Continual Learning for Human State Monitoring
This work addresses continual learning challenges in human state monitoring, but it is incremental as it focuses on benchmarking rather than novel method development.
The authors tackled the problem of continual learning on time series data for human state monitoring by proposing two new benchmarks that simulate real-world scenarios with continuously added subjects, and found that forgetting is easily mitigated with simple finetuning while existing strategies struggle to accumulate knowledge on a fixed test subject.
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.