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Title: Distilling the knowledge from handcrafted features for human activity recognition
Authors: Chen, Zhenghua
Zhang, Le
Cao, Zhiguang
Guo, Jing
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Human Activity Recognition
Deep Long Short-term Memory (LSTM) Network
Issue Date: 2018
Source: Chen, Z., Zhang, L., Cao, Z., & Guo, J. (2018). Distilling the Knowledge From Handcrafted Features for Human Activity Recognition. IEEE Transactions on Industrial Informatics, 14(10), 4334-4342. doi:10.1109/TII.2018.2789925
Series/Report no.: IEEE Transactions on Industrial Informatics
Abstract: Human activity recognition is a core problem in intelligent automation systems due to its far-reaching applications including ubiquitous computing, health-care services, and smart living. Due to the nonintrusive property of smartphones, smartphone sensors are widely used for the identification of human activities. However, unlike applications in vision or data mining domain, feature embedding from deep neural networks performs much worse in terms of recognition accuracy than properly designed handcrafted features. In this paper, we posit that feature embedding from deep neural networks may convey complementary information and propose a novel knowledge distilling strategy to improve its performance. More specifically, an efficient shallow network, i.e., single-layer feedforward neural network (SLFN), with handcrafted features is utilized to assist a deep long short-term memory (LSTM) network. On the one hand, the deep LSTM network is able to learn features from raw sensory data to encode temporal dependencies. On the other hand, the deep LSTM network can also learn from SLFN to mimic how it generalizes. Experimental results demonstrate the superiority of the proposed method in terms of recognition accuracy against several state-of-the-art methods in the literature.
ISSN: 1551-3203
DOI: 10.1109/TII.2018.2789925
Schools: School of Electrical and Electronic Engineering 
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
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