Recognizing and Analyzing of User's Continuous Action in Mobile Systems

Baek, Jaemin; Jang, Ik-Jin; Yun, Byoung-Ju

doi:10.1093/ietisy/e89-d.12.2957

Cited by 4 publications

(3 citation statements)

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“…Although more accurate gesture recognition may be on the horizon, Baek, Jang, and Yun (2006) reported incorrect gesture recognition of 5%-10% in participants' use of a handheld fishing game that included accelerometers for tilt and gesture recognition.…”

Section: Selection Methodsmentioning

confidence: 99%

Handheld Electronic Devices

Lewis¹,

Commarford²,

Sadowski³

2008

Reviews of Human Factors and Ergonomics

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From PDAs to cell phones to MP3 players, handheld electronic devices are ubiquitous. Human factors engineers and designers have a need to remain informed about advances in research on user interface design for this class of devices. This review provides human factors research summaries and research-based guidelines for the design of handheld devices. The major topics include anthropometry (fitting the device to the hand), input (types of device control and methods for data entry), output (display design), interaction design (one-handed use, scrolling, menu design, image manipulation, and using the mobile Web), and data sharing (among users, devices, and networks). Thus, this review covers the key aspects of the design of handheld devices, from the design of the physical form of the device through its hardware and software, including its behavior in networks.The past decade has seen an increasing proliferation of handheld electronic devices and mobile services, and this will certainly continue into the future. In this review we address recent research and design trends related to this challenging product class.We first address the design goal of ensuring a good fit between the shape of a handheld device and users' hands. The input section addresses the methods by which users control devices and enter data through the use of buttons, touch screens, speech, or more exotic methods (for example, tilt and acceleration sensors). After discussing the parallel challenges of designing displays, we discuss interaction design, addressing fundamental tasks that involve both input and output, such as designing for one-handed use, scrolling, menu design, image manipulation, and using the mobile Web. Finally, few modern handheld devices exist in isolation. Handheld devices are especially vulnerable to theft and damage, and the resulting loss of data can range from annoying to catastrophic, making it critical to have easy methods for synchronizing data among multiple devices. Indeed, these small devices present many significant human factors challenges. ANTHROPOMETRYOne of the first steps in the development of a handheld device is to ensure a good physical match with the human hand. Although the gross dimensions and morphology of the hand have not changed in thousands of years, designers must consider the intended

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Section: Selection Methodsmentioning

confidence: 99%

Handheld Electronic Devices

Lewis¹,

Commarford²,

Sadowski³

2008

Reviews of Human Factors and Ergonomics

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show abstract

“…(6) Similarly, in gesture detection, an input gesture is detected by setting a threshold value for the acceleration values acquired from an accelerometer. (1) There is a trade-off between false positives and false negatives, and it is difficult to reduce both at the same time. The F-measure can be used as an index to evaluate the recognition accuracy in such a case, and the F1-score has been used in many studies.…”

Section: Gesture Detection By Accelerometermentioning

confidence: 99%

“…• False positive (for gesture detection): a detected gesture is output when no gesture is performed • False negative (for gesture detection): no result is output when a gesture is performed • False classification (for gesture classification): an incorrect gesture is output Generally, in gesture detection, the existence of a gesture is determined by setting a threshold value and detecting whether the acceleration value exceeds the threshold. (1,2) Setting a threshold to make gestures easier to detect reduces false negatives but increases false positives due to noise. On the other hand, setting a threshold to make gestures harder to detect reduces false positives but increases false negatives.…”

Section: Introductionmentioning

confidence: 99%

High-F1-score Recognition of Input Gestures while Holding Smartphone by Reducing False Detection Due to Walking Noise

Katsuma¹,

Amiya²

2022

Sensors and Materials

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Gesture input using the acceleration sensor of a smartphone is a promising new input method. The target input gestures in this paper are movements of a user's hand holding a smartphone. However, if parameter tuning is performed to improve the recognition accuracy of input gestures while stationary, erroneous detection at the start of walking will increase. On the other hand, if parameter tuning is performed to reduce false detection at the start of walking, the recognition accuracy of input gestures while stationary is lowered. Thus, there is a trade-off problem. In this paper, we propose a gesture recognition method to reduce erroneous recognition by combining a gesture detection method that uses similarity based on dynamic time warping (DTW) (TD) and a gesture classification method that also includes walking data as a candidate (CD). We conducted evaluation experiments with nine subjects. As a result, we confirmed that false detection at the start of walking can be eliminated using the proposed method. By verification using t-test, we confirmed that the F1-score of the proposed method was significantly higher than that of CD.

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Precision Gesture-based Inputs for Smartphone with Two Consecutive Simple Gestures

Amiya

Katsuma

2021

2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)

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Recognizing and Analyzing of User's Continuous Action in Mobile Systems

Cited by 4 publications

References 0 publications

Handheld Electronic Devices

Handheld Electronic Devices

High-F1-score Recognition of Input Gestures while Holding Smartphone by Reducing False Detection Due to Walking Noise

Precision Gesture-based Inputs for Smartphone with Two Consecutive Simple Gestures

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