  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 State
Spaces Model 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. The model employs independent channels to process sensor data
streams, dividing each channel into patches and appending classification tokens
to the end of the sequence. It utilizes position embedding to represent the
sequence order. The patch sequence is subsequently processed by HARMamba Block,
and the classification head finally outputs the activity category. 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 4 publically available datasets namely PAMAP2, WISDM, UNIMIB SHAR
and UCI. The F1 scores of HARMamba on the four datasets are 99.74%, 99.20%,
88.23% and 97.01%, respectively.
