  The use of skeletal data allows deep learning models to perform action
recognition efficiently and effectively. Herein, we believe that exploring this
problem within the context of Continual Learning is crucial. While numerous
studies focus on skeleton-based action recognition from a traditional offline
perspective, only a handful venture into online approaches. In this respect, we
introduce CHARON (Continual Human Action Recognition On skeletoNs), which
maintains consistent performance while operating within an efficient framework.
Through techniques like uniform sampling, interpolation, and a memory-efficient
training stage based on masking, we achieve improved recognition accuracy while
minimizing computational overhead. Our experiments on Split NTU-60 and the
proposed Split NTU-120 datasets demonstrate that CHARON sets a new benchmark in
this domain. The code is available at https://github.com/Sperimental3/CHARON.
