'Visual-fidelity' dataglove calibration

Kahlesz,; Zachmann,; Klein,

doi:10.1109/cgi.2004.1309241

Cited by 34 publications

(38 citation statements)

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“…This stems from the fact that even identical postures can produce different sensor values when the sizes of the subjects' hands vary. One way to tackle this problem is to perform a calibration process as in Kahlesz et al [14]. However, this process can be complex, timeconsuming and in itself error-prone.…”

Section: Related Workmentioning

confidence: 99%

“…Due to cross-couplings between the sensors, however, more complex forms of calibration are necessary to achieve a satisfactory fidelity. In Kahlesz et al [14] some recent calibration techniques are summarized. Since our objective was to spare any calibration process, the raw sensor readings, as transmitted by the Cyberglove 2, were used directly as feature vector for hand posture classification.…”

Section: Data Glovementioning

confidence: 99%

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Grasp Recognition with Uncalibrated Data Gloves - A Comparison of Classification Methods

Heumer

Amor

Weber

et al. 2007

2007 IEEE Virtual Reality Conference

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This paper presents a comparison of various classification methods for the problem of recognizing grasp types involved in object manipulations performed with a data glove. Conventional wisdom holds that data gloves need calibration in order to obtain accurate results. However, calibration is a time-consuming process, inherently user-specific, and its results are often not perfect. In contrast, the present study aims at evaluating recognition methods that do not require prior calibration of the data glove, by using raw sensor readings as input features and mapping them directly to different categories of hand shapes. An experiment was carried out, where test persons wearing a data glove had to grasp physical objects of different shapes corresponding to the various grasp types of the Schlesinger taxonomy. The collected data was analyzed with 28 classifiers including different types of neural networks, decision trees, Bayes nets, and lazy learners. Each classifier was analyzed in six different settings, representing various application scenarios with differing generalization demands. The results of this work are twofold: (1) We show that a reasonably well to highly reliable recognition of grasp types can be achieved -depending on whether or not the glove user is among those training the classifier -even with uncalibrated data gloves. (2) We identify the best performing classification methods for recognition of various grasp types. To conclude, cumbersome calibration processes before productive usage of data gloves can be spared in many situations.

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Section: Related Workmentioning

confidence: 99%

Section: Data Glovementioning

confidence: 99%

Grasp Recognition with Uncalibrated Data Gloves - A Comparison of Classification Methods

Heumer

Amor

Weber

et al. 2007

2007 IEEE Virtual Reality Conference

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“…Previous work by [Kahlesz et al 2004;Steffen et al 2011] also reports severe coupling problems in their experiments. Their calibration methods synthesize training data using parabolic functions, to avoid too much sampling work.…”

Section: Related Workmentioning

confidence: 94%

“…hand sizes, influences from sensor stretching as well as other factors that cause reading variance. For example, the abduction sensors between fingers are stretched by finger flexion movements, and thus can not be accurately calibrated by methods that assume sensor independence [Kahlesz et al 2004]. The false assumption of exact sensor linearity restricts flexible sampling which either causes over-fitting or fails to get solutions [Griffin et al 2000;Hu et al 2004].…”

Section: Introductionmentioning

confidence: 99%

“…Accuracy requirements for captured hand motion depend on the applications: [Huenerfauth and Lu 2010;Kahlesz et al 2004;Chou et al 2000;Menon et al 2003] emphasize plausible hand shapes; [Griffin et al 2000;Hu et al 2004] focus on exact fingertip positions. No current calibration method can satisfy both FK and IK accuracy requirements simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Data-driven glove calibration for hand motion capture

Wang

Neff

2013

Proceedings of the 12th ACM SIGGRAPH/Eurographics Symposium on Computer Animation

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Hand motion is an important component of human motion, playing a central role in communication. However, it is difficult to capture hand motion optically, especially in conjunction with full body motion. Due to a lack of appropriate calibration methods, data gloves also do not provide sufficiently accurate hand motion. In this paper, we present a novel glove calibration approach that can map raw sensor readings to hand motion data with both accurate joint rotations and fingertip positions. Our method elegantly handles the sensor coupling problem by treating calibration as a flexible mapping from sensor readings to joint rotations. A sampling process collects data tuples according to accuracy requirements, and organizes all the tuples in a training set. From these data, a specially designed Gaussian Process Regression model is trained to infer the calibration function, and the learned model can be used to calibrate new sensor readings. For real-time hand motion capture, a sparse approximation of the model is used to enhance performance. Evaluation experiments demonstrate that our approach provides significantly better results that have more accurate hand shapes and fingertip positions, compared to other calibration methods.

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Data-Driven Hand Animation Synthesis

Jörg¹

2018

Handbook of Human Motion

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'Visual-fidelity' dataglove calibration

Cited by 34 publications

References 0 publications

Grasp Recognition with Uncalibrated Data Gloves - A Comparison of Classification Methods

Grasp Recognition with Uncalibrated Data Gloves - A Comparison of Classification Methods

Data-driven glove calibration for hand motion capture

Data-Driven Hand Animation Synthesis

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