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Title
Japanese: 
English:User Adaptation of Convolutional Neural Network for Human Activity Recognition 
Author
Japanese: Shinya Matsui, 井上 中順, Yuko Akagi, Goshu Nagino, 篠田 浩一.  
English: Shinya Matsui, Nakamasa Inoue, Yuko Akagi, Goshu Nagino, Koichi Shinoda.  
Language English 
Journal/Book name
Japanese: 
English:2017 25th European Signal Processing Conference (EUSIPCO) 
Volume, Number, Page         pp. 753-757
Published date Oct 26, 2017 
Publisher
Japanese: 
English: 
Conference name
Japanese: 
English:European Signal Processing Conference (EUSIPCO) 
Conference site
Japanese: 
English:the kos Island 
File
Official URL https://www.eusipco2017.org/
 
DOI http://dx.doi.org/10.23919/EUSIPCO.2017.8081308
Abstract Abstract—Recently, monitoring human activities using smartphone sensors, such as accelerometers, magnetometers, and gyroscopes, has been proved effective to improve productivity in daily work. Since human activities differ largely among individuals, it is important to adapt their model to each individual with a small amount of his/her data. In this paper, we propose a user adaptation method using Learning Hidden Unit Contributions (LHUC) for Convolutional Neural Networks (CNN). It inserts a special layer with a small number of free parameters between each of two CNN layers and estimates the free parameters using a small amount of data. We collected smartphone data of 43 hours from 9 users and utilized them to evaluate our method. It improved the recognition performance by 3.0% from a userindependent model on average. The largest improvement among users was 13.6%. Index Terms—Human activity recognition, User adaptation, Convolutional neural network, Learning hidden unit contributions

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