Enabling fast and effortless customisation in accelerometer based gesture interaction

Mäntyjärvi, Jani; Kela, Juha; Korpipää, Panu; Kallio, Sanna

doi:10.1145/1052380.1052385

Cited by 104 publications

(63 citation statements)

References 6 publications

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“…For the VCR control gesture set, recognition accuracy was approximately 99% for both the Näive Bayes and Nearest Neighbor classifiers in the user dependent case. Mäntyjärvi also looked at 3D gesture recognition with 8 gestures using HMMs and found recognition accuracy in the high 90s as well [9].…”

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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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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“…When a user performs his/her identifying gesture in the air by holding a telephone with an embedded accelerometer in his/her hand, the feature extraction system obtains the accelerations of the gesture in the three axes sampled at frequency rate of 50 Hz, enough to distinguish between different repetitions with a compromise of quality and consumption time (Kela et al, 2006;Mantyjarvi et al, 2004).…”

Section: Mathematical Analysis Methodsmentioning

confidence: 99%

Score optimization and template updating in a biometric technique for authentication in mobiles based on gestures

Guerra-Casanova

Ávila

Sierra

et al. 2011

Journal of Systems and Software

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Keywords:Mobile authentication Template updating Gestures Biometrics Security Accelerometers This article focuses on the evaluation of a biometric technique based on the performance of an identifying gesture by holding a telephone with an embedded accelerometer in his/her hand. The acceleration signals obtained when users perform gestures are analyzed following a mathematical method based on global sequence alignment. In this article, eight different scores are proposed and evaluated in order to quantify the differences between gestures, obtaining an optimal EER result of 3.42% when analyzing a random set of 40 users of a database made up of 80 users with real attempts of falsification. Moreover, a temporal study of the technique is presented leeding to the need to update the template to adapt the manner in which users modify how they perform their identifying gesture over time. Six updating schemes have been assessed within a database of 22 users repeating their identifying gesture in 20 sessions over 4 months, concluding that the more often the template is updated the better and more stable performance the technique presents.

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“…Acceleration-based gesture recognition using HMM has been studied in works of Mäntyjärvi [9], [10], [12], [13]. They discuss the control for design environments and information appliances.…”

Section: Gesture Modelingmentioning

confidence: 99%

MobiRing: A Finger-Worn Wireless Motion Tracker

Chen

Tsai

Huang

et al. 2014

2014 IEEE International Conference on Internet of Things(iThings), and IEEE Green Computing and Communications (GreenCom) and I

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Abstract-MobiRing is a compact, finger-worn wireless motion-tracking platform that can be reprogrammed for a wide variety of features to augmented mobile computing. First, it works as a wireless mouse in the air without requiring a surface for a mouse or a trackpad, making it suitable for augmented-reality or virtualreality systems. Second, it supports collection of stroke-in-theair data that can be interpreted as gestures for commands, text input, or raw strokes. An advanced version can combine input from additional units on multiple fingers for richer possibilities. Third, the ability to quickly switch pairing among multiple host computing systems enables MobiRing to support drag-and-droplike operations across systems such as PCs, smartphones, tablets, display walls, and even displayless embedded systems, where a wired connection may be inconvenient if not infeasible. MobiRing has been prototyped with an ultra-compact wireless sensing platform with an on-board triaxial accelerometer and a shortrange wireless transceiver with an optional second accelerometer for enhanced accuracy. The prototype has been evaluated quantitatively for accuracy and qualitatively for usability by asking ten volunteer novice users to try a variety of the proposed operations. The results show that the MobiRing system to be intuitive for mouse-like functions and the recognition accuracy of each gesture is over 90%.

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Enabling fast and effortless customisation in accelerometer based gesture interaction

Cited by 104 publications

References 6 publications

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

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

Score optimization and template updating in a biometric technique for authentication in mobiles based on gestures

MobiRing: A Finger-Worn Wireless Motion Tracker

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