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This content will become publicly available on January 31, 2026

Title: Sensor-Aware Data Imputation for Time-Series Machine Learning on Low-Power Wearable Devices
Wearable devices that have low-power sensors, processors, and communication capabilities are gaining wide adoption in several health applications. The machine learning algorithms on these devices assume that data from all sensors are available during runtime. However, data from one or more sensors may be unavailable due to energy or communication challenges. This loss of sensor data can result in accuracy degradation of the application. Prior approaches to handle missing data, such as generative models or training multiple classifiers for each combination of missing sensors are not suitable for low-energy wearable devices due to their high overhead at runtime. In contrast to prior approaches, we present an energy-efficient approach, referred to as Sensor-Aware iMputation (SAM), to accurately impute missing data at runtime and recover application accuracy. SAM first uses unsupervised clustering to obtain clusters of similar sensor data patterns. Next, it learns inter-relationship between clusters to obtain imputation patterns for each combination of clusters using a principled sensor-aware search algorithm. Using sensor data for clustering before choosing imputation patterns ensures that the imputation isawareof sensor data observations. Experiments on seven diverse wearable sensor-based time-series datasets demonstrate that SAM is able to maintain accuracy within 5% of the baseline with no missing data when one sensor is missing. We also compare SAM against generative adversarial imputation networks (GAIN), transformers, and k-nearest neighbor methods. Results show that SAM outperforms all three approaches on average by more than 25% when two sensors are missing with negligible overhead compared to the baseline.  more » « less
Award ID(s):
2238257
PAR ID:
10599925
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
Association for Computing Machinery
Date Published:
Journal Name:
ACM Transactions on Design Automation of Electronic Systems
Volume:
30
Issue:
1
ISSN:
1084-4309
Page Range / eLocation ID:
1 to 27
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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