  Continual Learning (CL) on time series data represents a promising but
under-studied avenue for real-world applications. We propose two new CL
benchmarks for Human State Monitoring. We carefully designed the benchmarks to
mirror real-world environments in which new subjects are continuously added. We
conducted an empirical evaluation to assess the ability of popular CL
strategies to mitigate forgetting in our benchmarks. Our results show that,
possibly due to the domain-incremental properties of our benchmarks, forgetting
can be easily tackled even with a simple finetuning and that existing
strategies struggle in accumulating knowledge over a fixed, held-out, test
subject.
