Shape Writing on Tablets: Better Performance or Better Experience?

Nguyen, Harry; Bartha, Michael C.

doi:10.1037/e572172013-330

Cited by 3 publications

(3 citation statements)

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“…Hoggan et al (2008) reported physical keyboard input was faster than onscreen Qwerty keyboard input, but Allen et al (2008) found input time for physical keyboard input to be equal to onscreen Qwerty keyboard input. Similar discrepancies occur when comparing results for onscreen Qwerty and shape-writing input (Castellucci & MacKenzie, 2013;Cuaresma & MacKenzie, 2013;Nguyen & Bartha, 2012;Yatani & Truong, 2007).…”

Section: Introductionmentioning

confidence: 59%

“…An examination of the literature shows wide variability in the results comparing performance of these input methods on a smartphone. The research varies in the type of protocol, stimuli, metrics, task type, level of participant expertise and demographics, and differences in the device used (Allen, McFarlin, & Green, 2008;Arif & Stuerzlinger, 2009;Azenkot & Lee, 2013;Castellucci & MacKenzie, 2013;Hoggan, Brewster, & Johnston, 2008;Nguyen & Bartha, 2012;Nicolau & Jorge, 2012;Page, 2013;Yatani & Truong, 2007). Hoggan et al (2008) reported physical keyboard input was faster than onscreen Qwerty keyboard input, but Allen et al (2008) found input time for physical keyboard input to be equal to onscreen Qwerty keyboard input.…”

Section: Introductionmentioning

confidence: 99%

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Smartphone Text Input Method Performance, Usability, and Preference With Younger and Older Adults

Smith

Chaparro

2015

Hum Factors

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Section: Introductionmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Smartphone Text Input Method Performance, Usability, and Preference With Younger and Older Adults

Smith

Chaparro

2015

Hum Factors

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“…An earlier version of these devices is H.P. slate [40] and Wiimote [14]. These wearable devices were difficult (and sometimes impossible) to handle.…”

Section: Related Work and The Dataset Of Air-writingmentioning

confidence: 99%

TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM Network

Alam

Imtiaz

Hossain

et al. 2022

Human Behavior and Emerging Technologies

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Air-writing is a growing research topic in the field of gesture-based writing systems. This research proposes a unified, lightweight, and general-purpose deep learning algorithm for a trajectory-based air-writing recognition network (TARNet). We combine a convolutional neural network (CNN) with a long short-term memory (LSTM) network. The architecture and applications of CNN and LSTM networks differ. LSTM is good for time series prediction yet time-consuming; on the other hand, CNN is superior in feature generation but comparatively faster. In this network, the CNN and LSTM serve as a feature generator and a recognizer, optimizing the time and accuracy, respectively. The TARNet utilizes 1-dimensional separable convolution in the first part to obtain local contextual features from low-level data (trajectories). The second part employs the recurrent algorithm to acquire the dependency of high-level output. Four publicly available air-writing digit (RealSense trajectory digit), character (RealSense trajectory character), smart-band, and Abas datasets were employed to verify the accuracy. Both the normalized and nonnormalized conditions were considered. The use of normalized data required longer training times but provided better accuracy. The test time was the same as those for nonnormalized data. The accuracy for RTD, RTC, smart-band, and Abas datasets were 99.63%, 98.74%, 95.62%, and 99.92%, respectively.

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Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor

Alam

Kwon

Alam

et al. 2020

Sensors

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Trajectory-based writing system refers to writing a linguistic character or word in free space by moving a finger, marker, or handheld device. It is widely applicable where traditional pen-up and pen-down writing systems are troublesome. Due to the simple writing style, it has a great advantage over the gesture-based system. However, it is a challenging task because of the non-uniform characters and different writing styles. In this research, we developed an air-writing recognition system using three-dimensional (3D) trajectories collected by a depth camera that tracks the fingertip. For better feature selection, the nearest neighbor and root point translation was used to normalize the trajectory. We employed the long short-term memory (LSTM) and a convolutional neural network (CNN) as a recognizer. The model was tested and verified by the self-collected dataset. To evaluate the robustness of our model, we also employed the 6D motion gesture (6DMG) alphanumeric character dataset and achieved 99.32% accuracy which is the highest to date. Hence, it verifies that the proposed model is invariant for digits and characters. Moreover, we publish a dataset containing 21,000 digits; which solves the lack of dataset in the current research.

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Shape Writing on Tablets: Better Performance or Better Experience?

Cited by 3 publications

References 0 publications

Smartphone Text Input Method Performance, Usability, and Preference With Younger and Older Adults

Smartphone Text Input Method Performance, Usability, and Preference With Younger and Older Adults

TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM Network

Trajectory-Based Air-Writing Recognition Using Deep Neural Network and Depth Sensor

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