Multimodal Fusion-AdaBoost Based Activity Recognition for Smart Home on WiFi Platform

With the ubiquity of commodity WiFi devices and the rapid development of Internet of Things (IoT), there are increasingly intelligent sensing applications emerging by utilizing fine-grained channel state information (CSI) from WiFi signals, which can realize contactless human-computer interaction (HCI) for smart home. However, most of CSI-based activity recognition approaches are vulnerable to random noises derived from indoor environments. In this paper, we present a multimodal fusion-AdaBoost based human activity recognition scheme on WiFi platform. For this purpose, we develop two theoretical underpinnings, including a sensing model and a recognition model. The sensing model is firstly established to investigate the impact of human activities on propagation properties of WiFi signals. To this end, Fresnel zone model is used to quantify the correlation between CSI dynamics and human activities. Moreover, the recognition model is used to exploit physical activity-induced signal changes to infer potential activity information. In the process, we firstly construct a CSI tensor through integrating all CSI information at each WiFi receiver. Then, a CANDECAMP/PARAFAC (CP) decomposition method is applied to this CSI tensor for obtaining representative features. Finally, based on the features extracted, human activities are able to be recognized by combining multimodal fusion and AdaBoost. We implement the proposed scheme on a set of WiFi devices and evaluate it in both laboratory and corridor environments. The experimental results confirm that the proposed scheme can achieve average recognition accuracies of 96% and 95% in two indoor scenarios, respectively.