Learning Datum-Wise Sampling Frequency for Energy-Efficient Human Activity Recognition

Authors

  • Weihao Cheng The University of Melbourne
  • Sarah Erfani The University of Melbourne
  • Rui Zhang The University of Melbourne
  • Ramamohanarao Kotagiri The University of Melbourne

Keywords:

Human Activity Recognition, Markov Decision Process, Sampling Frequency

Abstract

Continuous Human Activity Recognition (HAR) is an important application of smart mobile/wearable systems for providing dynamic assistance to users. However, HAR in real-time requires continuous sampling of data using built-in sensors (e.g., accelerometer), which significantly increases the energy cost and shortens the operating span. Reducing sampling rate can save energy but causes low recognition accuracy. Therefore, choosing adaptive sampling frequency that balances accuracy and energy efficiency becomes a critical problem in HAR. In this paper, we formalize the problem as minimizing both classification error and energy cost by choosing dynamically appropriate sampling rates. We propose Datum-Wise Frequency Selection (DWFS) to solve the problem via a continuous state Markov Decision Process (MDP). A policy function is learned from the MDP, which selects the best frequency for sampling an incoming data entity by exploiting a datum related state of the system. We propose a method for alternative learning the parameters of an activity classification model and the MDP that improves both the accuracy and the energy efficiency. We evaluate DWFS with three real-world HAR datasets, and the results show that DWFS statistically outperforms the state-of-the-arts regarding a combined measurement of accuracy and energy efficiency.

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Published

2018-04-26

How to Cite

Cheng, W., Erfani, S., Zhang, R., & Kotagiri, R. (2018). Learning Datum-Wise Sampling Frequency for Energy-Efficient Human Activity Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11862

Issue

Section

Main Track: Machine Learning Applications