Human Activity Recognition (HAR) on resource constrained wearables requires models that balance accuracy against strict memory and computational budgets. State of the art lightweight architectures such as TinierHAR (34K parameters) and TinyHAR (55K parameters) achieve strong accuracy, but exceed memory budgets of microcontrollers with limited SRAM once operating system overhead is considered. We present MicroBi-ConvLSTM, an ultra-lightweight convolutional-recurrent architecture achieving 11.4K parameters on average through two stage convolutional feature extraction with 4x temporal pooling and a single bidirectional LSTM layer. This represents 2.9x parameter reduction versus TinierHAR and 11.9x versus DeepConvLSTM while preserving linear O(N) complexity. Evaluation across eight diverse HAR benchmarks shows that MicroBi-ConvLSTM maintains competitive performance within the ultra-lightweight regime: 93.41% macro F1 on UCI-HAR, 94.46% on SKODA assembly gestures, and 88.98% on Daphnet gait freeze detection. Systematic ablation reveals task dependent component contributions where bidirectionality benefits episodic event detection, but provides marginal gains on periodic locomotion. INT8 post training quantization incurs only 0.21% average F1-score degradation, yielding a 23.0 KB average deployment footprint suitable for memory constrained edge devices.

翻译：在资源受限的可穿戴设备上进行人体活动识别，要求模型能在精度与严格的内存和计算预算之间取得平衡。诸如TinierHAR（3.4万参数）和TinyHAR（5.5万参数）等最先进的轻量级架构虽然实现了较高的精度，但一旦考虑操作系统开销，便会超出SRAM有限的微控制器的内存预算。我们提出了MicroBi-ConvLSTM，这是一种超轻量级的卷积-循环架构，它通过具有4倍时间池化的两阶段卷积特征提取和一个单层双向LSTM，平均实现了1.14万个参数。与TinierHAR相比，这代表了2.9倍的参数减少，与DeepConvLSTM相比则是11.9倍，同时保持了线性的O(N)复杂度。在八个不同的人体活动识别基准数据集上的评估表明，MicroBi-ConvLSTM在超轻量级体系中保持了有竞争力的性能：在UCI-HAR上达到93.41%的宏F1分数，在SKODA装配手势数据集上达到94.46%，在Daphnet步态冻结检测数据集上达到88.98%。系统性的消融实验揭示了任务依赖的组件贡献：双向性对片段性事件检测有益，但对周期性运动识别仅提供边际增益。INT8训练后量化仅导致平均F1分数下降0.21%，从而产生平均23.0 KB的部署占用空间，适合内存受限的边缘设备。