Método para a identificação de gestos de mão usando wi-fi sensing e aprendizado de máquina

Abstract

Human-Computer Interaction (HCI) seeks increasingly intuitive interfaces, and hand gesture recognition is a promising field for this evolution. Traditional approaches, such as those based on computer vision or specialized hardware, present limitations regarding cost, privacy, and line-of-sight requirements. This work explores WiFi Sensing as a robust and non-invasive alternative, utilizing disturbances in Wi-Fi signals, known as Channel State Information (CSI), to classify gestures. The central hypothesis is that machine learning models can achieve high accuracy in classifying gestures from CSI data. To validate this hypothesis, a proof-of-concept was developed with an evolutionary methodology, structured in two experimental scenarios: the first using a public dataset to establish a benchmark in a complex environment, and the second with a custom dataset collected in a controlled environment to isolate hand gestures. The data processing evolved from an initial approach that treated each signal subcarrier independently to a final methodology that aggregates the entire gesture capture into a single feature vector, preserving the correlation between channels. Model optimization was performed with the Optuna framework, which allowed not only for the search of optimal hyperparameters but also for an in-depth analysis of the computational cost (Work Effort) of each model. The results demonstrated the superiority of the aggregated capture approach. In the controlled scenario, the SGDClassifier model achieved the highest accuracy at 88.14%, also standing out for its exceptional computational efficiency, with the lowest optimization cost and the fastest convergence. This work concludes that WiFi Sensing, combined with machine learning pipelines and intelligent optimization, is a viable and effective solution for gesture recognition, offering a positive balance between accuracy, cost, and respect for privacy.