WiFi-based activity recognition with deep learning

Human activity recognition is drawing escalating attention in recent years in both academia and industry due to the potentials in bracing such a broad range of Internet of Things (IoT) applications as health diagnosis, human-machine interactions, safety surveillance, and so on. Among many forms of sensing technologies, e.g., using cameras, wearable sensors, and RFIDs, WiFi-based activity recognition is of particular interest given its ubiquity, low cost, device-free experience, and low dependence. Generally, people's motions will affect the reflected WiFi signals and incur specific radio patterns. Through profiling these specific patterns, we are able to recognize the original activities. Many existing works have reported relatively good activity recognition performance in dedicated scenarios; yet their performance degrades much in the practical complex applications with various impact factors, such as the co-channel interference, spatial diversity, and diverse environments, making existing WiFi-based solutions far from being satisfactory. In this thesis, we aim to address the existing key challenges and develop accurate, reliable, and adaptive WiFi-based human activity recognition systems. We argue that the integration of advanced deep learning techniques into the activity recognition will bring new opportunities towards our goal. Along this end, we first propose CSAR, a channel selective activity recognition framework that conquers the channel quality problem by active channel hopping and channel combination. We then develop WiSDAR, which constructs multiple separated antenna pairs and obtains features from multiple spatial dimensions to solve the spatial diversity problem. We at last investigate the activity recognition in a more compact in-car scenario and present WiCAR, a WiFi-based in-car activity recognition framework that leverages domain adaptation to remove the environment-specific information in the received signals while retaining the activity-related features for adaptive recognition. We have conducted extensive evaluations and the performance results further demonstrate the superiority of our frameworks over the state-of-the-art solutions.