DocumentCode :
2696537
Title :
Airwriting: Hands-Free Mobile Text Input by Spotting and Continuous Recognition of 3d-Space Handwriting with Inertial Sensors
Author :
Amma, Christoph ; Georgi, Marcus ; Schultz, Tanja
Author_Institution :
Cognitive Syst. Lab., Inst. for Anthropomatics, Karlsruhe, Germany
fYear :
2012
fDate :
18-22 June 2012
Firstpage :
52
Lastpage :
59
Abstract :
We present an input method which enables complex hands-free interaction through 3d handwriting recognition. Users can write text in the air as if they were using an imaginary blackboard. Motion sensing is done wirelessly by accelerometers and gyroscopes which are attached to the back of the hand. We propose a two-stage approach for spotting and recognition of handwriting gestures. The spotting stage uses a Support Vector Machine to identify data segments which contain handwriting. The recognition stage uses Hidden Markov Models (HMM) to generate the text representation from the motion sensor data. Individual characters are modeled by HMMs and concatenated to word models. Our system can continuously recognize arbitrary sentences, based on a freely definable vocabulary with over 8000 words. A statistical language model is used to enhance recognition performance and restrict the search space. We report the results from a nine-user experiment on sentence recognition for person dependent and person independent setups on 3d-space handwriting data. For the person independent setup, a word error rate of 11% is achieved, for the person dependent setup 3% are achieved. We evaluate the spotting algorithm in a second experiment on a realistic dataset including everyday activities and achieve a sample based recall of 99% and a precision of 25%. We show that additional filtering in the recognition stage can detect up to 99% of the false positive segments.
Keywords :
accelerometers; data gloves; filtering theory; gesture recognition; gyroscopes; handwriting recognition; hidden Markov models; image sensors; search problems; support vector machines; text detection; 3D handwriting recognition; 3D-space handwriting data; HMM; accelerometers; airwriting; arbitrary sentences; complex hands-free interaction; continuous recognition; data gloves; data segments; false positive segments; freely definable vocabulary; gyroscopes; hands-free mobile text input; handwriting gestures recognition; handwriting gestures spotting; hidden Markov models; imaginary blackboard; individual characters; inertial sensors; motion sensing; motion sensor data; person dependent setups; person independent setups; recognition performance; recognition stage filtering; search space; sentence recognition; spotting algorithm; spotting recognition; spotting stage; statistical language model; support vector machine; text representation; word error rate; word models; Handwriting recognition; Hidden Markov models; Motion segmentation; Sensors; Support vector machines; Text recognition; Vocabulary; Accelerometers; Handwriting recognition; User interfaces; Wearable computers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wearable Computers (ISWC), 2012 16th International Symposium on
Conference_Location :
Newcastle
ISSN :
1550-4816
Print_ISBN :
978-1-4673-1583-8
Type :
conf
DOI :
10.1109/ISWC.2012.21
Filename :
6246142
Link To Document :
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