HARMamba: Efficient Wearable Sensor Human Activity Recognition Based on
  Bidirectional Selective SSM
Shuangjian Li Tao Zhu Furong Duan Liming Chen Huansheng Ning Yaping Wan
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.