  Evolving consumer demands and market trends have led to businesses
increasingly embracing a production approach that prioritizes flexibility and
customization. Consequently, factory workers must engage in tasks that are more
complex than before. Thus, productivity depends on each worker's skills in
assembling products. Therefore, analyzing the behavior of a worker is crucial
for work improvement. However, manual analysis is time consuming and does not
provide quick and accurate feedback. Machine learning have been attempted to
automate the analyses; however, most of these methods need several labels for
training. To this end, we extend the Gaussian process hidden semi-Markov model
(GP-HSMM), to enable the rapid and automated analysis of worker behavior
without pre-training. The model does not require labeled data and can
automatically and accurately segment continuous motions into motion classes.
The proposed model is a probabilistic model that hierarchically connects
GP-HSMM and HSMM, enabling the extraction of behavioral patterns with different
granularities. Furthermore, it mutually infers the parameters between the
GP-HSMM and HSMM, resulting in accurate motion pattern extraction. We applied
the proposed method to motion data in which workers assembled products at an
actual production site. The accuracy of behavior pattern extraction was
evaluated using normalized Levenshtein distance (NLD). The smaller the value of
NLD, the more accurate is the pattern extraction. The NLD of motion patterns
captured by GP-HSMM and HSMM layers in our proposed method was 0.50 and 0.33,
respectively, which are the smallest compared to that of the baseline methods.
