BabyMamba-HAR: Lightweight Selective State Space Models for Efficient Human Activity Recognition on Resource Constrained Devices

Human activity recognition (HAR) on wearable and mobile devices is constrained by memory footprint and computational budget, yet competitive accuracy must be maintained across heterogeneous sensor configurations. Selective state space models (SSMs) offer linear time sequence processing with input dependent gating, presenting a compelling alternative to quadratic complexity attention mechanisms. However, the design space for deploying SSMs in the TinyML regime remains largely unexplored. In this paper, BabyMamba-HAR is introduced, a framework comprising two novel lightweight Mamba inspired architectures optimized for resource constrained HAR: (1) CI-BabyMamba-HAR, using a channel independent stem that processes each sensor channel through shared weight, but instance independent transformations to prevent cross channel noise propagation, and (2) Crossover-BiDir-BabyMamba-HAR, using an early fusion stem that achieves channel count independent computational complexity. Both variants incorporate weight tied bidirectional scanning and lightweight temporal attention pooling. Through evaluation across eight diverse benchmarks, it is demonstrated that Crossover-BiDir-BabyMamba-HAR achieves 86.52% average macro F1-score with approximately 27K parameters and 2.21M MACs, matching TinyHAR (86.16%) while requiring 11x fewer MACs on high channel datasets. Systematic ablation studies reveal that bidirectional scanning contributes up to 8.42% F1-score improvement, and gated temporal attention provides up to 8.94% F1-score gain over mean pooling. These findings establish practical design principles for deploying selective state space models as efficient TinyML backbones for HAR.