Continual Learning for Human State Monitoring

• URL: http://arxiv.org/abs/2207.00010v1
• Date: Wed, 29 Jun 2022 19:23:13 GMT
• Title: Continual Learning for Human State Monitoring
• Authors: Federico Matteoni, Andrea Cossu, Claudio Gallicchio, Vincenzo Lomonaco, Davide Bacciu
• Abstract summary: 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.
• Score: 20.8311956676327
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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