<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>JPL BLOG</title><link>https://blog.jpl.design/posts/f19/machine-learning-for-the-arts/</link><description>Recent content on JPL BLOG</description><generator>Hugo -- gohugo.io</generator><atom:link href="https://blog.jpl.design/posts/f19/machine-learning-for-the-arts/index.xml" rel="self" type="application/rss+xml"/><item><title>Handy</title><link>https://blog.jpl.design/posts/f19/machine-learning-for-the-arts/handy/</link><pubDate>Mon, 18 Nov 2019 00:00:00 +0000</pubDate><guid>https://blog.jpl.design/posts/f19/machine-learning-for-the-arts/handy/</guid><description>Handy is a wearable interface interpreting tangible interaction to intangible experiences.
Proposal # When we move, a lot more is happening than what can be explicitly observed: signals coming out of the brain, and converted into electrical inputs muscle can understand and execute. The signal that we can record on our skin is Electromyography (EMG). According to former research, EMG data can be collected and analyzed for the identification of certain movements.</description></item></channel></rss>