A Single Accelerometer based Wireless Embedded System for Predefined Dynamic Gesture Recognition

Parsani, Rahul; Singh, Karandeep

doi:10.1007/978-81-8489-203-1_18

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…Parsani et al [27] designed an embedded system which could analyze and recognize smartphone gestures involving a combination of straight line motions in three dimensions. Roy et al [28] suggested that the walking direction should be detectable through the accelerometer and get blended into various other motion patterns during the act of walking, including up and down bounce, side-to-side sway, swing of arms or legs, etc..…”

Section: Related Workmentioning

confidence: 99%

Deep Fisher discriminant learning for mobile hand gesture recognition

Xie

Zhang

et al. 2018

Pattern Recognition

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Section: Related Workmentioning

confidence: 99%

Deep Fisher discriminant learning for mobile hand gesture recognition

Xie

Zhang

et al. 2018

Pattern Recognition

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“…Fresca et al [26] studied and recognized human gesticulation and the manipulation of graspable and movable everyday artifacts as a potentially effective means for the interaction with smart things. Parsani et al [27] designed an embedded system which could analyze and recognize smartphone gestures involving a combination of straight line motions in three dimensions. Roy et al [28] suggested that the walking direction should be detectable through the accelerometer and get blended into various other motion patterns during the act of walking, including up and down bounce, side-to-side sway, swing of arms or legs, etc..…”

Section: Related Workmentioning

confidence: 99%

Deep Fisher Discriminant Learning for Mobile Hand Gesture Recognition

Xie¹,

Li²,

Zhang³

et al. 2017

Preprint

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Gesture recognition is a challenging problem in the field of biometrics. In this paper, we integrate Fisher criterion into Bidirectional Long-Short Term Memory (BLSTM) network and Bidirectional Gated Recurrent Unit (BGRU), thus leading to two new deep models termed as F-BLSTM and F-BGRU. Both Fisher discriminative deep models can effectively classify the gesture based on analyzing the acceleration and angular velocity data of the human gestures. Moreover, we collect a large Mobile Gesture Database (MGD) based on the accelerations and angular velocities containing 5547 sequences of 12 gestures. Extensive experiments are conducted to validate the superior performance of the proposed networks as compared to the state-of-the-art BLSTM and BGRU on MGD database and two benchmark databases (i.e. BUAA mobile gesture and SmartWatch gesture).

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