LAUSR

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.