Efficient machine learning for motion sensing for lighting applications

TL;DR

This paper introduces an efficient machine learning approach for motion sensing in lighting applications, focusing on skewed data, optimization, and embedded system suitability.

Contribution

It presents a novel method using a customized loss function and probability models to improve accuracy and efficiency in motion sensing classification.

Findings

Effective handling of skewed datasets

Reduced complexity of probability models

Automated and efficient machine learning process

Abstract

The use of machine learning for building a classifier in signal processing for motion sensing presents unique challenges. This paper proposes a novel method that effectively addresses the combination of skewed data sets and optimization requirements. By utilizing a customized loss function and a product of probability models, our approach achieves a fully automated and efficient machine learning process. Additionally, our resulting probability models offer reduced complexity, making them ideal for embedded applications. Our method offers a promising solution for motion sensing applications that require accurate and efficient classification.