IC-GP-HSMM: Unsupervised Segmentation of Behaviors with Individual Differences

Toshiyuki Hatta, Issei Saito, Masatoshi Nagano, Tomoaki Nakamura

Abstract: Robots and intelligent systems that understand and support human behavior are increasingly used in various settings, including public facilities, homes, offices, and manufacturing sites. To effectively understand human behavior, such systems must accurately segment and classify behaviors. Unsupervised learning, which does not require labeled data, is crucial for this task, as manually labeling every behavior in advance is challenging. Conventional unsupervised segmentation methods typically assume that instances of the same behavior share uniform characteristics. However, this assumption can lead to inaccurate segmentation when individual differences are present. To address this limitation, we propose the individuality-conditioned Gaussian process-hidden semi-Markov model (IC-GP-HSMM), an extension of the GP-HSMM. The original GP-HSMM uses unsupervised learning to model behavioral patterns as Gaussian processes based on observed sequences. Our extension assumes that information regarding individuals performing the behaviors is observable and incorporates this information as an individuality vector. This enables the model to learn behavior-specific Gaussian processes that consider individual variation, resulting in more accurate segmentation. Experiments conducted on synthetic and motion capture datasets demonstrate that IC-GP-HSMM outperforms conventional methods in segmenting behaviors with individual differences. The proposed IC-GPHSMM enables intelligent systems to more accurately recognize and adapt to individual variations in human behavior, enhancing their reliability and effectiveness in real-world applications.

External IDs:dblp:conf/iecon/HattaSNN25