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Comparative Study of Machine Learning Algorithms for Activity Recognition with Data Sequence in Home-like Environment


Authors: X. Fan, H. Zhang, C. Leung, and C. Miao
Title: Comparative Study of Machine Learning Algorithms for Activity Recognition with Data Sequence in Home-like Environment
Abstract: Activity recognition is a key problem in multi-sensor systems. With data collected from different sensors, a multi-sensor system identifies activities performed by the inhabitants. Since an activity always lasts a certain duration, it is beneficial to use data sequence for the desired recognition. In this work, we experiment several machine learning techniques, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Meta-Layer Network for solving this problem. We observe that (1) compare with “single-frame” activity recognition, data sequence based classification gives better performance; and (2) directly using data sequence information with a simple “mete layer” network model yields a better performance than memory based deep learning approaches.
Keywords: 
Conference Name: 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI'16)
Location: Baden-Baden, Germany
Publisher: IEEE
Year: 2016
Accepted PDF File: Comparative_Study_of_Machine_Learning_Algorithms_for_Activity_Recognition_with_Data_Sequence_in_Home-like_Environment_accepted.pdf
Permanent Link: https://doi.org/10.1109/MFI.2016.7849484
Reference: X. Fan, H. Zhang, C. Leung, and C. Miao, “Comparative study of machine learning algorithms for activity recognition with data sequence in home-like environment,” in Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI’16). IEEE, September 2016, pp. 168–173.
bibtex: 
@inproceedings{LILY-c102, 
    author	= {Fan, Xiuyi and Zhang, Huiguo and Leung, Cyril and Miao, Chunyan},
    title	= {Comparative Study of Machine Learning Algorithms for Activity Recognition with Data Sequence in Home-like Environment},  
    booktitle	= {Proceedings of the 2016 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI'16)}, 
    year		= {2016}, 
    month	= {September}, 
    pages	= {168-173}, 
    location	= {Baden-Baden, Germany},
    publisher	= {IEEE},
 }