Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by assessing the reliability of a model's output. However, existing uncertainty estimation techniques often require substantial computational resources and memory, making them impractical for implementation on microcontrollers (MCUs). This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection. In this paper, we present UR2M, a novel Uncertainty and Resource-aware event detection framework for MCUs. Specifically, we (i) develop an uncertainty-aware WED based on evidential theory for accurate event detection and reliable uncertainty estimation; (ii) introduce a cascade ML framework to achieve efficient model inference via early exits, by sharing shallower model layers among different event models; (iii) optimize the deployment of the model and MCU library for system efficiency. We conducted extensive experiments and compared UR2M to traditional uncertainty baselines using three wearable datasets. Our results demonstrate that UR2M achieves up to 864% faster inference speed, 857% energy-saving for uncertainty estimation, 55% memory saving on two popular MCUs, and a 22% improvement in uncertainty quantification performance. UR2M can be deployed on a wide range of MCUs, significantly expanding real-time and reliable WED applications.

翻译：传统机器学习技术在面对训练与测试阶段数据分布变化时容易产生不准确的预测。这种脆弱性可能导致严重后果，尤其是在移动医疗等应用中。不确定性估计可通过评估模型输出的可靠性来缓解该问题。然而，现有不确定性估计技术通常需要大量计算资源和内存，难以在微控制器上实现。这一局限性阻碍了许多重要的可穿戴设备事件检测（如心脏病发作检测）应用的实际可行性。本文提出UR2M——一种面向微控制器的创新不确定性及资源感知事件检测框架。具体而言，我们：(i) 基于证据理论开发了不确定性感知的WED方法，以实现准确的事件检测和可靠的不确定性估计；(ii) 引入级联机器学习框架，通过在不同事件模型间共享较浅网络层实现基于早期退出的高效模型推理；(iii) 优化模型部署与微控制器库以提升系统效率。基于三个可穿戴数据集的实验表明，与经典不确定性基线方法相比，UR2M在两种主流微控制器上实现推理速度提升高达864%、不确定性估计能耗降低857%、内存节省55%，以及不确定性量化性能提升22%。该框架可部署于多种微控制器，显著扩展了实时可靠的可穿戴事件检测应用范围。