Personalization in Human Activity Recognition

TL;DR

This paper investigates how personal physical characteristics and signal similarities can enhance human activity recognition accuracy, addressing diversity challenges in populations with varying physical traits.

Contribution

It introduces a method that leverages individual physical features and signal similarity to improve activity recognition over traditional deep learning classifiers.

Findings

Improved recognition accuracy using personalized features

Enhanced performance in diverse populations

Potential reduction in training data requirements

Abstract

In the recent years there has been a growing interest in techniques able to automatically recognize activities performed by people. This field is known as Human Activity recognition (HAR). HAR can be crucial in monitoring the wellbeing of the people, with special regard to the elder population and those people affected by degenerative conditions. One of the main challenges concerns the diversity of the population and how the same activities can be performed in different ways due to physical characteristics and life-style. In this paper we explore the possibility of exploiting physical characteristics and signal similarity to achieve better results with respect to deep learning classifiers that do not rely on this information.