Text or image? Investigating the effects of instruction type on mid-air gesture making with novice older adults

Unlike traditional interaction methods where the same command (e.g. mouse click) is used for different purposes, mid-air gesture interaction often makes use of different gesture commands for different functions, but first novice users need to learn these commands in order to interact with the system successfully. We describe an empirical study with 25 novice older adults that investigated the effectiveness of 3 “on screen” instruction types for demonstrating how to make mid-air gesture commands. We compared three interface design choices for providing instructions: descriptive (text-based), pictorial (static), and pictorial (animated). Results showed a significant advantage of pictorial instructions (static and animated) over text-based instructions for guiding novice older adults in making mid-air gestures with regards to accuracy, completion time and user preference. Pictorial (animated) was the instruction type leading to the fastest gesture making with 100% accuracy and may be the most suitable choice to support age-friendly gesture learning

An investigation of mid-air gesture interaction for older adults

Older adults (60+) face natural and gradual decline in cognitive, sensory and motor functions that are often the reason for the difficulties that older users come up against when interacting with computers. For that reason, the investigation and design of age-inclusive input methods for computer interaction is much needed and relevant due to an ageing population. The advances of motion sensing technologies and mid-air gesture interaction reinvented how individuals can interact with computer interfaces and this modality of input method is often deemed as a more “natural” and “intuitive” than using purely traditional input devices such mouse interaction. Although explored in gaming and entertainment, the suitability of mid-air gesture interaction for older users in particular is still little known. The purpose of this research is to investigate the potential of mid-air gesture interaction to facilitate computer use for older users, and to address the challenges that older adults may face when interacting with gestures in mid-air. This doctoral research is presented as a collection of papers that, together, develop the topic of ageing and computer interaction through mid-air gestures. The initial point for this research was to establish how older users differ from younger users and focus on the challenges faced by older adults when interacting with mid-air gesture interaction. Once these challenges were identified, this work aimed to explore a series of usability challenges and opportunities to further develop age-inclusive interfaces based on mid-air gesture interaction. Through a series of empirical studies, this research intends to provide recommendations for designing mid-air gesture interaction that better take into consideration the needs and skills of the older population and aims to contribute to the advance of age-friendly interfaces

Evaluating the effects of feedback type on older adults’ performance in mid-air pointing and target selection

“Hands-free” pointing techniques used in mid-air gesture interaction require precise motor control and dexterity. Although being applied in a growing number of interaction contexts over the past few years, this input method can be challenging for older users (60+ years old) who experience natural decline in pointing abilities due to natural ageing process. We report the findings of a target acquisition experiment in which older adults had to perform “pointand- select” gestures in mid-air. The experiment investigated the effect of 6 feedback conditions on pointing and selection performance of older users. Our findings suggest that the bimodal combination of Visual and Audio feedback lead to faster target selection times for older adults, but did not lead to making less errors. Furthermore, target location on screen was found to play a more important role in both selection time and accuracy of pointand- select tasks than feedback type

Investigating User Experience Using Gesture-based and Immersive-based Interfaces on Animation Learners

Creating animation is a very exciting activity. However, the long and laborious process can be extremely challenging. Keyframe animation is a complex technique that takes a long time to complete, as the procedure involves changing the poses of characters through modifying the time and space of an action, called frame-by-frame animation. This involves the laborious, repetitive process of constantly reviewing results of the animation in order to make sure the movement-timing is accurate. A new approach to animation is required in order to provide a more intuitive animating experience. With the evolution of interaction design and the Natural User Interface (NUI) becoming widespread in recent years, a NUI-based animation system is expected to allow better usability and efficiency that would benefit animation. This thesis investigates the effectiveness of gesture-based and immersive-based interfaces as part of animation systems. A practice-based element of this research is a prototype of the hand gesture interface, which was created based on experiences from reflective practices. An experimental design is employed to investigate the usability and efficiency of gesture-based and immersive-based interfaces in comparison to the conventional GUI/WIMP interface application. The findings showed that gesture-based and immersive-based interfaces are able to attract animators in terms of the efficiency of the system. However, there was no difference in their preference for usability with the two interfaces. Most of our participants are pleasant with NUI interfaces and new technologies used in the animation process, but for detailed work and taking control of the application, the conventional GUI/WIMP is preferable. Despite the awkwardness of devising gesture-based and immersive-based interfaces for animation, the concept of the system showed potential for a faster animation process, an enjoyable learning system, and stimulating interest in a kinaesthetic learning experience

Understanding Mode and Modality Transfer in Unistroke Gesture Input

Unistroke gestures are an attractive input method with an extensive research history, but one challenge with their usage is that the gestures are not always self-revealing. To obtain expertise with these gestures, interaction designers often deploy a guided novice mode -- where users can rely on recognizing visual UI elements to perform a gestural command. Once a user knows the gesture and associated command, they can perform it without guidance; thus, relying on recall. The primary aim of my thesis is to obtain a comprehensive understanding of why, when, and how users transfer from guided modes or modalities to potentially more efficient, or novel, methods of interaction -- through symbolic-abstract unistroke gestures. The goal of my work is to not only study user behaviour from novice to more efficient interaction mechanisms, but also to expand upon the concept of intermodal transfer to different contexts. We garner this understanding by empirically evaluating three different use cases of mode and/or modality transitions. Leveraging marking menus, the first piece investigates whether or not designers should force expertise transfer by penalizing use of the guided mode, in an effort to encourage use of the recall mode. Second, we investigate how well users can transfer skills between modalities, particularly when it is impractical to present guidance in the target or recall modality. Lastly, we assess how well users' pre-existing spatial knowledge of an input method (the QWERTY keyboard layout), transfers to performance in a new modality. Applying lessons from these three assessments, we segment intermodal transfer into three possible characterizations -- beyond the traditional novice to expert contextualization. This is followed by a series of implications and potential areas of future exploration spawning from our work

Motion-based Interaction for Head-Mounted Displays

Recent advances in affordable sensing technologies have enabled motion-based interaction (MbI) for head-mounted displays (HMDs). Unlike traditional input devices like the mouse and keyboard, which often offer comparatively limited interaction possibilities (e.g., single-touch interaction), MbI does not have these constraints and is more natural because they reflect more closely people do things in real life. However, several issues exist in MbI for HMDs due to the technical limitations of the sensing and tracking devices, higher degrees of freedom afforded to users, and limited research in the area due to the rapid advancement of HMDs and tracking technologies. This thesis first outlines four core challenges in the design space of MbI for HMDs: (1) boundary awareness for hand-based interaction, (2) efficient hands-free head-based interface for HMDs, (3) efficient and feasible full-body interaction for general tasks with HMDs, and (4) accessible full-body interaction for applications in HMDs. Then, this thesis presents an investigation into the contributions of these challenges in MbI for HMDs. The first challenge is addressed by providing visual feedback during interaction tailored for such technologies. The second challenge is addressed by using a circular layout with a go-and-hit selection style for head-based interaction using text entry as the scenario. In addition, this thesis explores additional interaction mechanisms that leverage the affordances of these techniques, and in doing so, we propose directional full-body motions as an interaction approach to perform general tasks with HDMs as an example to address the third challenge. The last challenge is addressed by (1) exploring the differences between performing full-body interaction for HMDs and common displays (i.e., TV) and (2) providing a set of design guidelines that are specific to current and future HMDs. The results of this thesis show that: (1) visual methods for boundary awareness can help with mid-air hand-based interaction in HMDs; (2) head-based interaction and interfaces that take advantages of MbI, such as a circular interface, can be very efficient and low error hands-free input method for HMDs; (3) directional full-body interaction can be a feasible and efficient interaction approach for general tasks involving HMDs; (4) full-body interaction for applications in HMDs should be designed differently than for traditional displays. In addition to these results, this thesis provides a set of design recommendations and takeaway messages for MbI for HMDs

Haptic feedback in freehand gesture interaction

In this thesis work, haptic feedback in gesture interaction was studied. More precisely, focus was on vibrotactile feedback and freehand gestural input methods. Vibrotactile feedback methods have been studied extensively in the fields of touch-based interaction, remote control and mid-air gestural input, and mostly positive effects on user performance have been found. An experiment was conducted in order to investigate if vibrotactile feedback has an impact on user performance in a simple data entry task. In the study, two gestural input methods were compared and the effects of visual and vibrotactile feedback added to each method were examined. Statistically significant differences in task performance between input methods were found. Results also showed that less keystrokes per character were required with visual feedback. No other significant differences were found between the types of feedback. However, preference for vibrotactile feedback was observed. The findings indicate that the careful design of an input method primarily has an impact on user performance and the feedback method can enhance this performance in diverse ways

Optimizing Human Performance in Mobile Text Entry

Although text entry on mobile phones is abundant, research strives to achieve desktop typing performance "on the go". But how can researchers evaluate new and existing mobile text entry techniques? How can they ensure that evaluations are conducted in a consistent manner that facilitates comparison? What forms of input are possible on a mobile device? Do the audio and haptic feedback options with most touchscreen keyboards affect performance? What influences users' preference for one feedback or another? Can rearranging the characters and keys of a keyboard improve performance? This dissertation answers these questions and more. The developed TEMA software allows researchers to evaluate mobile text entry methods in an easy, detailed, and consistent manner. Many in academia and industry have adopted it. TEMA was used to evaluate a typical QWERTY keyboard with multiple options for audio and haptic feedback. Though feedback did not have a significant effect on performance, a survey revealed that users' choice of feedback is influenced by social and technical factors. Another study using TEMA showed that novice users entered text faster using a tapping technique than with a gesture or handwriting technique. This motivated rearranging the keys and characters to create a new keyboard, MIME, that would provide better performance for expert users. Data on character frequency and key selection times were gathered and used to design MIME. A longitudinal user study using TEMA revealed an entry speed of 17 wpm and a total error rate of 1.7% for MIME, compared to 23 wpm and 5.2% for QWERTY. Although MIME's entry speed did not surpass QWERTY's during the study, it is projected to do so after twelve hours of practice. MIME's error rate was consistently low and significantly lower than QWERTY's. In addition, participants found MIME more comfortable to use, with some reporting hand soreness after using QWERTY for extended periods

Discoverable Free Space Gesture Sets for Walk-Up-and-Use Interactions

abstract: Advances in technology are fueling a movement toward ubiquity for beyond-the-desktop systems. Novel interaction modalities, such as free space or full body gestures are becoming more common, as demonstrated by the rise of systems such as the Microsoft Kinect. However, much of the interaction design research for such systems is still focused on desktop and touch interactions. Current thinking in free-space gestures are limited in capability and imagination and most gesture studies have not attempted to identify gestures appropriate for public walk-up-and-use applications. A walk-up-and-use display must be discoverable, such that first-time users can use the system without any training, flexible, and not fatiguing, especially in the case of longer-term interactions. One mechanism for defining gesture sets for walk-up-and-use interactions is a participatory design method called gesture elicitation. This method has been used to identify several user-generated gesture sets and shown that user-generated sets are preferred by users over those defined by system designers. However, for these studies to be successfully implemented in walk-up-and-use applications, there is a need to understand which components of these gestures are semantically meaningful (i.e. do users distinguish been using their left and right hand, or are those semantically the same thing?). Thus, defining a standardized gesture vocabulary for coding, characterizing, and evaluating gestures is critical. This dissertation presents three gesture elicitation studies for walk-up-and-use displays that employ a novel gesture elicitation methodology, alongside a novel coding scheme for gesture elicitation data that focuses on features most important to users’ mental models. Generalizable design principles, based on the three studies, are then derived and presented (e.g. changes in speed are meaningful for scroll actions in walk up and use displays but not for paging or selection). The major contributions of this work are: (1) an elicitation methodology that aids users in overcoming biases from existing interaction modalities; (2) a better understanding of the gestural features that matter, e.g. that capture the intent of the gestures; and (3) generalizable design principles for walk-up-and-use public displays.Dissertation/ThesisDoctoral Dissertation Computer Science 201