  Manual assembly workers face increasing complexity in their work.
Human-centered assistance systems could help, but object recognition as an
enabling technology hinders sophisticated human-centered design of these
systems. At the same time, activity recognition based on hand poses suffers
from poor pose estimation in complex usage scenarios, such as wearing gloves.
This paper presents a self-supervised pipeline for adapting hand pose
estimation to specific use cases with minimal human interaction. This enables
cheap and robust hand posebased activity recognition. The pipeline consists of
a general machine learning model for hand pose estimation trained on a
generalized dataset, spatial and temporal filtering to account for anatomical
constraints of the hand, and a retraining step to improve the model. Different
parameter combinations are evaluated on a publicly available and annotated
dataset. The best parameter and model combination is then applied to unlabelled
videos from a manual assembly scenario. The effectiveness of the pipeline is
demonstrated by training an activity recognition as a downstream task in the
manual assembly scenario.
