nesl/auritus
Auritus: An Open-Source Optimization Toolkit for Training and Development of Human Movement Models and Filters Using Earables
This toolkit helps researchers and developers create and refine applications that use ear-worn devices (earables) to understand human movement. It takes raw sensor data from earables and motion capture systems, processes it, and outputs optimized machine learning models for tasks like activity recognition and head-pose tracking. This is ideal for those building new earable-based health monitoring, augmented reality, or spatial audio applications.
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Use this if you need to develop highly accurate, lightweight machine learning models for human movement using earable sensor data, especially for applications like fall detection or AR interfacing.
Not ideal if you are looking for a plug-and-play solution without custom data collection, model training, or hardware-in-the-loop optimization.
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22
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Language
C++
License
BSD-3-Clause
Category
Last pushed
Sep 17, 2024
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