DWOSC: Dynamic Weight Optimization and Smoothness Constraint for Sensor-Based Human Activity Recognition

The sensor-based human activity recognition (SHAR) task aims to detect and analyze signals captured by various sensors embedded in intelligent devices to assist people's daily lives, which inherently have variations, spatial inconsistency, and noise characteristics. Inspired by the success of deep learning, many researchers have designed various neural network architectures to improve the SHAR model's performance. Albeit effective, these manners are not optimal due to ignoring the imbalance and difficulty of interclass and intraclass of sensor inherently characteristic, which proposes the challenge for resource-constrained devices. To address this challenge, we present a simple and effective optimization strategy for sensor signal processing, including dynamic weight optimization (DWO) and smoothness constraint (SC) strategies, termed DWOSC, which provide a framework to optimize loss function and enhance activity recognition performance. The DWO aims to address the class imbalance and hard-to-recognize samples. The SC seeks to maintain the smoothness of decision boundaries for complex samples. This way, the proposed approach can achieve a more stable convergence of the model and further improve the SHAR task's performance without raising complexity. Extensive experiments conducted on three benchmark SHAR datasets, e.g., OPPORTUNITY, University of Southern California Human Activity Dataset (USC-HAD), and UniMib, demonstrate the superiority of our method over the deep learning baselines and existing SHAR works.