Wearable sensor human activity recognition (HAR) is a crucial area of
research in activity sensing. While transformer-based temporal deep learning
models have been extensively studied and implemented, their large number of
parameters present significant challenges in terms of system computing load and
memory usage, rendering them unsuitable for real-time mobile activity
recognition applications. Recently, an efficient hardware-aware state space
model (SSM) called Mamba has emerged as a promising alternative. Mamba
demonstrates strong potential in long sequence modeling, boasts a simpler
network architecture, and offers an efficient hardware-aware design. Leveraging
SSM for activity recognition represents an appealing avenue for exploration. In
this study, we introduce HARMamba, which employs a more lightweight selective
SSM as the foundational model architecture for activity recognition. The goal
is to address the computational resource constraints encountered in real-time
activity recognition scenarios. Our approach involves processing sensor data
flow by independently learning each channel and segmenting the data into
"patches". The marked sensor sequence's position embedding serves as the input
token for the bidirectional state space model, ultimately leading to activity
categorization through the classification head. Compared to established
activity recognition frameworks like Transformer-based models, HARMamba
achieves superior performance while also reducing computational and memory
overhead. Furthermore, our proposed method has been extensively tested on four
public activity datasets: PAMAP2, WISDM, UNIMIB, and UCI, demonstrating
impressive performance in activity recognition tasks.

本研究介绍了一种更轻量级、选择性的状态空间模型为基础的活动识别模型 HARMamba，通过独立学习每个通道的传感器数据并将数据分割成 “补丁”，标记出的传感器序列的位置嵌入作为双向状态空间模型的输入令牌，最终通过分类头实现活动分类，相比于基于 Transformer 的模型，在减少计算和内存开销的同时实现了更好的性能，并在 PAMAP2、WISDM、UNIMIB 和 UCI 等四个公共活动数据集上进行了广泛测试，展示了令人印象深刻的活动识别性能。