Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes

40Citations
Citations of this article
45Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Smart homes based on the Internet of Things have been rapidly developed. To improve the safety, comfort, and convenience of residents' lives with minimal cost, daily activity recognition aims to know resident's daily activity in non-invasive manner. The performance of daily activity recognition heavily depends on solving strategy of activity feature. However, the current common employed solving strategy based on statistical information of individual activity does not support well the activity recognition. To improve the common employed solving strategy, an activity feature solving strategy based on TF-IDF is proposed in this paper. The proposed strategy exploits statistical information related to both individual activity and the whole of activities. Two distinct datasets have been commissioned, to mitigate against any possible effect of coupling between dataset and sensor configuration. Finally, a number of machine learning (ML) techniques and deep learning technique have been evaluated to assess their performance for residents activity recognition.

Cite

CITATION STYLE

APA

Guo, J., Mu, Y., Xiong, M., Liu, Y., Gu, J., & Garcia-Rodriguez, J. (2019). Activity Feature Solving Based on TF-IDF for Activity Recognition in Smart Homes. Complexity, 2019. https://doi.org/10.1155/2019/5245373

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free