Robust Human Activity Recognition using Generative Adversarial Imputation Networks
Dina Hussein1,a, Aaryan Jain2 and Ganapati Bhat1,b
1School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA, 99164
adina.hussein@wsu.edu
bganapati.bhat@wsu.edu
2Tesla STEM High School, Redmond, WA, 98052
jain_aaryan@outlook.com
ABSTRACT
Human activity recognition (HAR) is widely used in applications ranging from activity tracking to rehabilitation of patients. HAR classifiers are typically trained with data collected from a known set of users while assuming that all the sensors needed for activity recognition are working perfectly and there are no missing samples. However, real-world usage of the HAR classifier may encounter missing data samples due to user error, device error, or battery limitations. The missing samples, in turn, lead to a significant reduction in accuracy. To address this limitation, we propose an adaptive method that either uses low-power mean imputation or generative adversarial imputation networks (GAIN) to recover the missing data samples before classifying the activities. Experiments on a public HAR dataset with 22 users show that the proposed robust HAR classifier achieves 94% classification accuracy with as much as 20% missing samples from the sensors with 390 µJ energy consumption per imputation.
