Fuzzy human motion analysis: A review

TL;DR

This review paper discusses how fuzzy set theory enhances human motion analysis (HMA) by addressing uncertainties, categorizing approaches into low, mid, and high-level analysis, and explores future research directions.

Contribution

It provides the first comprehensive review of fuzzy set approaches in HMA, classifying methods across different analysis levels and outlining future perspectives.

Findings

Fuzzy set theory improves robustness in HMA applications.

Classification of fuzzy approaches into three analysis levels.

Identifies gaps and future directions in fuzzy HMA research.

Abstract

Human Motion Analysis (HMA) is currently one of the most popularly active research domains as such significant research interests are motivated by a number of real world applications such as video surveillance, sports analysis, healthcare monitoring and so on. However, most of these real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, the fuzzy set theory has been applied and showed great success in the recent past. In this paper, we aim at reviewing the fuzzy set oriented approaches for HMA, individuating how the fuzzy set may improve the HMA, envisaging and delineating the future perspectives. To the best of our knowledge, there is not found a single survey in the current literature that has discussed and reviewed fuzzy approaches towards the HMA. For ease of understanding, we conceptually classify the human motion into three broad levels: Low-Level (LoL), Mid-Level (MiL), and High-Level (HiL) HMA.