Airwriting: Hands-Free Mobile Text Input by Spotting and Continuous Recognition of 3d-Space Handwriting with Inertial Sensors

Amma, Christoph; Georgi, Marcus; Schultz, Tanja

doi:10.1109/iswc.2012.21

Cited by 98 publications

(47 citation statements)

References 18 publications

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“…Mid-air input is a promising input modality for emerging devices like smartwatches and heads-up displays [20]. In contrast to previous mid-air text entry methods which used extrinsic key targets or handwriting gestures [1,20,25,35], we focus on chord-like gestures controlled by angular motions. Although more complex than single finger input, it has been shown that a large number of chords can be memorized [34] and used for text entry (e.g.…”

Section: Application To Text Entrymentioning

confidence: 99%

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Investigating the Dexterity of Multi-Finger Input for Mid-Air Text Entry

Sridhar

Feit²,

Theobalt

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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Figure 1. We investigate the dexterity of using multiple fingers for mid-air input. The paper reports performance and individuation characteristics of fingers and deploys them to the design of a mid-air text entry method using multi-objective optimization. Here we show an example of the word 'hand' being typed using one of our automatically obtained designs. ABSTRACTThis paper investigates an emerging input method enabled by progress in hand tracking: input by free motion of fingers. The method is expressive, potentially fast, and usable across many settings as it does not insist on physical contact or visual feedback. Our goal is to inform the design of high-performance input methods by providing detailed analysis of the performance and anatomical characteristics of finger motion. We conducted an experiment using a commercially available sensor to report on the speed, accuracy, individuation, movement ranges, and individual differences of each finger. Findings show differences of up to 50% in movement times and provide indices quantifying the individuation of single fingers. We apply our findings to text entry by computational optimization of multi-finger gestures in mid-air. To this end, we define a novel objective function that considers performance, anatomical factors, and learnability. First investigations of one optimization case show entry rates of 22 words per minute (WPM). We conclude with a critical discussion of the limitations posed by human factors and performance characteristics of existing markerless hand trackers.

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Section: Application To Text Entrymentioning

confidence: 99%

“…In this input gesture, there is no external target like a button (cf. most previous work on mid-air text entry [1,20,25,35]). The involved fingers are extended or flexed at a single joint to a discriminable end posture.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Dexterity of Multi-Finger Input for Mid-Air Text Entry

Sridhar

Feit²,

Theobalt

et al. 2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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“…The work most related to ours is the 'Air Writing' system by Amma et.al. [21,22]. In this system, the authors used HMM based systems to directly recognize words and use a language model to correct errors in sentences.…”

Section: Related Workmentioning

confidence: 99%

Feedback-based handwriting recognition from inertial sensor data for wearable devices

Yao

Zweig

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper presents a novel interactive method for recognizing handwritten words, using the inertial sensor data available on smart watches. The goal is to allow the user to write with a finger, and use the smart watch sensor signals to infer what the user has written. Past work has exploited the similarity of handwriting recognition to speech recognition in order to deploy HMM based methods. In contrast to speech recognition, however, in our scenario, the user can see the individual letters that are recognized on a sequential basis, and provide feedback or corrections after each letter. In this paper, we exploit this key difference to improve the input mechanism over a classical source-channel model. For a small increase in the amount of time required to input a word, we improve recognition accuracy from 59.6% to 91.4% with an implicit feedback mechanism, and to 100% with an explicit feedback mechanism.

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“…They are especially useful if motions can be modeled as a sequence of motion primitives like for example gestures from a gesture alphabet (Amma et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

Human Activity Recognition for an Intelligent Knee Orthosis

Rebelo

Amma

Gambôa

et al. 2013

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Abstract:This paper investigates the possibility to classify isolated human activities from biosignal sensors integrated into a knee orthosis. An intelligent orthosis that is capable to recognize its wearers activity would be able to adapt itself to the users situation for enhanced comfort. We use a setup with three modalities: accelerometry, electromyography and goniometry to measure leg motion and muscle activity of the wearer. We segment signals in motion primitives and apply Hidden Markov Models to classify these isolated motion primitives. We discriminate between seven activities like for example walking stairs and ascend or descend a hill. In a small user study we reach an average person-dependent accuracy of 98% and a person-independent accuracy of 79%.

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Airwriting: Hands-Free Mobile Text Input by Spotting and Continuous Recognition of 3d-Space Handwriting with Inertial Sensors

Cited by 98 publications

References 18 publications

Investigating the Dexterity of Multi-Finger Input for Mid-Air Text Entry

Investigating the Dexterity of Multi-Finger Input for Mid-Air Text Entry

Feedback-based handwriting recognition from inertial sensor data for wearable devices

Human Activity Recognition for an Intelligent Knee Orthosis

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