Multi-scale Convolution Transformer for Human Activity Detection

Abstract

Human activity recognition (HAR) plays an important role in many applications such as smart homes, healthcare services, and security monitoring. Recently, WiFi-based human activity recognition (HAR) is becoming increasingly popular due to its non-invasiveness. Most existing HAR works only use classification methods for activity recognition, without focusing on the start time and end time of actions. In this paper, we propose to use a detection method that predicts both the type of activity as well as its start and end times. For detection tasks, both global information and local information are essential for modeling and identifying various types of activities. Therefore, we propose a multi-scale convolution Transformer that is able to exploit local features of WiFi data more effectively using CNNs, while global features are captured with Transformer. In our experiments, the proposed model shows outstanding performance in indoor environment, with a weak micro F1 score of 98.37% and a strong micro F1 score of 92.81%.