Hierarchical‐Split Multi‐Scale Convolution Network With Multi‐Task Learning for Human Activity Recognition in Wearable Devices

ABSTRACT Human Activity Recognition (HAR) using built‐in sensors in wearable devices offers valuable insights for continuous behaviour monitoring and health assessment. However, existing HAR approaches face several key challenges: (1) limited capacity to capture multi‐scale temporal dependencies; (2) difficulty in recognising low‐motion or static activities when relying solely on inertial sensors such as 3D accelerometers (3D‐ACC); and (3) insufficient utilisation of auxiliary physiological signals in conventional single‐task learning frameworks. To address these limitations, we propose a Hierarchical‐Split Multi‐Scale Convolutional Network with Multi‐Task Learning for wearable HAR. The proposed model integrates a hierarchical‐split convolutional block to efficiently extract both local and global temporal features. It further employs a channel‐wise attention mechanism to adaptively fuse photoplethysmography (PPG) and 3D‐ACC signals, enabling the model to capture complementary physiological dynamics and improve the recognition of subtle or low‐motion activities. In addition, we introduce a multi‐task learning framework with an auxiliary heart rate estimation task, which enhances physiological representation learning without requiring additional annotations, thereby improving model robustness and generalisability. Extensive experiments on two public HAR datasets demonstrate that our method consistently outperforms existing approaches under both overall and cross‐subject evaluation protocols, highlighting its effectiveness in realistic, user‐independent settings.