Sensor‐based human activity recognition (HAR) provides vital information for an ocean of health care and entertainment applications. However, data acquisition of such studies usually requires huge time and manpower, which delays the progress of the whole project. Data augmentation is a promising way to solve this issue. In this paper, we expand the origin dataset via a GAN‐based augmentation neural network. The experimental results suggest that the generated data has a certain substitution and complementary effect in terms of the real data based on the caching deployment ability. In addition, the quasi‐real data also gains good performance on identifying certain activities.

With the development and popularity of micro-electromechanical systems (MEMS) and smartphones, sensor-based human activity recognition (HAR) has been widely applied. Although various kinds of HAR systems have achieved outstanding results, there are still issues to be solved in this field, such as transition activities, which means the transitional process between two different basic activities, discussed in this paper. In this paper, we design an algorithm based on standard deviation trend analysis (STD-TA) for recognizing transition activity. Compared with other methods, which directly take them as basic activities, our method achieves a better overall performance: the accuracy is over 80% on real data.

Smartphone-based human activity recognition has become a considerable research field as a subdomains of pattern recognition and pervasive computing. With the increasing popularity of smartphones, HAR has prominent applications in number of fields such as health care, education, entertainment and etc. Smart devices have a huge advantage in convenience as the main acquisition and processing equipment, but the battery life of smartphone and other resources are limited for long-duration tasks. In this paper, we propose a lightweight HAR system. The system realizes HAR algorithm with deep learning algorithm. Beyond that, we introduce a clustering-center based pre-classification strategy to reduce the call frequency of the DL model. Meanwhile, we add a sampling frequency control mechanism to the inertial sensor. The goal of the whole system is to achieve low power consumption and time delay. According to the final experiment results, the energy consumption reduces about 49% and time delay reduces about 55% while the overall recognition accuracy only suffers about 10% reduction.