Improved Deep Representation Learning for Human Activity Recognition using IMU Sensors

Abstract

The paper proposes an improved representation learning framework for human activity classification using IMU sensors, namely accelerometer and gyroscope. In practical deployment of the IMU-based activity classification the system is expected to encounter variations in data due to sensor degradation, alien environment or sensor noise and will be subjected to unknown activities. To address these issues pertaining to open world classification, in this paper we propose a novel Bayesian inference framework that uses variational embedding model to predict the activity class, followed by tracking through Kalman filter to smoothen these embedding vector, which is then fed into linear classifier for predicting the activity class. We evaluate the performance of our novel Bayesian inference framework on IMU activity classification and demonstrate that the classification accuracy, clustering scores, and the unknown class rejection performance improves substantially compared to its counter-part embedding model.