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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