ZebraSense

Wu, Tung-Yu; Fukuhara, Shiho; Gillian, Nicholas; Sundara-Rajan, Kishore; Poupyrev, Ivan

doi:10.1145/3379337.3415886

Cited by 26 publications

(6 citation statements)

References 31 publications

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“…For this reason, there are challenges when sensing while taking advantage of existing furniture designs. Wu et al [31] have developed unique touch sensors that can identify the front and back sides of the touch surface, respectively. These studies are very good in that soft materials such as clothing can be used as touch sensors, but because this method is also a capacitance-based sensor, it is difficult to place the sensor under thick boards such as furniture.…”

Section: Capacitive Touch Sensormentioning

confidence: 99%

Smatable: A Vibration-Based Sensing Method for Making Ordinary Tables Touch-Interfaces

Yoshida,

Matsui,

Ishiyama

et al. 2023

IEEE Access

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In recent years, the equipment that makes up smart homes is required not only to be functional, but also to be integrated with the design and aesthetics of the living space. Among them, interfaces that directly touch the human eye and hands are the key to maintaining design, but there were many issues in terms of integration with design and aesthetics of living spaces. In this paper, we propose an interface system that operates existing furniture by touching it as a new interface that integrates beautifully into the living space. The proposed system detects user operations with four small vibration sensors attached to hidden locations of existing furniture and uses deep learning to learn the vibrations when a person touches the furniture. Using this method, thick materials difficult to achieve with normal capacitive touch sensors can be utilized. In the experiment, a dining table was used as a representative piece of furniture, and the accuracy of detecting the direction in which three participants swiped in four directions on the table was verified. As a result of the experiment, the accuracy was confirmed by Leave-One-Person-Out-Cross-Validation using 3 sessions of swipe data for each individual for 3 participants, and the accuracy was 0.67. Furthermore, we verified the accuracy of a trained model created by adding only one session of evaluation target data to each learning dataset used in the Leave-One-Person-Out-Cross-Validation. As a result, the accuracy reached 0.90, achieving practical precision.

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Section: Capacitive Touch Sensormentioning

confidence: 99%

Smatable: A Vibration-Based Sensing Method for Making Ordinary Tables Touch-Interfaces

Yoshida,

Matsui,

Ishiyama

et al. 2023

IEEE Access

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“…With the development of tactile sensor [67], numerous flexible touch sensors [73,74] have appeared. Some researchers use flexible sensors as wearable interactive interfaces to achieve interactive control of mobile smart devices.…”

Section: Interaction On Flexible Touch Sensormentioning

confidence: 99%

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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“…Fabric style touch sensors [20,21] have been made using sandwich layers of fabrics and conductive rubber sheets (cut with laser). The fabrication process for the transducer elements uses common techniques such as ironing, gluing, and layering.…”

Section: Related Work -Soft Touch Sensormentioning

confidence: 99%

Soft Body Belt-Type Touch Sensor With Impact Resistance: A Study of Dynamic Behavior

2021

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We report the development of a novel soft body touch sensor capable of measuring contact force along its length. The sensor can be embedded in soft objects and on curved surfaces for smart furniture, interactive electronics, and general robotics applications. The sensor uses pneumatic transduction principle and achieves both high sensitivity and impact loading tolerance. The development is motivated by the needs of consumer electronics industries for smart embedded furniture and interactive toys. The current work fills a gap of existing soft-bodied touch sensors. The main sensor body is completely polymeric and contains no conductive or semiconducting material elements. It exhibits high pass frequency response to reject common mode contact forces or shape variations. The central question addressed by this paper is dynamic behavior of this sensor under normal and impact touch force. In this study we developed a theoretical model for the sensor output and validated with experimental measurements. We modeled sensor behavior under two operational regimes of different unloading speed. Depending on the rate of change, the sensor could be in force limited relaxation mode or elastic relaxation (free relaxation) mode. Measurements have been performed to capture time constants of occupant behavior in a smart cushion application.

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ZebraSense

Cited by 26 publications

References 31 publications

Smatable: A Vibration-Based Sensing Method for Making Ordinary Tables Touch-Interfaces

Smatable: A Vibration-Based Sensing Method for Making Ordinary Tables Touch-Interfaces

Handwriting Velcro

Soft Body Belt-Type Touch Sensor With Impact Resistance: A Study of Dynamic Behavior

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