Energy-Efficient Missing Data Recovery in Wearable Devices: A Novel Search-Based Approach

Wearable and internet of things (IoT) devices are transforming a number of high-impact applications. Machine learning (ML) algorithms on wearable devices assume that data from all sensors is available at runtime. However, one or more sensors may be unavailable at runtime due to malfunction, energy constraints or communication challenges. Loss of sensor data can potentially lead to severe degradation in application accuracy and quality of service. Commonly employed generative ML methods to recover missing data are not suitable for resource-constrained wearables because they incur significant memory, execution time, and energy overhead at runtime. In contrast to prior methods, this paper presents a novel search-based accuracy-preserving imputation (AIM) algorithm that obtains most likely imputation patterns of sensor data for each missing data scenario via offline analytics. Specifically, for each missing data condition, we store the most likely recovery patterns which preserve ML classifier-based application accuracy in a look up table and use it appropriately at runtime. The key insight behind AIM is that we do not need exact recovery of the missing data as long as the ML classifier-based application accuracy (e.g., health assessment) is preserved. To further improve the overall effectiveness of AIM, we train the ML classifiers to be robust to small errors in data recovery. Experiments on four diverse wearable sensor based time-series benchmarks demonstrate that AIM is able to maintain accuracy within 5% of the baseline with no missing data when one sensor is missing, and improves the overall accuracy by 15% compared to a state-of-the-art baseline. AIM achieves this improvement with negligible energy consumption overhead.