ArXiv Preprint
Human activity recognition is a core technology for applications such as
rehabilitation, ambient health monitoring, and human-computer interactions.
Wearable devices, particularly IMU sensors, can help us collect rich features
of human movements that can be leveraged in activity recognition. Developing a
robust classifier for activity recognition has always been of interest to
researchers. One major problem is that there is usually a deficit of training
data for some activities, making it difficult and sometimes impossible to
develop a classifier. In this work, a novel GAN network called TheraGAN was
developed to generate realistic IMU signals associated with a particular
activity. The generated signal is of a 6-channel IMU. i.e., angular velocities
and linear accelerations. Also, by introducing simple activities, which are
meaningful subparts of a complex full-length activity, the generation process
was facilitated for any activity with arbitrary length. To evaluate the
generated signals, besides perceptual similarity metrics, they were applied
along with real data to improve the accuracy of classifiers. The results show
that the maximum increase in the f1-score belongs to the LSTM classifier by a
13.27% rise when generated data were added. This shows the validity of the
generated data as well as TheraGAN as a tool to build more robust classifiers
in case of imbalanced data problem.
Mohammad Mohammadzadeh, Ali Ghadami, Alireza Taheri, Saeed Behzadipour
2023-02-16
