Machar: A Frequency-Aware Mamba–Convolution Hybrid Architecture for Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) aims to classify human behaviors from large-scale sensor data. A key challenge is to achieve high recognition accuracy while maintaining low computational cost. Recent advances such as Mamba address this by enabling long-range dependency modeling with subquadratic computational complexity, thus achieving strong representational capacity at reduced cost. However, when directly applied to HAR tasks, lightweight Mamba-based backbones often underperform compared to conventional CNN and Transformer architectures. To investigate this gap, we perform detailed temporal and spectral analyses, revealing that Mamba exhibits an inherent bias towards low-frequency components. In contrast, HAR sensor signals typically comprise a mixture of both high- and low-frequency information, both of which are crucial for accurate activity recognition. To address this limitation, we propose Machar, a novel lightweight MAmba-Convolution Hybrid ARchitecture specifically designed for HAR. Instead of relying solely on global modeling, Machar introduces a dedicated FreqDecoupler that decomposes sensor signals into high- and low-frequency components, enabling each to be processed by the most appropriate mechanism. Furthermore, we propose a frequency scheduling strategy that dynamically adjusts channel capacity allocation across network stages, effectively combining the local feature extraction capability of CNNs with Mamba’s global modeling strength. Extensive experiments on three widely used HAR benchmarks, namely USC-HAD, UCI-HAR, and UniMiB-SHAR, show that Machar consistently outperforms existing methods, achieving impressive accuracy while preserving a favorable computational footprint, which underscore the effectiveness and scalability of Machar for real-world HAR applications.