Airwriting: a wearable handwriting recognition system

Amma, Christoph; Georgi, Marcus; Schultz, Tanja

doi:10.1007/s00779-013-0637-3

Cited by 89 publications

(62 citation statements)

References 23 publications

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“…In total, the classification task comprises 26 classes, which is typical for many gesture recognition systems proposed recently. We use Hidden Markov Models for classification which have proofed to perform very well for these kind of problems [2,1]. We therefore deem our results can be transfered to other similar tasks.…”

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confidence: 72%

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Compressed signal representation for inertial sensor signals

Amma

Volk

Schultz

2013

Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication

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We present and evaluate a method to generate a compressed representation of multi-dimensional inertial sensor signals using a piecewise linear approximation. The representation can be computed on small sensor nodes and thus allows for a reduction of the amount of data that needs to be transmitted to the main processing node. On an existing gesture database, we present the compression rate that is reached and evaluate the quality of the representation in terms of the accuracy reached for gesture classification. We compare the results to our baseline system using a simpler approach for data reduction.

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confidence: 72%

“…We use a dataset from our airwriting system, which can recognize handwriting performed in the air by an inertial measurement unit placed on the back of the hand [1]. We use a subset of our data containing recordings of single characters written in the air.…”

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confidence: 99%

Compressed signal representation for inertial sensor signals

Amma

Volk

Schultz

2013

Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication

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“…There has been a lot of research and development on recognition systems of handwritten characters [21] [22][23] [24]. Christoph et al [21] proposed an interactive handwriting input method using motion sensors such as accelerometer and gyroscope.…”

Section: Related Workmentioning

confidence: 99%

“…Christoph et al [21] proposed an interactive handwriting input method using motion sensors such as accelerometer and gyroscope. They focus on the modality and intuitiveness of their 3D recognition system but the system has very limited practical application.…”

Section: Related Workmentioning

confidence: 99%

Manual Grading Task Support System with Interactive Correction Mechanism

Yamamoto

Kan

Murao

et al. 2018

Preprint

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Abstract:Gesture-based recognition is one of the most intuitive methods for inputting information and is not subject to cumbersome operations. Recognition is performed on human's consecutive motion without reference to retrial or alternation by user. We propose a gesture recognition model with a mechanism for correcting recognition errors that operates interactively and is practical. We applied the model to a setting involving a manual grading task in order to verify its effectiveness. Our system, named GERMIC, consists of two major modules, namely, handwritten recognition and interactive correction. Recognition is materialized with image feature extraction and convolutional neural network. A mechanism for interactive correction is called on-demand by a user-based trigger. GERMIC monitors, track, and stores information on the user's grading task and generates output based on the recognition information collected. In contrast to conventional grading done manually, GERMIC significantly shortens the total time for completing the task by 24.7% and demonstrates the effectiveness of the model with interactive correction in two real world user environments.

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“…However, the camera-based techniques require high computational power and are sensitive to surrounding illumination conditions. Inertial sensors [3] provide another solution for motion tracking, but the sensors need a subsystem for transmitting the collected data to a central processor to analyze in real time, which makes the wearable system bulky. Wi-Fi signals [4] leveraging the ubiquitous radio signals.…”

Section: Introductionmentioning

confidence: 99%

Angle-of-arrival-based gesture recognition using ultrasonic multi-frequency signals

Chen

Ballal

Saad

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Hand gestures are tools for conveying information, expressing emotion, interacting with electronic devices or even serving disabled people as a second language. A gesture can be recognized by capturing the movement of the hand, in real time, and classifying the collected data. Several commercial products such as Microsoft Kinect, Leap Motion Sensor, Synertial Gloves and HTC Vive have been released and new solutions have been proposed by researchers to handle this task. These systems are mainly based on optical measurements, inertial measurements, ultrasound signals and radio signals. This paper proposes an ultrasonic-based gesture recognition system using AOA (Angle of Arrival) information of ultrasonic signals emitted from a wearable ultrasound transducer. The 2-D angles of the moving hand are estimated using multi-frequency signals captured by a fixed receiver array. A simple redundant dictionary matching classifier is designed to recognize gestures representing the numbers from '0' to '9' and compared with a neural network classifier. Average classification accuracies of 95.5% and 94.4% are obtained, respectively, using the two classification methods.

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Airwriting: a wearable handwriting recognition system

Cited by 89 publications

References 23 publications

Compressed signal representation for inertial sensor signals

Compressed signal representation for inertial sensor signals

Manual Grading Task Support System with Interactive Correction Mechanism

Angle-of-arrival-based gesture recognition using ultrasonic multi-frequency signals

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