Cross-Location Activity Recognition using Adversarial Learning

Human activity recognition (HAR) is an emerging field of study to recognize human movement and actions from recorded data. It plays a significant role in human-computer interaction. Depending upon their complexity, human activities are divided into gestures, actions, interactions, and group activities. This work aims at introducing the importance of building a system for recognizing human actions such as indoor activities (sitting, standing) and outdoor activities (running, walking), often called low-level activities, using Wi-Fi signals. Although a Significant amount of work has been done on human activity recognition based on WiFi Channel State Information (CSI) with significant results but they face one major issue of Location Dependency. An activity recognition model trained at one location does not perform properly at other locations, because the human location also has significant influence on Wi-Fi signal propagation.The main objective is to make predictions independent of the location of the Wi-Fi device, which is necessary to improve the generalization of such systems. That is, our aim is to develop a location independent WiFi based HAR system that can be trained using data captured from different locations, such that it is capable of providing accurate predictions on data coming from new locations via Adversarial learning, i.e., locations that are not part of the training process. In this paper, we highlight the importance of building such a system, its significance from the perspective of different real-world applications, a hierarchical architecture of the model for learning location-independent representations from the signals using adversarial training, and the results of validation/evaluation of the proposed system on a public benchmark. The obtained results highlight the success of our proposed system in learning location-independent representations which provide a significant boost in the recognition performance of downstream classifiers trained on such representations.