  There is a research field of human activity recognition that automatically
recognizes a user's physical activity through sensing technology incorporated
in smartphones and other devices. When sensing daily activity, various
measurement conditions, such as device type, possession method, wearing method,
and measurement application, are often different depending on the user and the
date of the measurement. Models that predict activity from sensor values are
often implemented by machine learning and are trained using a large amount of
activity-labeled sensor data measured from many users who provide labeled
sensor data. However, collecting activity-labeled sensor data using each user's
individual smartphones causes data being measured in inconsistent environments
that may degrade the estimation accuracy of machine learning. In this study, I
propose an activity recognition method that is robust to different sampling
rates -- even in the measurement environment. The proposed method applies an
adversarial network and data augmentation by downsampling to a common activity
recognition model to achieve the acquisition of feature representations that
make the sampling rate unspecifiable. Using the Human Activity Sensing
Consortium (HASC), which is a dataset of basic activity recognition using
smartphone sensors, I conducted an evaluation experiment to simulate an
environment in which various sampling rates were measured. As a result, I found
that estimation accuracy was reduced by the conventional method in the above
environment and could be improved by my proposed method.
