Software-defined radio-based contactless localization for diverse human activity recognition

This article presents a study on contactless localization for activity recognition based on radio frequency (RF) sensing. The focus of this study is on the quantitative analysis of the collected data, which is in the form of channel state information (CSI). The proposed method utilizes a software-defined radio (SDR) system in combination with an ensemble learning technique to localize and identify daily living activities such as leaning, sitting, standing, and walking. Specifically, an SDR device, a universal software radio peripheral (USRP) model X300/X310, is utilized to collect data on the aforementioned activities. The data is collected from an empty space and a participant performing daily living activities in different territories. The acquired data is labeled based on the region, zone, and performed activity. The CSI data is subsequently preprocessed and fed into an ensemble-based machine-learning algorithm for classification. Furthermore, the stability analysis of the proposed method is performed to evaluate its reliability and robustness. The performance of the algorithm is evaluated using various metrics, including a confusion matrix, accuracy, cross-validation score, and training time (Shah et al., 2017 and Taylor et al., 2020). The results demonstrate that the proposed ensemble-based approach achieves a high accuracy of up to 90% in activity recognition, highlighting the effectiveness of the proposed method in contactless localization for activity recognition.