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J Nathan Kutz

As seen in: BioRxiv, AI Magazine
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Articles

Human motion data expansion from arbitrary sparse sensors with shallow recurrent decoders

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By Megan R Ebers, MacKenzie Pitts, Katherine M Steele, J Nathan Kutz
| BioRxiv
Abstract Advances in deep learning and sparse sensing have emerged as powerful tools for monitoring human motion in natural environments. We develop a deep learning architecture, constructed from a shallow recurrent decoder network, that expands human motion data by mapping a limited (sparse) number of sensors to a comprehensive (dense) configuration, thereby inferring the motion of unmonitored body segments.
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A machine learning approach to quantify individual gait responses to ankle exoskeletons

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By Michael Rosenberg Verified, Katherine M Steele, Megan R Ebers, J Nathan Kutz
| BioRxiv
Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website.
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