Continual Learning for Human State Monitoring
Federico Matteoni, Andrea Cossu, Claudio Gallicchio, Vincenzo Lomonaco, Davide Bacciu · 2022
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 domainincremental 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.* This work has been partially