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

From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI

This paper gives an overview of the ten-year devel- opment of the papers presented at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI) from 2009 to 2018. We categorize the topics into two main groups, namely, manual driving-related research and automated driving-related re- search. Within manual driving, we mainly focus on studies on user interfaces (UIs), driver states, augmented reality and head-up displays, and methodology; Within automated driv- ing, we discuss topics, such as takeover, acceptance and trust, interacting with road users, UIs, and methodology. We also discuss the main challenges and future directions for AutoUI and offer a roadmap for the research in this area.https://deepblue.lib.umich.edu/bitstream/2027.42/153959/1/From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdfDescription of From Manual Driving to Automated Driving: A Review of 10 Years of AutoUI.pdf : Main articl

The effects of encumbrance and mobility on interactions with touchscreen mobile devices

Mobile handheld devices such as smartphones are now convenient as they allow users to make calls, reply to emails, find nearby services and many more. The increase in functionality and availability of mobile applications also allow mobile devices to be used in many different everyday situations (for example, while on the move and carrying items). While previous work has investigated the interaction difficulties in walking situations, there is a lack of empirical work in the literature on mobile input when users are physically constrained by other activities. As a result, how users input on touchscreen handheld devices in encumbered and mobile contexts is less well known and deserves more attention to examine the usability issues that are often ignored. This thesis investigates targeting performance on touchscreen mobile phones in one common encumbered situation - when users are carrying everyday objects while on the move. To identify the typical objects held during mobile interactions and define a set of common encumbrance scenarios to evaluate in subsequent user studies, Chapter 3 describes an observational study that examined users in different public locations. The results showed that people carried different types of bags and boxes the most frequently. To measure how much tapping performance on touchscreen mobile phones is affected, Chapter 4 examines a range of encumbrance scenarios, which includes holding a bag in-hand or a box underarm, either on the dominant or non-dominant side, during target selections on a mobile phone. Users are likely to switch to a more effective input posture when encumbered and on the move, so Chapter 5 investigates one- and two- handed encumbered interactions and evaluates situations where both hands are occupied with multiple objects. Touchscreen devices afford various multi-touch input types, so Chapter 6 compares the performance of four main one- and two- finger gesture inputs: tapping, dragging, spreading & pinching and rotating, while walking and encumbered. Several main evaluation approaches have been used in previous walking studies, but more attention is required when the effects of encumbrance is also being examined. Chapter 7 examines the appropriateness of two methods (ground and treadmill walking) for encumbered and walking studies, justifies the need to control walking speed and examines the effects of varying walking speed (i.e. walking slower or faster than normal) on encumbered targeting performance. The studies all showed a reduction in targeting performance when users were walking and encumbered, so Chapter 8 explores two ways to improve target selections. The first approach defines a target size, based on the results collected from earlier studies, to increase tapping accuracy and subsequently, a novel interface arrangement was designed which optimises screen space more effectively. The second approach evaluates a benchmark pointing technique, which has shown to improve the selection of small targets, to see if it is useful in walking and encumbered contexts

WearPut : Designing Dexterous Wearable Input based on the Characteristics of Human Finger Motions

Department of Biomedical Engineering (Human Factors Engineering)Powerful microchips for computing and networking allow a wide range of wearable devices to be miniaturized with high fidelity and availability. In particular, the commercially successful smartwatches placed on the wrist drive market growth by sharing the role of smartphones and health management. The emerging Head Mounted Displays (HMDs) for Augmented Reality (AR) and Virtual Reality (VR) also impact various application areas in video games, education, simulation, and productivity tools. However, these powerful wearables have challenges in interaction with the inevitably limited space for input and output due to the specialized form factors for fitting the body parts. To complement the constrained interaction experience, many wearable devices still rely on other large form factor devices (e.g., smartphones or hand-held controllers). Despite their usefulness, the additional devices for interaction can constrain the viability of wearable devices in many usage scenarios by tethering users' hands to the physical devices. This thesis argues that developing novel Human-Computer interaction techniques for the specialized wearable form factors is vital for wearables to be reliable standalone products. This thesis seeks to address the issue of constrained interaction experience with novel interaction techniques by exploring finger motions during input for the specialized form factors of wearable devices. The several characteristics of the finger input motions are promising to enable increases in the expressiveness of input on the physically limited input space of wearable devices. First, the input techniques with fingers are prevalent on many large form factor devices (e.g., touchscreen or physical keyboard) due to fast and accurate performance and high familiarity. Second, many commercial wearable products provide built-in sensors (e.g., touchscreen or hand tracking system) to detect finger motions. This enables the implementation of novel interaction systems without any additional sensors or devices. Third, the specialized form factors of wearable devices can create unique input contexts while the fingers approach their locations, shapes, and components. Finally, the dexterity of fingers with a distinctive appearance, high degrees of freedom, and high sensitivity of joint angle perception have the potential to widen the range of input available with various movement features on the surface and in the air. Accordingly, the general claim of this thesis is that understanding how users move their fingers during input will enable increases in the expressiveness of the interaction techniques we can create for resource-limited wearable devices. This thesis demonstrates the general claim by providing evidence in various wearable scenarios with smartwatches and HMDs. First, this thesis explored the comfort range of static and dynamic touch input with angles on the touchscreen of smartwatches. The results showed the specific comfort ranges on variations in fingers, finger regions, and poses due to the unique input context that the touching hand approaches a small and fixed touchscreen with a limited range of angles. Then, finger region-aware systems that recognize the flat and side of the finger were constructed based on the contact areas on the touchscreen to enhance the expressiveness of angle-based touch input. In the second scenario, this thesis revealed distinctive touch profiles of different fingers caused by the unique input context for the touchscreen of smartwatches. The results led to the implementation of finger identification systems for distinguishing two or three fingers. Two virtual keyboards with 12 and 16 keys showed the feasibility of touch-based finger identification that enables increases in the expressiveness of touch input techniques. In addition, this thesis supports the general claim with a range of wearable scenarios by exploring the finger input motions in the air. In the third scenario, this thesis investigated the motions of in-air finger stroking during unconstrained in-air typing for HMDs. The results of the observation study revealed details of in-air finger motions during fast sequential input, such as strategies, kinematics, correlated movements, inter-fingerstroke relationship, and individual in-air keys. The in-depth analysis led to a practical guideline for developing robust in-air typing systems with finger stroking. Lastly, this thesis examined the viable locations of in-air thumb touch input to the virtual targets above the palm. It was confirmed that fast and accurate sequential thumb touch can be achieved at a total of 8 key locations with the built-in hand tracking system in a commercial HMD. Final typing studies with a novel in-air thumb typing system verified increases in the expressiveness of virtual target selection on HMDs. This thesis argues that the objective and subjective results and novel interaction techniques in various wearable scenarios support the general claim that understanding how users move their fingers during input will enable increases in the expressiveness of the interaction techniques we can create for resource-limited wearable devices. Finally, this thesis concludes with thesis contributions, design considerations, and the scope of future research works, for future researchers and developers to implement robust finger-based interaction systems on various types of wearable devices.ope

Using pressure input and thermal feedback to broaden haptic interaction with mobile devices

Pressure input and thermal feedback are two under-researched aspects of touch in mobile human-computer interfaces. Pressure input could provide a wide, expressive range of continuous input for mobile devices. Thermal stimulation could provide an alternative means of conveying information non-visually. This thesis research investigated 1) how accurate pressure-based input on mobile devices could be when the user was walking and provided with only audio feedback and 2) what forms of thermal stimulation are both salient and comfortable and so could be used to design structured thermal feedback for conveying multi-dimensional information. The first experiment tested control of pressure on a mobile device when sitting and using audio feedback. Targeting accuracy was >= 85% when maintaining 4-6 levels of pressure across 3.5 Newtons, using only audio feedback and a Dwell selection technique. Two further experiments tested control of pressure-based input when walking and found accuracy was very high (>= 97%) even when walking and using only audio feedback, when using a rate-based input method. A fourth experiment tested how well each digit of one hand could apply pressure to a mobile phone individually and in combination with others. Each digit could apply pressure highly accurately, but not equally so, while some performed better in combination than alone. 2- or 3-digit combinations were more precise than 4- or 5-digit combinations. Experiment 5 compared one-handed, multi-digit pressure input using all 5 digits to traditional two-handed multitouch gestures for a combined zooming and rotating map task. Results showed comparable performance, with multitouch being ~1% more accurate but pressure input being ~0.5sec faster, overall. Two experiments, one when sitting indoors and one when walking indoors tested how salient and subjectively comfortable/intense various forms of thermal stimulation were. Faster or larger changes were more salient, faster to detect and less comfortable and cold changes were more salient and faster to detect than warm changes. The two final studies designed two-dimensional structured ‘thermal icons’ that could convey two pieces of information. When indoors, icons were correctly identified with 83% accuracy. When outdoors, accuracy dropped to 69% when sitting and 61% when walking. This thesis provides the first detailed study of how precisely pressure can be applied to mobile devices when walking and provided with audio feedback and the first systematic study of how to design thermal feedback for interaction with mobile devices in mobile environments

Machine learning techniques for implicit interaction using mobile sensors

Interactions in mobile devices normally happen in an explicit manner, which means that they are initiated by the users. Yet, users are typically unaware that they also interact implicitly with their devices. For instance, our hand pose changes naturally when we type text messages. Whilst the touchscreen captures finger touches, hand movements during this interaction however are unused. If this implicit hand movement is observed, it can be used as additional information to support or to enhance the users’ text entry experience. This thesis investigates how implicit sensing can be used to improve existing, standard interaction technique qualities. In particular, this thesis looks into enhancing front-of-device interaction through back-of-device and hand movement implicit sensing. We propose the investigation through machine learning techniques. We look into problems on how sensor data via implicit sensing can be used to predict a certain aspect of an interaction. For instance, one of the questions that this thesis attempts to answer is whether hand movement during a touch targeting task correlates with the touch position. This is a complex relationship to understand but can be best explained through machine learning. Using machine learning as a tool, such correlation can be measured, quantified, understood and used to make predictions on future touch position. Furthermore, this thesis also evaluates the predictive power of the sensor data. We show this through a number of studies. In Chapter 5 we show that probabilistic modelling of sensor inputs and recorded touch locations can be used to predict the general area of future touches on touchscreen. In Chapter 7, using SVM classifiers, we show that data from implicit sensing from general mobile interactions is user-specific. This can be used to identify users implicitly. In Chapter 6, we also show that touch interaction errors can be detected from sensor data. In our experiment, we show that there are sufficient distinguishable patterns between normal interaction signals and signals that are strongly correlated with interaction error. In all studies, we show that performance gain can be achieved by combining sensor inputs

Musical Haptics

Haptic Musical Instruments; Haptic Psychophysics; Interface Design and Evaluation; User Experience; Musical Performanc

Interacting "Through the Display"

The increasing availability of displays at lower costs has led to a proliferation of such in our everyday lives. Additionally, mobile devices are ready to hand and have been proposed as interaction devices for external screens. However, only their input mechanism was taken into account without considering three additional factors in environments hosting several displays: first, a connection needs to be established to the desired target display (modality). Second, screens in the environment may be re-arranged (flexibility). And third, displays may be out of the user’s reach (distance). In our research we aim to overcome the problems resulting from these characteristics. The overall goal is a new interaction model that allows for (1) a non-modal connection mechanism for impromptu use on various displays in the environment, (2) interaction on and across displays in highly flexible environments, and (3) interacting at variable distances. In this work we propose a new interaction model called through the display interaction which enables users to interact with remote content on their personal device in an absolute and direct fashion. To gain a better understanding of the effects of the additional characteristics, we implemented two prototypes each of which investigates a different distance to the target display: LucidDisplay allows users to place their mobile device directly on top of a larger external screen. MobileVue on the other hand enables users to interact with an external screen at a distance. In each of these prototypes we analyzed their effects on the remaining two criteria – namely the modality of the connection mechanism as well as the flexibility of the environment. With the findings gained in this initial phase we designed Shoot & Copy, a system that allows the detection of screens purely based on their visual content. Users aim their personal device’s camera at the target display which then appears in live video shown in the viewfinder. To select an item, users take a picture which is analyzed to determine the targeted region. We further extended this approach to multiple displays by using a centralized component serving as gateway to the display environment. In Tap & Drop we refined this prototype to support real-time feedback. Instead of taking pictures, users can now aim their mobile device at the display resulting and start interacting immediately. In doing so, we broke the rigid sequential interaction of content selection and content manipulation. Both prototypes allow for (1) connections in a non-modal way (i.e., aim at the display and start interacting with it) from the user’s point of view and (2) fully flexible environments (i.e., the mobile device tracks itself with respect to displays in the environment). However, the wide-angle lenses and thus greater field of views of current mobile devices still do not allow for variable distances. In Touch Projector, we overcome this limitation by introducing zooming in combination with temporarily freezing the video image. Based on our extensions to taxonomy of mobile device interaction on external displays, we created a refined model of interacting through the display for mobile use. It enables users to interact impromptu without explicitly establishing a connection to the target display (non-modal). As the mobile device tracks itself with respect to displays in the environment, the model further allows for full flexibility of the environment (i.e., displays can be re-arranged without affecting on the interaction). And above all, users can interact with external displays regardless of their actual size at variable distances without any loss of accuracy.Die steigende Verfügbarkeit von Bildschirmen hat zu deren Verbreitung in unserem Alltag geführt. Ferner sind mobile Geräte immer griffbereit und wurden bereits als Interaktionsgeräte für zusätzliche Bildschirme vorgeschlagen. Es wurden jedoch nur Eingabemechanismen berücksichtigt ohne näher auf drei weitere Faktoren in Umgebungen mit mehreren Bildschirmen einzugehen: (1) Beide Geräte müssen verbunden werden (Modalität). (2) Bildschirme können in solchen Umgebungen umgeordnet werden (Flexibilität). (3) Monitore können außer Reichweite sein (Distanz). Wir streben an, die Probleme, die durch diese Eigenschaften auftreten, zu lösen. Das übergeordnete Ziel ist ein Interaktionsmodell, das einen nicht-modalen Verbindungsaufbau für spontane Verwendung von Bildschirmen in solchen Umgebungen, (2) Interaktion auf und zwischen Bildschirmen in flexiblen Umgebungen, und (3) Interaktionen in variablen Distanzen erlaubt. Wir stellen ein Modell (Interaktion durch den Bildschirm) vor, mit dem Benutzer mit entfernten Inhalten in direkter und absoluter Weise auf ihrem Mobilgerät interagieren können. Um die Effekte der hinzugefügten Charakteristiken besser zu verstehen, haben wir zwei Prototypen für unterschiedliche Distanzen implementiert: LucidDisplay erlaubt Benutzern ihr mobiles Gerät auf einen größeren, sekundären Bildschirm zu legen. Gegensätzlich dazu ermöglicht MobileVue die Interaktion mit einem zusätzlichen Monitor in einer gewissen Entfernung. In beiden Prototypen haben wir dann die Effekte der verbleibenden zwei Kriterien (d.h. Modalität des Verbindungsaufbaus und Flexibilität der Umgebung) analysiert. Mit den in dieser ersten Phase erhaltenen Ergebnissen haben wir Shoot & Copy entworfen. Dieser Prototyp erlaubt die Erkennung von Bildschirmen einzig über deren visuellen Inhalt. Benutzer zeigen mit der Kamera ihres Mobilgeräts auf einen Bildschirm dessen Inhalt dann in Form von Video im Sucher dargestellt wird. Durch die Aufnahme eines Bildes (und der darauf folgenden Analyse) wird Inhalt ausgewählt. Wir haben dieses Konzept zudem auf mehrere Bildschirme erweitert, indem wir eine zentrale Instanz verwendet haben, die als Schnittstelle zur Umgebung agiert. Mit Tap & Drop haben wir den Prototyp verfeinert, um Echtzeit-Feedback zu ermöglichen. Anstelle der Bildaufnahme können Benutzer nun ihr mobiles Gerät auf den Bildschirm richten und sofort interagieren. Dadurch haben wir die strikt sequentielle Interaktion (Inhalt auswählen und Inhalt manipulieren) aufgebrochen. Beide Prototypen erlauben bereits nicht-modale Verbindungsmechanismen in flexiblen Umgebungen. Die in heutigen Mobilgeräten verwendeten Weitwinkel-Objektive erlauben jedoch nach wie vor keine variablen Distanzen. Mit Touch Projector beseitigen wir diese Einschränkung, indem wir Zoomen in Kombination mit einer vorübergehenden Pausierung des Videos im Sucher einfügen. Basierend auf den Erweiterungen der Klassifizierung von Interaktionen mit zusätzlichen Bildschirmen durch mobile Geräte haben wir ein verbessertes Modell (Interaktion durch den Bildschirm) erstellt. Es erlaubt Benutzern spontan zu interagieren, ohne explizit eine Verbindung zum zweiten Bildschirm herstellen zu müssen (nicht-modal). Da das mobile Gerät seinen räumlichen Bezug zu allen Bildschirmen selbst bestimmt, erlaubt unser Modell zusätzlich volle Flexibilität in solchen Umgebungen. Darüber hinaus können Benutzer mit zusätzlichen Bildschirmen (unabhängig von deren Größe) in variablen Entfernungen interagieren