Utilising presence in places to support mobile interaction

Physical places are given contextual meaning by the objects and people that make up the space. Presence in physical places can be utilised to support mobile interaction by making access to media and notifications on a smartphone easier and more visible to other people. Smartphone interfaces can be extended into the physical world in a meaningful way by anchoring digital content to artefacts, and interactions situated around physical artefacts can provide contextual meaning to private manipulations with a mobile device. Additionally, places themselves are designed to support a set of tasks, and the logical structure of places can be used to organise content on the smartphone. Menus that adapt the functionality of a smartphone can support the user by presenting the tools most likely to be needed just-in-time, so that information needs can be satisfied quickly and with little cognitive effort. Furthermore, places are often shared with people whom the user knows, and the smartphone can facilitate social situations by providing access to content that stimulates conversation. However, the smartphone can disrupt a collaborative environment, by alerting the user with unimportant notifications, or sucking the user in to the digital world with attractive content that is only shown on a private screen. Sharing smartphone content on a situated display creates an inclusive and unobtrusive user experience, and can increase focus on a primary task by allowing content to be read at a glance. Mobile interaction situated around artefacts of personal places is investigated as a way to support users to access content from their smartphone while managing their physical presence. A menu that adapts to personal places is evaluated to reduce the time and effort of app navigation, and coordinating smartphone content on a situated display is found to support social engagement and the negotiation of notifications. Improving the sensing of smartphone users in places is a challenge that is out-with the scope of this thesis. Instead, interaction designers and developers should be provided with low-cost positioning tools that utilise presence in places, and enable quantitative and qualitative data to be collected in user evaluations. Two lightweight positioning tools are developed with the low-cost sensors that are currently available: The Microsoft Kinect depth sensor allows movements of a smartphone user to be tracked in a limited area of a place, and Bluetooth beacons enable the larger context of a place to be detected. Positioning experiments with each sensor are performed to highlight the capabilities and limitations of current sensing techniques for designing interactions with a smartphone. Both tools enable prototypes to be built with a rapid prototyping approach, and mobile interactions can be tested with more advanced sensing techniques as they become available. Sensing technologies are becoming pervasive, and it will soon be possible to perform reliable place detection in-the-wild. Novel interactions that utilise presence in places can support smartphone users by making access to useful functionality easy and more visible to the people who matter most in everyday life.

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.

Interaction techniques with novel multimodal feedback for addressing gesture-sensing systems

Users need to be able to address in-air gesture systems, which means finding where to perform gestures and how to direct them towards the intended system. This is necessary for input to be sensed correctly and without unintentionally affecting other systems. This thesis investigates novel interaction techniques which allow users to address gesture systems properly, helping them find where and how to gesture. It also investigates audio, tactile and interactive light displays for multimodal gesture feedback; these can be used by gesture systems with limited output capabilities (like mobile phones and small household controls), allowing the interaction techniques to be used by a variety of device types. It investigates tactile and interactive light displays in greater detail, as these are not as well understood as audio displays. Experiments 1 and 2 explored tactile feedback for gesture systems, comparing an ultrasound haptic display to wearable tactile displays at different body locations and investigating feedback designs. These experiments found that tactile feedback improves the user experience of gesturing by reassuring users that their movements are being sensed. Experiment 3 investigated interactive light displays for gesture systems, finding this novel display type effective for giving feedback and presenting information. It also found that interactive light feedback is enhanced by audio and tactile feedback. These feedback modalities were then used alongside audio feedback in two interaction techniques for addressing gesture systems: sensor strength feedback and rhythmic gestures. Sensor strength feedback is multimodal feedback that tells users how well they can be sensed, encouraging them to find where to gesture through active exploration. Experiment 4 found that they can do this with 51mm accuracy, with combinations of audio and interactive light feedback leading to the best performance. Rhythmic gestures are continuously repeated gesture movements which can be used to direct input. Experiment 5 investigated the usability of this technique, finding that users can match rhythmic gestures well and with ease. Finally, these interaction techniques were combined, resulting in a new single interaction for addressing gesture systems. Using this interaction, users could direct their input with rhythmic gestures while using the sensor strength feedback to find a good location for addressing the system. Experiment 6 studied the effectiveness and usability of this technique, as well as the design space for combining the two types of feedback. It found that this interaction was successful, with users matching 99.9% of rhythmic gestures, with 80mm accuracy from target points. The findings show that gesture systems could successfully use this interaction technique to allow users to address them. Novel design recommendations for using rhythmic gestures and sensor strength feedback were created, informed by the experiment findings.