A Data-Driven Feature Extraction Method Based on Data Supplement for Human Activity Recognition

Human activity recognition (HAR) has garnered attention as a significant technology that can enhance the quality of human life. However, existing HAR works still face great challenges such as a shortage of labeled data and the difficulty of rebuilding a deep-learning (DL) model whenever the application environment (e.g., user or sensor position) changes. To address these challenges, we propose a new data-centric approach for HAR by using a semi-supervised generative adversarial network (SGAN). To improve the accuracy of HAR, we propose a data supplement strategy that systematically improves data quality, rather than the model, by using data refinement and data-driven feature extraction techniques. The proposed HAR method applies simple SGAN to achieve considerably high accuracy with only a small fraction of the labeled data. Therefore, the proposed HAR method can reduce overhead from data labeling, which is a labor-intensive and time-consuming process for many HAR tasks. Moreover, the data-centric HAR method is robust even in scenarios when there is a change in person/sensor location. Experimental results show that our method improves accuracy by as much as 3% over state-of-the-art semi-supervised HAR methods with only 3% of the data being labeled, leading to comparable accuracy to state-of-the-art HAR methods based on supervised learning.