Touch Input Devices Research Articles

PERANCANGAN KUNCI PINTU OTOMATIS PADA RUANG SENTRAL TELEKOMUNIKASI (STO) DI TELKOM MENGGUNAKAN SOLENOID DOOR LOCK DAN TOUCH SENSOR BERBASIS ARDUINO UNO

With the development of current microcontroller technology, it can be done using electronic devices as a substitute for conventional key security systems. This door security device uses a solenoid and controls it via a keypad and touch sensor. This tool is designed by utilizing the Arduino Uno microcontroller as the main controller, where the keypad is a password code input device and the touch sensor is a finger touch input device by giving commands to the microcontroller to control the relay. This tool works when the door will open automatically if the user enters the password that has been prepared via the keypad from outside the room, while the door will open automatically with the touch of a finger on the touch sensor from inside the room. If the password code entered is correct and the finger touch is right, the microcontroller will provide a high input to the relay to activate the solenoid. From the results of the tests carried out, it shows that the door lock solenoid has been activated, this is evidenced by the Arduino IDE software that the microcontroller can detect keypad and touch sensor inputs, the applied delay can activate the solenoid, and the feature to change and save a new password code.

MaterialSense: Estimating and utilizing material properties of contact objects in multi-touch interaction

In today’s digital art applications, users can play virtual musical instruments or paint on virtual canvas using multi-touch input. Existing touch input devices, however, do not consider that the result of artistic expression changes depending on the material properties of the tool that comes into contact with the art medium. This paper proposes a novel method called MaterialSense that estimates the material properties of objects in contact with a touch input surface. The technique uses a commercial touchpad with six load cells attached, and when up to two objects are in contact, solves the force equilibrium equation to estimate the stiffness and friction coefficient of each contacted object. By analyzing the time-series data of the measured 3-axis force and normal vector for each touchpoint, it can estimate the stiffness and kinetic friction coefficient of the contacted object. Estimated material properties enable novel and realistic artistic expressions in touch-based digital art applications, such as changes in the tone of virtual instruments or the effects of different painting brushes. We present two application scenarios using MaterialSense, along with an in-depth technical evaluation to verify the accuracy and precision of its estimates.

Studies and guidelines for two concurrent stroke gestures

This paper investigates thumb–index interaction on touch input devices, and more precisely the potential of two concurrent stroke gestures, i.e. gestures in which two fingers of the same hand concurrently draw one stroke each. We present two fundamental studies, one using such gestures for two-dimensional control, by precisely drawing figures, and the other for command activation, by roughly sketching figures. Results give a first analysis of user performance on 35 gestures with a varying complexity based on numbers of turns and symmetries. All 35 gestures, were grouped into six families. From these results we classify these families and propose new guidelines for designing future mobile interfaces. For instance, favoring anchored gestures (forefinger drawing while the thumb remains still on the surface) to increase input bandwidth when forefinger precision is required.

Detecting Touch and Grasp Gestures Using a Wrist-Worn Optical and Inertial Sensing Network

Freehand gesture-based interaction promises to enable rich interaction in applications such as augmented reality, virtual reality, human-robot interaction, and robotic prosthetic devices. However, current sensing approaches are limited to mid-air whole hand gestures and fail to identify small-scale tactile interactions with unsensorized environments. Detecting tactile interactions may unlock potential new applications in augmented reality and human-robot interaction in which unsensorized surfaces are used as touch input devices. This work presents a novel wrist-worn sensing device that combines near-infrared and inertial measurement unit sensing to enable high-accuracy detection of surface touch and grasp interactions. Two convolutional neural networks were used to map device inputs to detect touch events, and classify them by gesture type or direction. We evaluated the accuracy and temporal precision of our system for event detection and classification. Results from an in-lab user study of 12 participants showed an average of 97% touch detection accuracy and 98% grasp detection accuracy. In our study, we found that near-infrared and inertial sensing are complementary and can be used in tandem to effectively address both touch event detection and directionality classification.

One-handed Input for Mobile Devices via Motion Matching and Orbits Controls

We introduce a novel one-handed input technique for mobile devices that is not based on pointing, but on motion matching -where users select a target by mimicking its unique animation. Our work is motivated by the findings of a survey (N=201) on current mobile use, from which we identify lingering opportunities for one-handed input techniques. We follow by expanding on current motion matching implementations - previously developed in the context of gaze or mid-air input - so these take advantage of the affordances of touch-input devices. We validate the technique by characterizing user performance via a standard selection task (N=24) where we report success rates (>95%), selection times (~1.6 s), input footprint, grip stability, usability, and subjective workload - in both phone and tablet conditions. Finally, we present a design space that illustrates six ways in which motion matching can be embedded into mobile interfaces via a camera prototype application.

Partially shaded sketch-based image search in real mobile device environments via sketch-oriented compact neural codes

With the advent of touch screens in mobile devices, sketch-based image search is becoming the most intuitive method to query multimedia contents. Traditionally, sketch-based queries were formulated with hand-drawn shapes without any shades or colors. The absence of such critical information from sketches increased the ambiguity between natural images and their sketches. Although it was previously considered too cumbersome for users to add colors to hand-drawn sketches in image retrieval systems, the modern day touch input devices make it convenient to add shades or colors to query sketches. In this work, we propose deep neural codes extracted from partially colored sketches by an efficient convolutional neural network (CNN) fine-tuned on sketch-oriented augmented dataset. The training dataset is constructed with hand-drawn sketches, natural color images, de-colorized, and de-texturized images, coarse and fine edge maps, and flipped and rotated images. Fine-tuning CNN with augmented dataset enabled it to capture features effectively for representing partially colored sketches. We also studied the effects of shading and partial coloring on retrieval performance and show that the proposed method provides superior performance in sketch-based large-scale image retrieval on mobile devices as compared to other state-of-the-art methods.

A model of scrolling on touch-sensitive displays

Scrolling interaction is a common and frequent activity allowing users to browse content that is initially off-screen. With the increasing popularity of touch-sensitive devices, gesture-based scrolling interactions (e.g., finger panning and flicking) have become an important element in our daily interaction vocabulary. However, there are currently no comprehensive user performance models for scrolling tasks on touch displays. This paper presents an empirical study of user performance in scrolling tasks on touch displays. In addition to three geometrical movement parameters—scrolling distance, display window size, and target width, we also investigate two other factors that could affect the performance, i.e., scrolling modes—panning and flicking, and feedback techniques—with and without distance feedback. We derive a quantitative model based on four formal assumptions that abstract the real-world scrolling tasks, which are drawn from the analysis and observations of user scrolling actions. The results of a control experiment reveal that our model generalizes well for direct-touch scrolling tasks, accommodating different movement parameters, scrolling modes and feedback techniques. Also, the supporting blocks of the model, the four basic assumptions and three important mathematical components, are validated by the experimental data. In-depth comparisons with existing models of similar tasks indicate that our model performs the best under different measurement criteria. Our work provides a theoretical foundation for modeling sophisticated scrolling actions, as well as offers insights into designing scrolling techniques for next-generation touch input devices.

Dealing with uncertainty

Processes in the healthcare domain are characterized by coarsely predefined recurring procedures that are flexibly adapted by the personnel to suite-specific situations. In this setting, a workflow management system that gives guidance and documents the personnel's actions can lead to a higher quality of care, fewer mistakes, and higher efficiency. However, most existing workflow management systems enforce rigid inflexible workflows and rely on direct manual input. Both are inadequate for healthcare processes. In particular, direct manual input is not possible in most cases since (1) it would distract the personnel even in critical situations and (2) it would violate fundamental hygiene principles by requiring disinfected doctors and nurses to touch input devices. The solution could be activity recognition systems that use sensor data (e.g., audio and acceleration data) to infer the current activities by the personnel and provide input to a workflow (e.g., informing it that a certain activity is finished now). However, state-of-the-art activity recognition technologies have difficulties in providing reliable information. We describe a comprehensive framework tailored for flexible human-centric healthcare processes that improves the reliability of activity recognition data. We present a set of mechanisms that exploit the application knowledge encoded in workflows in order to reduce the uncertainty of this data, thus enabling unobtrusive robust healthcare workflows. We evaluate our work based on a real-world case study and show that the robustness of unobtrusive healthcare workflows can be increased to an absolute value of up to 91% (compared to only 12% with a classical workflow system). This is a major breakthrough that paves the way towards future IT-enabled healthcare systems.

Light curtain for 2D large-area object detection

Fluorescent foils are used with silicon photodiodes for large-area detection of objects, when combined with lasers forming a light curtain. An object entering the detection area penetrates the light curtain and casts shadows onto the fluorescent foils. Using a simple mathematical algorithm, the position of the object is detected with high speed. The device is suitable for security applications and can be used as a touch input device for computers, gaming and presentations.

2P1-O01 FuwaFuwa : 反射型光センサによる柔軟物体への接触位置および圧力の計測手法(VRとインタフェース)

We present the FuwaFuwa sensor module, a round, hand-size, wireless device for measuring the shape deformations of soft objects such as cushions and plush toys. It can be embedded in typical soft objects in the household without complex installation procedures and without spoiling the softness of the object because it requires no physical connection. Six LEDs in the module emit IR light in six orthogonal directions, and six corresponding photosensors measure the reflected light energy. One can easily convert almost any soft object into a touch-input device that can detect both touch position and surface displacement by embedding multiple FuwaFuwa sensor modules in the object.

Interacting with mouse and touch devices on horizontal interactive displays

This article presents and discusses findings on collaborative interaction on horizontal interactive displays using touch and mouse input devices. Tabletop interaction environments are suitable for small computer-supported group collaboration, and usually allow for concurrent interaction by multiple users. The appropriate support of interaction on horizontal interactive displays is crucial to the successful design and adoption of digital tabletops for work, education and entertainment. A study was conducted investigating interactions of users on tabletops operating with mouse and touch input devices, and shedding light on the differences and commonalities from different perspectives. This laboratory study was designed to allow for as much ecological validity and to provide good experimental control at the same time. The study builds on the standard digital puzzle, or mosaic task, by adding a hidden or implicit task, for the participants to discover and work on. Without solving the hidden task, participants were not able to solve the primary puzzle task. This approach was chosen to trigger spontaneous and natural collaborative work amongst the participants in the lab setting. The analysis of the interaction is based on system-logged interaction data, questionnaires and observations focused on the participants’ selection and usage of input devices during the task execution. The study revealed, amongst others, that participants did not change their initially preferred input device even when they became more engaged in coordination and communication with their partner during the course of the study.

A Touchpad for Force and Location Sensing

This paper presents the design and fabrication model of a touchpad based on a contact-resistance-type force sensor. The touchpad works as a touch input device, which can sense contact location and contact force simultaneously. The touchpad is 40 mm wide and 40 mm long. The touchpad is fabricated by using a simple screen printing technique. The contact location is evaluated by the calibration setup, which has a load cell and three-axis stages. The location error is approximately 4 mm with respect to x-axis and y-axis directions. The force response of the fabricated touchpad is obtained at three points by loading and unloading of the probe. The touchpad can detect loads from 0 N to 2 N. The touchpad shows a hysteresis error rate of about 11% and uniformity error rate of about 3%.

The use of auditory feedback in the design of touch-input devices

In workspaces with complex displays and controls, and limited space in which to mount them, the use of touchscreen displays as input devices offers significant advantages. To overcome the lack of tactile feedback on touchscreen input devices, auditory feedback has been suggested. This paper examines the results of an experiment looking at the effect of auditory feedback on typing performance. While error rates were not affected by the feedback, the addition of auditory feedback to a typing task did improve typing speeds under all tested conditions. This indicates that the addition of auditory feedback to touchscreen input devices provides a measurable benefit and should be considered, where possible.

Effects of Error-Proofing and Chemical/Biological/Radiation Protective Glove use on Touch Panel Operation

Previous studies suggested that initial-contact error rates were higher for touch input devices (resistive and infrared) than for various hardware keys. A follow-up study was conducted to evaluate the effects of chemical/biological/radiation (CBR) protective gloves on performance. Error data from this and the preceding study in this series were somewhat misleading due to differences in the input sampling strategies between hardware keys and touch panels. The initially high error rates for the resistive panel (21.6% using flight gloves and a 26.1% using CBR gloves) were considered unrepresentative of a true operating environment because nonkey contacts would be ignored by the system. Revision of the input-sensing logic produced 100% accuracy for the resistive panel in the replication study. However, multiple contacts were required in 5% (median of 4.1%) of the trials. Overall, 4% required only a second contact for correct activation of the designated key while 3- and 4-contact activations amounted to 0.6% of all trials. Conclusions drawn from the research were that CBR gloves do not significantly degrade performance over standard flight gloves or, for that matter, over the bare hand (errors or response times) and that the occurrence of multiple touches is small and virtually disappears with experience and adequate feedback, errors usually occurring in early trials and in no-feedback conditions. Thus, systems using gloved operators are unlikely to experience serious performance degradation if adequate key sizes for bare-handed activation are used and if timely and relevant feedback is provided to the operator.

Touch Panel Sampling Strategies and Keypad Performance Comparisons

A study was conducted to improve accuracy of touch input devices to be used in helicopter evnironments through serial sampling and minima selection algorithms. Neither first nor last contact point was congruent with point of minimum error and sampling techniques were derived to reduce error without using “touch-mouse” strategies, being most effective for the infrared panel. Wearing flight gloves and using the nonpreferred hand had no practical detrimental effects on performance for a high-resolution touch input task. In a second study several types of mechanical keypads were compared with the two types of touch-input devices. Results obtained with this menu-selection task suggested no practical degradation in performance when substituting the touch input devices for bezel-mounted hardware keys. This was true of both error rate (in a qualified sense) and response time. It was concluded that the touch-input devices could provide flexibility for high-resolution input using maps and other spatial forms of data while allowing effective use of previously developed menus and menu-selection routines without the need for auxiliary bezel-mounted mechanical keys.

Underlying behavioural parameters of the operation of touch-input devices: Biases, models and feedback : Beringer, D.B., and Peterson, J.G.Human Factors, 1985, 27.4, 445–458.

Underlying behavioural parameters of the operation of touch-input devices: Biases, models and feedback : Beringer, D.B., and Peterson, J.G.Human Factors, 1985, 27.4, 445–458.

Underlying Behavioral Parameters of the Operation of Touch-Input Devices: Biases, Models, and Feedback

Touch-entry devices have exhibited a number of advantages in relatively low-resolution applications. Requirements for high-resolution input must, however, take into account the inherent biases and limitations of the operator. In this study performance biases in the use of an infrared touch-input device were examined with the input (display) surface at various declinations. A general bias to touch low and to the right of targets was found, the low bias increasing with increasing display declination. Attempts to reduce bias error through simple models based upon earlier trials were more effective when derived from individual performances rather than populational behavior (averages). Handedness effects on performance were also found. Training is discussed as a viable alternative, along with preliminary training results.

Some comparisons of on-display and off-display touch input devices for interaction with computer generated displays

Abstract The relative merits of on-display and off-display touch input devices are discussed, together with the additional features of computer-generated feedback and the means of confirming a selection. Subjects' performances with on-display and off-display devices are compared in three experiments with increasing target resolution requirements —menu selection, tabular display, and target acquisition. Performance, and subjective reactions, appear to be no worse for the off-display input, although both devices are worse than a rolling ball (tracker ball) for high resolution targets. Characteristic types of error with these devices are demonstrated, and no beneficial effects of enhanced computer-generated feedback are shown. It is concluded that off-display continuous touch input carries no performance penalty over the on-display counterpart, and that its use is justified where its other advantages are apposite.

Touch-input Devices: Training the Device or the Operator?

Although touch-entry devices offer a number of advantages in low-resolution situations, requirements for high-resolution input must take into account the inherent biases and limitations of the operator. After the determination of operator bias in making high-resolution descriminations, attempts were made to examine the applicability of performance models for reducing the amount of “registered” error. It was found that bias error could be reduced, but that individual models are probably necessary for this to be done effectively.

Development of an off-display, high resolution, direct touch input device: the RSRE touchpad

The relative advantages of on-display and off-display touch input devices are discussed, and there is a description of the Royal Signals and Radar Establishment's Touchpad as exemplifying the latter. Three experiments are briefly described, comparing user performance with the Touchpad, an on-display touch input device, and a rolling ball. The Touchpad compares favourably with the other two devices, except at very high levels of resolution, where the rolling ball is superior in terms of accuracy.