Journal ArticleDOI
MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition
Ruize Xu,Shengli Zhou,Wen J. Li +2 more
TLDR
A recognition algorithm based on sign sequence and template matching as presented in this paper can be used for nonspecific-users hand-gesture recognition without the time consuming user-training process prior to gesture recognition.Abstract:
This paper presents three different gesture recognition models which are capable of recognizing seven hand gestures, i.e., up, down, left, right, tick, circle, and cross, based on the input signals from MEMS 3-axes accelerometers. The accelerations of a hand in motion in three perpendicular directions are detected by three accelerometers respectively and transmitted to a PC via Bluetooth wireless protocol. An automatic gesture segmentation algorithm is developed to identify individual gestures in a sequence. To compress data and to minimize the influence of variations resulted from gestures made by different users, a basic feature based on sign sequence of gesture acceleration is extracted. This method reduces hundreds of data values of a single gesture to a gesture code of 8 numbers. Finally, the gesture is recognized by comparing the gesture code with the stored templates. Results based on 72 experiments, each containing a sequence of hand gestures (totaling 628 gestures), show that the best of the three models discussed in this paper achieves an overall recognition accuracy of 95.6%, with the correct recognition accuracy of each gesture ranging from 91% to 100%. We conclude that a recognition algorithm based on sign sequence and template matching as presented in this paper can be used for nonspecific-users hand-gesture recognition without the time consuming user-training process prior to gesture recognition.read more
Citations
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Journal ArticleDOI
High speed control of electro-mechanical transduction Advanced Drive Techniques for Optimized Step-and-Settle Response of MEMS Micromirrors
Matthias Imboden,Jackson Chang,Corey Pollock,Evan Lowell,Mehmet Akbulut,Jessica Morrison,Thomas Stark,Thomas G. Bifano,David J. Bishop +8 more
TL;DR: In this paper, the authors proposed an approach to improve response times of micro/Nano Electro Mechanical Systems (MEMS/NEMS) by using engineered drive techniques to reduce response times by as much as a thousand.
Proceedings ArticleDOI
GestureSeg: developing a gesture segmentation system using gesture execution phase labeling by crowd workers
Sven Kratz,Jason Wiese +1 more
TL;DR: This work explores the use of motion gesture data labeled with gesture execution phases for training supervised learning classifiers for gesture segmentation, and describes initial results that indicate that gesture execution phase can be accurately recognized by SVM classifiers.
Hand Gesture Recognition for Dumb People using Indian Sign Language
TL;DR: In this research, flex sensor and accelerometer sensors based hand glove is designed to recognize the Indian sign language and every time updating the new words in the database the dumb will speak like a normal person.
Journal ArticleDOI
Effective Inertial Hand Gesture Recognition Using Particle Filtering Based Trajectory Matching
Zuocai Wang,Bin Chen,Jin Wu +2 more
TL;DR: Compared with representative methods using accelerometer or vision sensors, the proposed inertial sensor based hand gesture recognition method is proved to be fast, reliable, and accurate.
Journal ArticleDOI
Augmented reality displaying scheme in a smart glass based on relative object positions and orientation sensors
Shih-Wei Sun,Yi-Shan Lan +1 more
TL;DR: This paper proposes to use a depth camera to detect a human subject in a real 3D space, and orientation sensors on a smart glass are used to reveal the attitude and orientations of a user’s head for pose estimation in an AR application.
References
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Pattern Recognition and Machine Learning
TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
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Pattern Recognition and Machine Learning
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
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Principles of Interactive Computer Graphics
TL;DR: The principles of interactive computer graphics are discussed in this article, where the authors propose a set of principles for the development of computer graphics systems, including the principles of Interactive Computer Graphics (ICG).
Proceedings ArticleDOI
Gesture recognition with a Wii controller
TL;DR: The design and evaluation of the sensor-based gesture recognition system is presented, which allows the training of arbitrary gestures by users which can then be recalled for interacting with systems like photo browsing on a home TV.
Journal ArticleDOI
Glove-Talk: a neural network interface between a data-glove and a speech synthesizer
Sidney Fels,Geoffrey E. Hinton +1 more
TL;DR: To illustrate the potential of multilayer neural networks for adaptive interfaces, a VPL Data-Glove connected to a DECtalk speech synthesizer via five neural networks was used to implement a hand-gesture to speech system, demonstrating that neural networks can be used to develop the complex mappings required in a high bandwidth interface that adapts to the individual user.