Wearable sensor-based human activity recognition (HAR) is a critical research domain in activity perception. However, achieving high efficiency and long sequence recognition remains a challenge. Despite the extensive investigation of temporal deep learning models, such as CNNs, RNNs, and transformers, their extensive parameters often pose significant computational and memory constraints, rendering them less suitable for resource-constrained mobile health applications. This study introduces HARMamba, an innovative light-weight and versatile HAR architecture that combines selective bidirectional SSM and hardware-aware design. To optimize real-time resource consumption in practical scenarios, HARMamba employs linear recursive mechanisms and parameter discretization, allowing it to selectively focus on relevant input sequences while efficiently fusing scan and recompute operations. To address potential issues with invalid sensor data, the system processes the data stream through independent channels, dividing each channel into "patches" and appending classification token to the end of the sequence. Position embeddings are incorporated to represent the sequence order, and the activity categories are output through a classification header. The HARMamba Block serves as the fundamental component of the HARMamba architecture, enabling the effective capture of more discriminative activity sequence features. HARMamba outperforms contemporary state-of-the-art frameworks, delivering comparable or better accuracy with significantly reducing computational and memory demands. It's effectiveness has been extensively validated on public datasets like PAMAP2, WISDM, UNIMIB SHAR and UCI, showcasing impressive results.

翻译：基于可穿戴传感器的人体活动识别（HAR）是活动感知领域的关键研究方向。然而，实现高效率和长序列识别仍是一大挑战。尽管卷积神经网络（CNN）、递归神经网络（RNN）及Transformer等时序深度学习模型已被广泛研究，但其参数数量庞大，往往带来显著的计算与内存限制，难以适用于资源受限的移动健康应用。本文提出HARMamba——一种创新轻量级且通用的HAR架构，融合了选择性双向状态空间模型（SSM）与硬件感知设计。为优化实际场景下的实时资源消耗，HARMamba采用线性递归机制与参数离散化策略，可在有效融合扫描与重计算操作的同时，选择性聚焦于相关输入序列。针对传感器数据潜在无效性问题，系统通过独立通道处理数据流，将各通道划分为“片段”（patch），并在序列末尾添加分类标记（classification token）。通过引入位置嵌入（position embedding）表征序列顺序，借助分类头（classification header）输出活动类别。HARMamba模块作为该架构的核心组件，能够有效捕获更具判别性的活动序列特征。实验表明，HARMamba在显著降低计算与内存需求的同时，实现了与现有最先进框架相当或更优的精度。其在PAMAP2、WISDM、UNIMIB SHAR及UCI等公开数据集上的广泛验证，展现了卓越性能。