Time series classification underpins applications such as human activity recognition, healthcare monitoring, and gesture detection in the IoT domain. Tiny Machine Learning enables models to run directly on low-power microcontroller units, improving efficiency, ensuring privacy, and reducing cost by avoiding reliance on cloud or edge computing. While Long Short-Term Memory networks are widely used for capturing temporal dependencies, their high computational and memory demands make real-time MCU deployment impractical. In this work, we conduct a hardware-aware feasibility study of LSTM versus 1D Convolutional Neural Networks across five benchmark datasets. Results show that 1D-CNN consistently achieves comparable or higher accuracy around 95% than LSTM which is around 89%, while requiring 35% less RAM, approx. 25% less Flash, and enabling real-time inference that is 27.6 ms vs. 2038 ms. Being so lightweight, 1D-CNN is particularly suitable for on-device processing in wearables and other low-power, battery-operated systems, establishing it as a practical and resource-efficient choice for TinyML deployment.

翻译：时间序列分类是物联网领域中人体活动识别、健康监测和手势检测等应用的基础。微型机器学习使模型能够直接在低功耗微控制器单元上运行，通过避免依赖云或边缘计算来提高效率、确保隐私并降低成本。虽然长短期记忆网络被广泛用于捕捉时序依赖关系，但其高昂的计算和内存需求使得在MCU上实时部署变得不切实际。在本研究中，我们针对LSTM与一维卷积神经网络在五个基准数据集上进行了硬件感知的可行性研究。结果表明，1D-CNN在保持约95%的准确率（LSTM约为89%）的同时，持续达到相当或更高的精度，且所需RAM减少35%，Flash占用降低约25%，并实现27.6毫秒相对于2038毫秒的实时推理能力。凭借其轻量级特性，1D-CNN特别适用于可穿戴设备及其他低功耗电池供电系统的端侧处理，这使其成为TinyML部署中实用且资源高效的选择。