Online gesture recognition system for mobile interaction

Kallio, Sanna; Kela, Juha; Mäntyjärvi, Jani

doi:10.1109/icsmc.2003.1244189

Cited by 42 publications

(27 citation statements)

References 9 publications

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“…The experiments were carried out using 500 training gestures with 10 samples per gesture, yielding an accuracy of 95.6% for 100 test gestures. In [24], gestures were captured with a small wireless sensor-box that produced three dimensional acceleration signal. Kallio et altrained the dHMM model by using five states and a codebook size of eight.…”

Section: Related Workmentioning

confidence: 99%

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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%

“…Gesture recognition has been extensively investigated in the last two decades, and remarkable advances are achieved using inertial sensors in mobile devices [22,23,24,25]. For the application of human computer interaction, Rekimoto et al [22] detected the movement of arm using a specific wearable device.…”

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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“…In contrast to our problem, gesture recognition aims to classify some gestures performed by different people to identify the gesture not the person. Nowadays, as a result of the high acceptability of interfaces incorporating an accelerometer, there are many previous works on this topic based on this sensor [17]. In-air signature recognition is also similar to dynamic handwritten signature [18] since both methods represent the signatures by means of temporal signals with spatial information.…”

Section: Introductionmentioning

confidence: 99%

Analysis of pattern recognition techniques for in-air signature biometrics

Bailador

Ávila

Guerra-Casanova

et al. 2011

Pattern Recognition

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Keywords:Biometrics Signature Accelerometer Hidden Markov models Bayes Dynamic time warpingAs a result of advances in mobile technology, new services which benefit from the ubiquity of these devices are appearing. Some of these services require the identification of the subject since they may access private user information. In this paper, we propose to identify each user by drawing his/her handwritten signature in the air (in-air signature). In order to assess the feasibility of an in-air signature as a biometric feature, we have analysed the performance of several well-known pattern recognition techniques-Hidden Markov Models, Bayes classifiers and dynamic time warping-to cope with this problem. Each technique has been tested in the identification of the signatures of 96 individuals. Furthermore, the robustness of each method against spoofing attacks has also been analysed using six impostors who attempted to emulate every signature. The best results in both experiments have been reached by using a technique based on dynamic time warping which carries out the recognition by calculating distances to an average template extracted from several training instances. Finally, a permanence analysis has been carried out in order to assess the stability of in-air signature over time.

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“…However once the number of training samples was increased to 20, accuracy levels rose to over 95%. Kallio also discussed classification confusion between the Line to Right and Parry gestures when providing two samples for training [6]. To the best of our knowledge, this paper presents the first 3D gesture recognition study using the Wiimote and Wii MotionPlus attachment that examines recognition accuracy for up to 25 distinct gestures.…”

Section: Related Workmentioning

confidence: 99%

Breaking the status quo: Improving 3D gesture recognition with spatially convenient input devices

Hoffman

Varcholik

LaViola

2010

2010 IEEE Virtual Reality Conference (VR)

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We present a systematic study on the recognition of 3D gestures using spatially convenient input devices. Specifically, we examine the linear acceleration-sensing Nintendo Wii Remote coupled with the angular velocity-sensing Nintendo Wii MotionPlus. For the study, we created a 3D gesture database, collecting data on 25 distinct gestures totalling 8500 gestures samples. Our experiment explores how the number of gestures and the amount of gestures samples used to train two commonly used machine learning algorithms, a linear and AdaBoost classifier, affect overall recognition accuracy. We examined these gesture recognition algorithms with user dependent and user independent training approaches and explored the affect of using the Wii Remote with and without the Wii MotionPlus attachment.Our results show that in the user dependent case, both the AdaBoost and linear classification algorithms can recognize up to 25 gestures at over 90% accuracy, with 15 training samples per gesture, and up to 20 gestures at over 90% accuracy, with only five training samples per gesture. In particular, all 25 gestures could be recognized at over 99% accuracy with the linear classifier using 15 training samples per gesture, with the Wii Remote coupled with the Wii MotionPlus. In addition, both algorithms can recognize up to nine gestures at over 90% accuracy using a user independent training database with 100 samples per gesture. The Wii MotionPlus attachment played a significant role in improving accuracy in both the user dependent and independent cases.

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Online gesture recognition system for mobile interaction

Cited by 42 publications

References 9 publications

Deep Fisher discriminant learning for mobile hand gesture recognition

Deep Fisher discriminant learning for mobile hand gesture recognition

Analysis of pattern recognition techniques for in-air signature biometrics

Breaking the status quo: Improving 3D gesture recognition with spatially convenient input devices

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