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Surabhi Simha proslijedio/la je Tweet
'Taking advantage of external mechanical work to reduce metabolic cost: the mechanics and energetics of split‐belt treadmill walking' from
@nat_san_ald@simha_surabhi@maxdonelan@jamesmfinley@USC@uscbknpt@SFU#locomotion#motorlearning https://buff.ly/2ZpzaVD pic.twitter.com/NSprsqQvih
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It looks like my first attempt at tweeting produced two half threads. So, I'm going to try again! Aptly, that's one of the main lessons I learnt when building this system. Here's the link to connect to the second half of the thread.https://twitter.com/simha_surabhi/status/1143958273143345152 …
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We have some plans for future studies with this system but would also love to hear your thoughts. Thanks for reading!
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Finally, satisfied with our system, protocol, and analysis design, we had 8 people experience a cost landscape and protocol similar to what Jess used in the study with the exos. And we found that people shifted their preferred step frequency towards the new energy minimum!pic.twitter.com/tV9cY67t9Y
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In analyzing pilot data, we entered another detour - how to be confident of the minimum of a shallow cost landscape obtained from noisy metabolic measures, esp. for a single person? We present some interesting methods in the paper.
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We built a deck around the treadmill, gave users noise cancellation headphones and dribble goggles, and fine tuned our verbal instructions. I also pestered all of Max's visitors David Remy,
@manojmovement@Ferris_vball Art Kuo@StevenHCollins for insights. Sorry, and thanks
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But we recovered well, and actually had a bunch of people (not Max) walk in the system, to watch them adapt. This was the trickiest bit, causing us to make modifications to improve user comfort and confidence in the system - aspects not part of the technical specs.
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Max’s response - “This is really good! Don’t get used to such results”. But in fact, Max was so psyched that he wanted to show the system to Tim Carroll who was visiting us then. Turns out, one quick step for Max is one broken system for me.
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We had one user walk at a range of step frequencies and found that we could create cost landscapes where people could reduce their energetic costs by ~50% or increase by 230% depending on the gait they chose. As predicted by our design.pic.twitter.com/pdHstzcsPm
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Ford to Tesla, as
@RodgerKram put it (Rodger is Max’s PhD supervisor). On left is the figure from@jingerfitness and@RodgerKram's paper from 2002. Right is the figure from our paper.pic.twitter.com/0baVQobPOy
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We built the physical system using IMUs placed on user's feet to measure step frequency, a dSPACE board for real-time control of forces and a Yobotics series elastic actuator
@jerryepratt built for us, to apply the forces. We measure energetic cost with a Vmax metabolic cart.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
To design our system, we used data collected by
@jingerfitness and@RodgerKram on the energy people expend to walk at any constant horizontal force. We added this to the data@BrianUmberger gave us about the energy people expend to walk at any constant step frequency.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The system measures your gait and, in real-time, uses horizontal fore-aft forces applied to the hip to manipulate the energy you expend to execute that gait. In this paper, we use only step frequency to characterize a gait while the user walks at a constant speed.
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To address these, we built a mechatronic system that quickly, accurately, and precisely targets specific changes in energetic cost, including a wide range of rewards and penalties, as a function of measured gait.
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But how are people's nervous systems sensing energy changes and finding minima? What are the limits to this, if any? Can we do anything to better help the nervous system find these minima?
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In 2015, Jess Selinger observed that people can continuously adapt their walking gait to changes in energetic minima. She commanded knee joint torques to an exoskeleton that changed as a function of step frequency, and watched people adapt.
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Thanks
@TNSRE1 for publishing my first paper https://ieeexplore.ieee.org/document/8717707 … … and tweeting about it! Also thanks to@maxdonelan for well rounded supervision - he convinced me (long-time silent twitter user) to write this tweprint. Fun science and stories in thread below!Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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