Personalized Human Activity Recognition Using Convolutional Neural Networks

Authors

  • Seyed Ali Rokni Washington State University
  • Marjan Nourollahi Washington State University
  • Hassan Ghasemzadeh Washington State University

Keywords:

Activity Recognition, Deep Learning, Ubiquitous computing

Abstract

A major barrier to the personalized Human Activity Recognition using wearable sensors is that the performance of the recognition model drops significantly upon adoption of the system by new users or changes in physical/behavioral status of users. Therefore, the model needs to be retrained by collecting new labeled data in the new context. In this study, we develop a transfer learning framework using convolutional neural networks to build a personalized activity recognition model with minimal user supervision.

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Published

2018-04-29

How to Cite

Rokni, S. A., Nourollahi, M., & Ghasemzadeh, H. (2018). Personalized Human Activity Recognition Using Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12185