populations.…”
Section: Discussionmentioning
confidence: 99%
“…We disruptive AI-driven companies taking similar marketing-heavy paths [218,283,545,730]. [673,707,758,890], but with collaborative interest from sports scientists, the models developed e.g. with OpenSIM-RL [404] could be easily retrained for athlete populations.…”
Section: Discussionmentioning
confidence: 99%
“…This provides new tools to determine the relative weight of the different components in the neuromusculoskeletal system, that determine the final motor output (Sartori et al, 2017). As previously reported, sensory information is especially difficult to harvest in intact moving humans, and physiologically correct, person-specific neuro-mechanical simulations represent a viable contribution to advance toward more complete hypotheses on how the central nervous system control physical movement in a closed-loop fashion (Sartori and Sawicki, 2021).…”
Section: Modelingmentioning
confidence: 99%
“…However, its use is mostly limited to isometric contractions or slow dynamic contractions, mainly due to computational challenges related to the assumption that motor units are stationary and real-time implementation of the method. Both model-free AI (e.g., machine and deep learning techniques) (Chen et al, 2020 ; Clarke et al, 2020 ) and model-based techniques (e.g., data-driven mechanistic modeling) (Sartori and Sawicki, 2021 ) are explored to enable real-time implementation, which would allow mechanical and neural adaptations to exoskeleton training and neurostimulation to be predicted.…”
Section: Interfacing With the Central Nervous Systemmentioning
confidence: 99%