Human-Computer Interaction Control of Snake-Like Robot Based on Gesture Recognition

Chen, Yidong; Wu, Wei; Xiao, Wenyu

doi:10.1145/3351917.3351984

Cited by 1 publication

(2 citation statements)

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“…Hand gestures are an important communication channel for humans [1] and provide a natural way to support humancomputer interactions [2]. Recent years have witnessed increased interest in exploiting wireless signals such as WiFi [3][4][5][6][7] or RFID [8] for gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

“…It then provides the user with the correct stroke order for incorrectly written characters, by comparing the detected stroke order against a database of standardized order. We design a range of new analysis and models based on statistical methods and machine learning to achieve our objectives: (1) to enable WiCG to adapt to different deployment sites and user patterns, (2) to map the tracked CSI signal to specific strokes, and (3) to exploit contextual information to infer what characters are most likely to be written when ambiguity arises. e result is a new way to use CSI to track fine-grained gesture movements and in which order the movements were performed.…”

Section: Introductionmentioning

confidence: 99%

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Exploiting Serialized Fine-Grained Action Recognition Using WiFi Sensing

Tong

Gong

et al. 2021

Mobile Information Systems

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Gestures serve an important role in enabling natural interactions with computing devices, and they form an important part of everyday nonverbal communication. In increasingly many application scenarios of gesture interaction, such as gesture-based authentication, calligraphy, sketching, and even artistic expression, not only are the underlying gestures complex and consist of multiple strokes but also the correctness of the gestures depends on the order at which the strokes are performed. In this paper, we present WiCG, an innovative and novel WiFi sensing approach for capturing and providing feedback on stroke order. Our approach tracks the user’s hand movement during writing and exploits this information in combination with statistical methods and machine learning techniques to infer what characters have been written and at which stroke order. We consider Chinese calligraphy as our use case as the resulting gestures are highly complex, and their assessment depends on the correct stroke order. We develop a set of analyses and algorithms to overcome many issues of this challenging task. We have conducted extensive experiments and user studies to evaluate our approach. Experimental results show that our approach is highly effective in identifying the written characters and their written stroke order. We show that our approach can adapt to different deployment environments and user patterns.

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