Device free human gesture recognition using Wi-Fi CSI: A survey

Ahmed, Hasmath Farhana Thariq; Ahmad, Hilal; Vaithilingam, Chockalingam Aravind

doi:10.1016/j.engappai.2019.103281

Cited by 67 publications

(22 citation statements)

References 56 publications

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“…Many researchers try to build a sign language recognition system to break these barriers [1]. Currently, sign language recognition systems are roughly divided into two categories: (i) device-based sign language recognition systems; (ii) device-free sign language recognition systems [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Kanokod et al recognized gestures through the time delay neural networks (TDNNs) algorithm, and the gesture data is obtained from data gloves based on pyrolytic graphite sheets (PGS) [6]. In general, the advantages of the wearable device-based sign language recognition method are that the input data is accurate and the recognition rate is high [2,[4][5][6]. The disadvantages are also obvious.…”

Section: Introductionmentioning

confidence: 99%

“…The device-free sign language recognition systems are usually inexpensive and not limited by the wearable device [2,7]. Several device-free sign language recognition systems use computer vision techniques with cameras.…”

Section: Introductionmentioning

confidence: 99%

“…Koller conducted a survey on gesture recognition, focusing on the With the widespread deployment of wireless networks, wireless related technologies have experienced very rapid growth. Gesture recognition based on commodity Wi-Fi sensing solutions has been widely studied [2,7]. Thus, a sign language recognition system that is non-intrusive and insensitive to lighting conditions has attracted the interest of many researchers.…”

Section: Introductionmentioning

confidence: 99%

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Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals

Lee

Gao

2020

Applied Sciences

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Sign language is an important way for deaf people to understand and communicate with others. Many researchers use Wi-Fi signals to recognize hand and finger gestures in a non-invasive manner. However, Wi-Fi signals usually contain signal interference, background noise, and mixed multipath noise. In this study, Wi-Fi Channel State Information (CSI) is preprocessed by singular value decomposition (SVD) to obtain the essential signals. Sign language includes the positional relationship of gestures in space and the changes of actions over time. We propose a novel dual-output two-stream convolutional neural network. It not only combines the spatial-stream network and the motion-stream network, but also effectively alleviates the backpropagation problem of the two-stream convolutional neural network (CNN) and improves its recognition accuracy. After the two stream networks are fused, an attention mechanism is applied to select the important features learned by the two-stream networks. Our method has been validated by the public dataset SignFi and adopted five-fold cross-validation. Experimental results show that SVD preprocessing can improve the performance of our dual-output two-stream network. For home, lab, and lab + home environment, the average recognition accuracy rates are 99.13%, 96.79%, and 97.08%, respectively. Compared with other methods, our method has good performance and better generalization capability.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Sign Language Recognition Using Two-Stream Convolutional Neural Networks with Wi-Fi Signals

Lee

Gao

2020

Applied Sciences

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“…Recently, intelligent systems recognizing human faces or gestures by training images has been developing [21], and the performance of classifiers adopting machine learning techniques such as deep learning has been improved [22]. With an intelligent system that can automatically classify the actual occupant activity in real time, the observation method can be a practical and convenient solution.…”

Section: Introductionmentioning

confidence: 99%

Accuracy Analysis of DNN-Based Pose-Categorization Model and Activity-Decision Algorithm

Park

Choi

et al. 2020

Energies

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The objective of this study is to develop (1) a pose-categorization model that classifies the poses of an occupant based on their image in an indoor space and (2) an activity-decision algorithm that identifies the activity being performed by the occupant. For developing an automated intelligent model, a deep neural network is adopted. The model considers the coordinates of the joints of the occupant in the image as input data and returns the pose of the occupant. Datasets composed of indoor images of home and office environments are used for training and testing the model. The training and testing accuracies of the optimized model were 100% for both the home and office environments. A representative activity of an occupant for a certain period has to be decided to control an indoor environment for comfort. The activity-decision algorithm employs a frequency-based method to determine the representative activity type for real-time occupant poses using the pose-categorization model. This study highlights the potential of the developed model and algorithm to determine the activity of occupants to provide an optimal thermal environment corresponding to the individual’s metabolic rate.

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