Timing-Aware and Interpretable Self-Attention for Event-Based Wearable Task Transition Detection
Task transition detection aims to identify when a person shifts from one task to another, providing a foundation for continuous health monitoring and timely context-aware interventions. While statistical and deep feature representations have demonstrated strong classification performance, they often rely on aggregated or latent features that are difficult to interpret in behavioural terms. Event-based features provide a compact and interpretable alternative by encoding behavioural and physiological patterns as discrete tokens with explicit semantic meaning, making them well suited for behavioural analysis in health informatics. Self-attention offers a mechanism for modelling interactions ...
| Authors | Anda Ouyang, Siyuan Chen, Julien Epps |
|---|---|
| Primary category | cs.LG |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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