  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.
