<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Machine Learning |</title><link>https://aroraanshu26.github.io/tags/machine-learning/</link><atom:link href="https://aroraanshu26.github.io/tags/machine-learning/index.xml" rel="self" type="application/rss+xml"/><description>Machine Learning</description><generator>HugoBlox Kit (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sat, 26 Apr 2025 00:00:00 +0000</lastBuildDate><image><url>https://aroraanshu26.github.io/media/icon_hu_da05098ef60dc2e7.png</url><title>Machine Learning</title><link>https://aroraanshu26.github.io/tags/machine-learning/</link></image><item><title>EMG Controlled Prosthetic Arm</title><link>https://aroraanshu26.github.io/projects/pandas/</link><pubDate>Sat, 26 Apr 2025 00:00:00 +0000</pubDate><guid>https://aroraanshu26.github.io/projects/pandas/</guid><description>&lt;p&gt;Prosthetic arm that can recoganize three gestures - pinch, grasp and release from EMG signals. I focussed on the machine learning model to classify the different guestures, and coding the microcontroller.&lt;/p&gt;
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&lt;p&gt;Everything in this video (except for the laptop, electrodes and OpenBCI module) has been made in house! The hand is 3D printed and the PCB was printed at IITB. Notice how the probabilities of different motions (seen in red, green and blue on the computer screen) fluctuate as the motion is changed!&lt;/p&gt;</description></item></channel></rss>