Abstract
Device-free sensing of human gestures has gained tremendous research attention with the recent advancements in wireless technologies. Channel State Information (CSI), a metric of Wi-Fi devices adopted for device-free sensing achieves better recognition performance. This survey classifies the state of the art recognition task into device-based and device-free sensing methods and highlights advancements with Wi-Fi CSI. This paper also comprehensively summarizes the recognition performance of device-free sensing using CSI under two approaches: model-based and learning based approaches. Machine Learning and Deep Learning algorithms are discussed under the learning based approaches with its corresponding recognition accuracy. Various signal pre-processing, feature extraction, selection, and classification techniques that are widely adopted for gesture recognition along with the environmental factors that influence the recognition accuracy are also discussed. This survey presents the conclusion spotting the challenges and opportunities that could be explored in the device free gesture recognition using the CSI metric of Wi-Fi devices.
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