A unified generative model using generative adversarial network for activity recognition

The recent advancement of deep learning methods has seen a significant increase in recognition accuracy in many important applications such as human activity recognition. However, deep learning methods require a vast amount of sensor data to automatically extract the most salient features for activity classification. Therefore, in this paper, a unified generative model is proposed to generate verisimilar data of different activities for activity recognition. The proposed generative model not only able to generate data that have a similar pattern, but also data with diverse characteristics. This allows for data augmentation in activity classification to improve the overall recognition accuracy. Three similarity measures are proposed to assess the quality of the synthetic data in addition to two visual evaluation methods. The proposed generative model was evaluated on a public dataset. The training data was prepared by systematically varying the combination of original and synthetic data. Results have shown that classification using the hybrid training data achieved a comparable recognition accuracy with the classification using the original training data. The performance of the classifiers maintained at the recognition accuracy of 85%.

Automated summary

The paper introduces a unified generative model to generate realistic data of different activities for activity recognition, which not only generates data with similar patterns but also data with diverse characteristics, providing possibilities for data augmentation in activity classification. Through the evaluation of the quality of synthetic data, the results showed that classification using hybrid training data achieved a comparable recognition accuracy with the classification using original training data.