Optimizing Pressure Matrices

Strohmeier, Paul; Håkansson, Victor; Honnet, Cédric; Ashbrook, Daniel; Hornbæk, Kasper

doi:10.1145/3294109.3295638

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…Among various flexible touch sensors, pressure and strain sensors are the most popular categories [48,54]. Pressure-based position input used in wearable systems is highly common [64], including on-body gesture input [60], body posture classification [63], detection of advanced deformation gestures [55], etc. However, because the pressure sensor matrix often encounters problems such as jitter, jumping, or other nonlinear artifacts in practice, the current application of pressure matrix for precise and continuous position control is very rare [64].…”

Section: Interaction On Flexible Touch Sensormentioning

confidence: 99%

“…As the original form of creating human-readable text [45], handwriting is highly user-friendly. However, being flexible implies shape deformation, which produces jerky signals and other nonlinear artifacts when monitoring continuous position input [64]. To directly address the challenges in signal processing, we propose dedicated data preprocessing methods and use a deep convolutional neural network (CNN) to implement handwritten character recognition (Sec.…”

Section: Introductionmentioning

confidence: 99%

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Handwriting Velcro

Fang

Zhang

Zhan

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Text input is a desired feature for AR glasses. While there already exist various input modalities (e.g., voice, mid-air gesture), the diverse demands required by different input scenarios can hardly be met by the small number of fixed input postures offered by existing solutions. In this paper, we present Handwriting Velcro, a novel text input solution for AR glasses based on flexible touch sensors. The distinct advantage of our system is that it can easily stick to different body parts, thus endowing AR glasses with posture-adaptive handwriting input. We explored the design space of on-body device positions and identified the best interaction positions for various user postures. To flatten users' learning curves, we adapt our device to the established writing habits of different users by training a 36-character (i.e., A-Z, 0-9) recognition neural network in a human-in-the-loop manner. Such a personalization attempt ultimately achieves a low error rate of 0.005 on average for users with different writing styles. Subjective feedback shows that our solution has a good performance in system practicability and social acceptance. Empirically, we conducted a heuristic study to explore and identify the best interaction Position-Posture Correlation. Experimental results show that our Handwriting Velcro excels similar work [6] and commercial product in both practicality (12.3 WPM) and user-friendliness in different contexts.

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Section: Interaction On Flexible Touch Sensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Handwriting Velcro

Fang

Zhang

Zhan

et al. 2022

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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“…Due to the properties of textiles being lightweight, highly flexible, and often also stretchable they are applied in a wide range of use cases, e.g. for wireless monitoring suits [3], music control [7], multi-touch input [49,15] and motion capturing systems [54,11]. Machine embroidery has been one of the first fabrication techniques used in the field of smart textiles [37,41], as it is highly useful for rapid prototyping [28,48].…”

Section: Embroidered Smart Textilesmentioning

confidence: 99%

Sonoflex: Embroidered Speakers Without Permanent Magnets

Preindl

Honnet

Pointner

et al. 2020

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology

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“…Future work is involving pressure hoping to increase efficiency [20]. However, pressure is not the only way since it can utilize other works such as adding recognition to different fingers or parts of fingers allowing them to be mapped to different authentication functions [21,22]; or adding a new concept called finger-aware interaction system which enables to recognize fingers' touches on whole smartphone's surface [23].…”

Section: Future Workmentioning

confidence: 99%

Password-free Authentication for Smartphone Touchscreen Based on Finger Size Pattern

Enaya

Mohammed

Bilal³

2020

Int. J. Interact. Mob. Technol.

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This study introduces a novel authentication methodology; it is based on pattern recognition of fingers size and pressure when users touch smartphone screen. By analyzing diagrams of these touches and applying data mining for the first time as an authentication technique, this paper presents three new approaches. First, an exact-range evaluation approach has been verified that size is more recognition consistency than pressure. Second, a pattern-range is a new technique reliance on size frequency position. At last, using a size-range has been facilitated the login. The association rules have been modified to work on finger touchscreen data files. To login, 94.1111% of 18 authorized users are succeeded and 98.9% of 20 unauthorized users are failed. Android device and Android studio are used. Size and pressure are normalized to 1; a training set is applied; the password is not considered.

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Optimizing Pressure Matrices

Cited by 9 publications

References 26 publications

Handwriting Velcro

Handwriting Velcro

Sonoflex: Embroidered Speakers Without Permanent Magnets

Password-free Authentication for Smartphone Touchscreen Based on Finger Size Pattern

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