Understanding WiFi signal frequency features for position-independent gesture sensing - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Mobile Computing Année : 2022

Understanding WiFi signal frequency features for position-independent gesture sensing

Résumé

Recent years have witnessed rapid development in the research area of WiFi sensing, which senses human activities in a contactless and non-intrusive manner. One major issue that hinders real-world deployment of these systems is position dependence, i.e., once the human target changes location and orientation, the sensing performance degrades significantly. Existing machine learning based methods aim to solve this problem by either generating high-dimensional features or transfer learning the environment knowledge. However, these methods require significant training effort and yet acquire limited improvement. In this paper, we start by understanding and analyzing the Doppler frequency shift in WiFi sensing. We then develop a WiFi frequency model to quantify the relationship between signal frequency and target position, motion direction and speed for human activities. Based on this theoretical model, we prove that the commonly-used movement speed and motion direction features are position dependent, and further identify movement fragments and relative motion direction changes as two position-independent features. Building upon the frequency model and the position-independent features, we design a suite of position-independent gestures and develop the gesture recognition system accordingly. Evaluation results show that under various conditions (i.e., different locations, orientations, environments, and persons), our system achieves more than 96% recognition accuracy without any training, significantly outperforming state-of-the-art machine learning based solutions.
Fichier principal
Vignette du fichier
Understanding_WiFi_Signal_Frequency_Features_for_Position-Independent_Gesture_Sensing.pdf (10.11 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03363402 , version 1 (03-10-2021)

Identifiants

Citer

Kai Niu, Fusang Zhang, Xuanzhi Wang, Qin Lv, Haitong Luo, et al.. Understanding WiFi signal frequency features for position-independent gesture sensing. IEEE Transactions on Mobile Computing, 2022, 21 (11), pp.4156 - 4171. ⟨10.1109/TMC.2021.3063135⟩. ⟨hal-03363402⟩
454 Consultations
451 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More