  The development of IoT technology enables a variety of sensors can be
integrated into mobile devices. Human Activity Recognition (HAR) based on
sensor data has become an active research topic in the field of machine
learning and ubiquitous computing. However, due to the inconsistent frequency
of human activities, the amount of data for each activity in the human activity
dataset is imbalanced. Considering the limited sensor resources and the high
cost of manually labeled sensor data, human activity recognition is facing the
challenge of highly imbalanced activity datasets. In this paper, we propose
Balancing Sensor Data Generative Adversarial Networks (BSDGAN) to generate
sensor data for minority human activities. The proposed BSDGAN consists of a
generator model and a discriminator model. Considering the extreme imbalance of
human activity dataset, an autoencoder is employed to initialize the training
process of BSDGAN, ensure the data features of each activity can be learned.
The generated activity data is combined with the original dataset to balance
the amount of activity data across human activity classes. We deployed multiple
human activity recognition models on two publicly available imbalanced human
activity datasets, WISDM and UNIMIB. Experimental results show that the
proposed BSDGAN can effectively capture the data features of real human
activity sensor data, and generate realistic synthetic sensor data. Meanwhile,
the balanced activity dataset can effectively help the activity recognition
model to improve the recognition accuracy.
