Wi-ID: WiFi-Based Identification System Using Rock-Paper-Scissors Hand Gestures

Abstract This paper proposes using a WiFi-based identification system, Wi-ID, to identify users from their unique hand gestures. Hand gestures from the popular game rock-paper-scissors are utilized for the system’s user authentication commands. The whole feature of three hand gestures is extracted instead of the single gesture feature extracted by the existing methods. Dynamic time warping (DTW) is utilized to analyze the amplitude information in the time domain based on linear discriminant analysis (LDA), while extract amplitude kurtosis (AP-KU) and shape skewness (SP-SK) are utilized to analyze the Wi-Fi signals energy distribution in the frequency domain. Based on the contributions of the extracted features, the random forests algorithm is utilized for weight inputs in the LSTM model. The experiment is conducted on a computer installed with an Intel 5300 wireless networking card to evaluate the effectiveness and robustness of the Wi-ID system. The experiment results showed the accuracy of the proposed Wi-ID system has a personal differentiation accuracy rate over 92%, and with an average accuracy of 96%. Authorized persons who performed incomplete hand gestures are identified with an accuracy of 92% and hostile intruders can be identified with a probability of 90%. Such performance demonstrates that the Wi-ID system achieved the aim of user authentication.

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Geometry-based Dynamic Hand Gesture Recognition

Journal of Science and Technology Issue on Information and Communications Technology ◽

10.31130/jst.2015.6 ◽

2015 ◽

Vol 1 ◽

pp. 13

Author(s):

Duc-Hoang Vo ◽

Huu-Hung Huynh ◽

Jean Meunier

Keyword(s):

Hard Of Hearing ◽

Dynamic Time Warping ◽

Hidden Markov ◽

Hand Gesture ◽

Time Warping ◽

Color Information ◽

Hand Gestures ◽

Geometrical Properties ◽

Dynamic Hand Gesture Recognition ◽

Dynamic Time

Hand gestures play an important role in communication in the hard-of-hearing community. They are used to convey information instead of words. Besides, a system which is developed to identify gestures can be also used for human-computer interaction. In this paper, we propose a vision-based approach for recognizing dynamic hand gestures. Our processing consists of three main stages: pre-processing, feature extraction and recognition. The first stage involves two sub-stages: segmentation which locates the hand and extracts the corresponding silhouette using color information; separation that removes the arm based on geometrical properties. Some characteristics which describe the hand posture are then extracted. Finally, the recognition is performed using two popular algorithms, which are Dynamic Time Warping and Hidden Markov Model. The experiment is conducted on SKIG dataset with a comparison of classification accuracies corresponding to the two mentioned methods.

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An Upper Extremity Rehabilitation System Using Efficient Vision-Based Action Identification Techniques

Applied Sciences ◽

10.3390/app8071161 ◽

2018 ◽

Vol 8 (7) ◽

pp. 1161

Author(s):

Yen-Lin Chen ◽

Chin-Hsuan Liu ◽

Chao-Wei Yu ◽

Posen Lee ◽

Yao-Wen Kuo

Keyword(s):

Upper Extremity ◽

Personal Computer ◽

Skin Color ◽

Dynamic Time Warping ◽

Identification System ◽

Action Identification ◽

Time Warping ◽

Rehabilitation System ◽

Upper Extremity Rehabilitation ◽

Dynamic Time

This study proposes an action identification system for home upper extremity rehabilitation. In the proposed system, we apply an RGB-depth (color-depth) sensor to capture the image sequences of the patient’s upper extremity actions to identify its movements. We apply a skin color detection technique to assist with extremity identification and to build up the upper extremity skeleton points. We use the dynamic time warping algorithm to determine the rehabilitation actions. The system presented herein builds up upper extremity skeleton points rapidly. Through the upper extremity of the human skeleton and human skin color information, the upper extremity skeleton points are effectively established by the proposed system, and the rehabilitation actions of patients are identified by a dynamic time warping algorithm. Thus, the proposed system can achieve a high recognition rate of 98% for the defined rehabilitation actions for the various muscles. Moreover, the computational speed of the proposed system can reach 125 frames per second—the processing time per frame is less than 8 ms on a personal computer platform. This computational efficiency allows efficient extensibility for future developments to deal with complex ambient environments and for implementation in embedded and pervasive systems. The major contributions of the study are: (1) the proposed system is not only a physical exercise game, but also a movement training program for specific muscle groups; (2) The hardware of upper extremity rehabilitation system included a personal computer with personal computer and a depth camera. These are economic equipment, so that patients who need this system can set up one set at home; (3) patients can perform rehabilitation actions in sitting position to prevent him/her from falling down during training; (4) the accuracy rate of identifying rehabilitation action is as high as 98%, which is sufficient for distinguishing between correct and wrong action when performing specific action trainings; (5) The proposed upper extremity rehabilitation system is real-time, efficient to vision-based action identification, and low-cost hardware and software, which is affordable for most families.

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Floor Based Sensors Walk Identification System Using Dynamic Time Warping with Cloudlet Support

2019 IEEE 13th International Conference on Semantic Computing (ICSC) ◽

10.1109/icosc.2019.8665560 ◽

2019 ◽

Cited By ~ 3

Author(s):

Robert Hughes ◽

Fadi Muheidat ◽

Mason Lee ◽

Lo'ai A. Tawalbeh

Keyword(s):

Dynamic Time Warping ◽

Identification System ◽

Time Warping ◽

Dynamic Time

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A Novel Similarity Measure for Clustering Vessel Trajectories Based on Dynamic Time Warping

Journal of Navigation ◽

10.1017/s0373463318000723 ◽

2018 ◽

Vol 72 (2) ◽

pp. 290-306 ◽

Cited By ~ 5

Author(s):

Liangbin Zhao ◽

Guoyou Shi

Keyword(s):

Similarity Measure ◽

Dynamic Time Warping ◽

Similarity Measurement ◽

Automatic Identification ◽

Identification System ◽

The Novel ◽

Clustering Methods ◽

Time Warping ◽

Water Transportation ◽

Dynamic Time

Clustering methods that use a similarity measurement for evaluating vessel trajectories are important for mining spatial distribution information in water transportation. To better measure the similarity of vessel trajectories, a novel similarity measure is proposed based on the dynamic time warping distance, which considers the course change of track points and the meaning at the route level. Parallel experiments were conducted based on a month of Automatic Identification System (AIS) data collected from the Zhoushan Islands area, China. After evaluation of the accuracy and the cluster degree, the novel measure demonstrated its capabilities for distinguishing different vessel trajectories and detecting similar vessel trajectories with high accuracy and has a better performance compared to some existing methods.

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