Gated Adaptation for Continual Learning in Human Activity Recognition

ArXi:2603.10046v1 Announce Type: cross Wearable sensors in Internet of Things (IoT) ecosystems increasingly applications such as remote health monitoring, elderly care, and smart home automation, all of which rely on robust human activity recognition (HAR). Continual learning systems must balance plasticity (learning new tasks) with stability (retaining prior knowledge), yet AI models often exhibit catastrophic forgetting, where learning new tasks degrades performance on earlier ones.