Classification of Human Whole-Body Motion using Hidden Markov Models

TL;DR

This paper explores multi-label classification of human whole-body motion using Hidden Markov Models, introducing a novel direct prediction approach that scales efficiently with the number of labels and achieves high accuracy.

Contribution

It presents a new direct multi-label classification method with HMMs that avoids combinatorial complexity, improving scalability and performance.

Findings

System 1 achieves 98.02% accuracy.

System 2 achieves 93.39% accuracy.

The direct approach scales linearly with labels.

Abstract

Human motion plays an important role in many fields. Large databases exist that store and make available recordings of human motions. However, annotating each motion with multiple labels is a cumbersome and error-prone process. This bachelor's thesis presents different approaches to solve the multi-label classification problem using Hidden Markov Models (HMMs). First, different features that can be directly obtained from the raw data are introduced. Next, additional features are derived to improve classification performance. These features are then used to perform the multi-label classification using two different approaches. The first approach simply transforms the multi-label problem into a multi-class problem. The second, novel approach solves the same problem without the need to construct a transformation by predicting the labels directly from the likelihood scores. The second approach scales linearly with the number of labels whereas the first approach is subject to combinatorial explosion. All aspects of the classification process are evaluated on a data set that consists of 454 motions. System 1 achieves an accuracy of 98.02% and system 2 an accuracy of 93.39% on the test set.