[3D1-GS-9] Human interface:

[3D1-GS-9-01] Analysis of Latent Behavioral Individuality Based on Gaussian Process-Hidden Semi-Markov Model

〇Toshiyuki Hatta^1,2, Shintaro Watanabe², Issei Saito¹, Masatoshi Nagano¹, Tomoaki Nakamura¹ (1. The University of Electro-Communications, 2. Advanced Technology R&D Center, Mitsubishi Electric Corporation)

Keywords:Segmentation, Unsupervised Learning, Gaussian Process-Hidden Semi-Markov Model

To realize robots and intelligent systems that comprehend human behavior, it is essential to segment and classify target behaviors in an unsupervised manner. However, conventional unsupervised segmentation methods do not account for individual differences, resulting in reduced segmentation accuracy when behaviors vary among individuals. In this paper, to address this issue, we propose the LIC-GP-HSMM (Latent Individuality Conditioned Gaussian Process-Hidden Semi-Markov Model), an extension of the GP-HSMM known for its high accuracy in previous unsupervised segmentation studies. The LIC-GP-HSMM introduces latent variables (latent individuality vectors) that represent behavioral individuality into the GP-HSMM. These latent individuality vectors can be inferred in a manner equivalent to the GPLVM (Gaussian Process Latent Variable Model). In the experiments, using synthetic data that simulates behaviors with individual differences, we demonstrate that the proposed method enables more accurate segmentation than GP-HSMM. Furthermore, we show that the inferred latent individuality vectors effectively represent the individuality of behaviors.