Nomadic input on mobile devices: the influence of touch input technique and walking speed on performance and offset modeling

In everyday life people use their mobile phones on-the-go with different walking speeds and with different touch input techniques. Unfortunately, much of the published research in mobile interaction does not quantify the influence of these variables. In this paper, we analyze the influence of walking speed, gait pattern and input techniques on commonly used performance parameters like error rate, accuracy and tapping speed, and we compare the results to the static condition. We examine the influence of these factors on the machine learned offset model used to correct user input and we make design recommendations. The results show that all performance parameters degraded when the subject started to move, for all input techniques. Index finger pointing techniques demonstrated overall better performance compared to thumb-pointing techniques. The influence of gait phase on tap event likelihood and accuracy was demonstrated for all input techniques and all walking speeds. Finally, it was shown that the offset model built on static data did not perform as well as models inferred from dynamic data, which indicates the speed-specific nature of the models. Also, models identified using specific input techniques did not perform well when tested in other conditions, demonstrating the limited validity of offset models to a particular input technique. The model was therefore calibrated using data recorded with the appropriate input technique, at 75% of preferred walking speed, which is the speed to which users spontaneously slow down when they use a mobile device and which presents a tradeoff between accuracy and usability. This led to an increase in accuracy compared to models built on static data. The error rate was reduced between 0.05% and 5.3% for landscape-based methods and between 5.3% and 11.9% for portrait-based methods

Proceedings of the 4th Workshop on Interacting with Smart Objects 2015

These are the Proceedings of the 4th IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

Tablet Applications for the Elderly: Specific Usability Guidelines

While the world population is aging, the technological progress is steadily increasing. Smartphones and tablets belong to a growing market and even more people aged 65 and above are using such touch devices. However, with advancing age normal cognitive, sensory, perceptual and motor changes influence psychological and physical capabilities and therefore the way the elderly are able to use tablet-applications. When designing tablet-applications for the elderly developers have to be supported in understanding these capabilities. Therefore, this thesis provides a comprehensive compilation of usability guidelines in order to develop user-friendly tablet-applications for older people. The development and testing of an exemplary tablet-application within this thesis shows how these guidelines can be brought into practice and how this realization is evaluated by test persons in this age group

Dwell-free input methods for people with motor impairments

Millions of individuals affected by disorders or injuries that cause severe motor impairments have difficulty performing compound manipulations using traditional input devices. This thesis first explores how effective various assistive technologies are for people with motor impairments. The following questions are studied: (1) What activities are performed? (2) What tools are used to support these activities? (3) What are the advantages and limitations of these tools? (4) How do users learn about and choose assistive technologies? (5) Why do users adopt or abandon certain tools? A qualitative study of fifteen people with motor impairments indicates that users have strong needs for efficient text entry and communication tools that are not met by existing technologies. To address these needs, this thesis proposes three dwell-free input methods, designed to improve the efficacy of target selection and text entry based on eye-tracking and head-tracking systems. They yield: (1) the Target Reverse Crossing selection mechanism, (2) the EyeSwipe eye-typing interface, and (3) the HGaze Typing interface. With Target Reverse Crossing, a user moves the cursor into a target and reverses over a goal to select it. This mechanism is significantly more efficient than dwell-time selection. Target Reverse Crossing is then adapted in EyeSwipe to delineate the start and end of a word that is eye-typed with a gaze path connecting the intermediate characters (as with traditional gesture typing). When compared with a dwell-based virtual keyboard, EyeSwipe affords higher text entry rates and a more comfortable interaction. Finally, HGaze Typing adds head gestures to gaze-path-based text entry to enable simple and explicit command activations. Results from a user study demonstrate that HGaze Typing has better performance and user satisfaction than a dwell-time method

What does touch tell us about emotions in touchscreen-based gameplay?

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACM. It is posted here by permission of ACM for your personal use. Not for redistribution.Nowadays, more and more people play games on touch-screen mobile phones. This phenomenon raises a very interesting question: does touch behaviour reflect the player’s emotional state? If possible, this would not only be a valuable evaluation indicator for game designers, but also for real-time personalization of the game experience. Psychology studies on acted touch behaviour show the existence of discriminative affective profiles. In this paper, finger-stroke features during gameplay on an iPod were extracted and their discriminative power analysed. Based on touch-behaviour, machine learning algorithms were used to build systems for automatically discriminating between four emotional states (Excited, Relaxed, Frustrated, Bored), two levels of arousal and two levels of valence. The results were very interesting reaching between 69% and 77% of correct discrimination between the four emotional states. Higher results (~89%) were obtained for discriminating between two levels of arousal and two levels of valence