Barehand Mode Switching in Touch and Mid-Air Interfaces

Raskin defines a mode as a distinct setting within an interface where the same user input will produce results different to those it would produce in other settings. Most interfaces have multiple modes in which input is mapped to different actions, and, mode-switching is simply the transition from one mode to another. In touch interfaces, the current mode can change how a single touch is interpreted: for example, it could draw a line, pan the canvas, select a shape, or enter a command. In Virtual Reality (VR), a hand gesture-based 3D modelling application may have different modes for object creation, selection, and transformation. Depending on the mode, the movement of the hand is interpreted differently. However, one of the crucial factors determining the effectiveness of an interface is user productivity. Mode-switching time of different input techniques, either in a touch interface or in a mid-air interface, affects user productivity. Moreover, when touch and mid-air interfaces like VR are combined, making informed decisions pertaining to the mode assignment gets even more complicated. This thesis provides an empirical investigation to characterize the mode switching phenomenon in barehand touch-based and mid-air interfaces. It explores the potential of using these input spaces together for a productivity application in VR. And, it concludes with a step towards defining and evaluating the multi-faceted mode concept, its characteristics and its utility, when designing user interfaces more generally

Exploring the Potential of Wrist-Worn Gesture Sensing

This thesis aims to explore the potential of wrist-worn gesture sensing. There has been a large amount of work on gesture recognition in the past utilizing different kinds of sensors. However, gesture sets tested across different work were all different, making it hard to compare them. Also, there has not been enough work on understanding what types of gestures are suitable for wrist-worn devices. Our work addresses these two problems and makes two main contributions compared to previous work: the specification of larger gesture sets, which were verified through an elicitation study generated by combining previous work; and an evaluation of the potential of gesture sensing with wrist-worn sensors. We developed a gesture recognition system, WristRec, which is a low-cost wrist-worn device utilizing bend sensors for gesture recognition. The design of WristRec aims to measure the tendon movement at the wrist while people perform gestures. We conducted a four-part study to verify the validity of the approach and the extent of gestures which can be detected using a wrist-worn system. During the initial stage, we verified the feasibility of WristRec using the Dynamic Time Warping (DTW) algorithm to perform gesture classification on a group of 5 gestures, the gesture set of the MYO armband. Next, we conducted an elicitation study to understand the trade-offs between hand, wrist, and arm gestures. The study helped us understand the type of gestures which wrist-worn system should be able to recognize. It also served as the base of our gesture set and our evaluation on the gesture sets used in the previous research. To evaluate the overall potential of wrist-worn recognition, we explored the design of hardware to recognize gestures by contrasting an Inertial measurement unit (IMU) only recognizer (the Serendipity system of Wen et al.) with our system. We assessed accuracies on a consensus gesture set and on a 27-gesture referent set, both extracted from the result of our elicitation study. Finally, we discuss the implications of our work both to the comparative evaluation of systems and to the design of enhanced hardware sensing

Deep learning-based artificial vision for grasp classification in myoelectric hands

Objective. Computer vision-based assistive technology solutions can revolutionise the quality of care for people with sensorimotor disorders. The goal of this work was to enable trans-radial amputees to use a simple, yet efficient, computer vision system to grasp and move common household objects with a two-channel myoelectric prosthetic hand. Approach. We developed a deep learning-based artificial vision system to augment the grasp functionality of a commercial prosthesis. Our main conceptual novelty is that we classify objects with regards to the grasp pattern without explicitly identifying them or measuring their dimensions. A convolutional neural network (CNN) structure was trained with images of over 500 graspable objects. For each object, 72 images, at ${{5}^{\circ}}$ intervals, were available. Objects were categorised into four grasp classes, namely: pinch, tripod, palmar wrist neutral and palmar wrist pronated. The CNN setting was first tuned and tested offline and then in realtime with objects or object views that were not included in the training set. Main results. The classification accuracy in the offline tests reached $85 \%$ for the seen and $75 \%$ for the novel objects; reflecting the generalisability of grasp classification. We then implemented the proposed framework in realtime on a standard laptop computer and achieved an overall score of $84 \%$ in classifying a set of novel as well as seen but randomly-rotated objects. Finally, the system was tested with two trans-radial amputee volunteers controlling an i-limb UltraTM prosthetic hand and a motion controlTM prosthetic wrist; augmented with a webcam. After training, subjects successfully picked up and moved the target objects with an overall success of up to $88 \%$ . In addition, we show that with training, subjects' performance improved in terms of time required to accomplish a block of 24 trials despite a decreasing level of visual feedback. Significance. The proposed design constitutes a substantial conceptual improvement for the control of multi-functional prosthetic hands. We show for the first time that deep-learning based computer vision systems can enhance the grip functionality of myoelectric hands considerably

User-based gesture vocabulary for form creation during a product design process

There are inconsistencies between the nature of the conceptual design and the functionalities of the computational systems supporting it, which disrupt the designers’ process, focusing on technology rather than designers’ needs. A need for elicitation of hand gestures appropriate for the requirements of the conceptual design, rather than those arbitrarily chosen or focusing on ease of implementation was identified.The aim of this thesis is to identify natural and intuitive hand gestures for conceptual design, performed by designers (3rd, 4th year product design engineering students and recent graduates) working on their own, without instruction and without limitations imposed by the facilitating technology. This was done via a user centred study including 44 participants. 1785 gestures were collected. Gestures were explored as a sole mean for shape creation and manipulation in virtual 3D space. Gestures were identified, described in writing, sketched, coded based on the taxonomy used, categorised based on hand form and the path travelled and variants identified. Then they were statistically analysed to ascertain agreement rates between the participants, significance of the agreement and the likelihood of number of repetitions for each category occurring by chance. The most frequently used and statistically significant gestures formed the consensus set of vocabulary for conceptual design. The effect of the shape of the manipulated object on the gesture performed, and if the sequence of the gestures participants proposed was different from the established CAD solid modelling practices were also observed.Vocabulary was evaluated by non-designer participants, and the outcomes have shown that the majority of gestures were appropriate and easy to perform. Evaluation was performed theoretically and in the VR environment. Participants selected their preferred gestures for each activity, and a variant of the vocabulary for conceptual design was created as an outcome, that aims to ensure that extensive training is not required, extending the ability to design beyond trained designers only.There are inconsistencies between the nature of the conceptual design and the functionalities of the computational systems supporting it, which disrupt the designers’ process, focusing on technology rather than designers’ needs. A need for elicitation of hand gestures appropriate for the requirements of the conceptual design, rather than those arbitrarily chosen or focusing on ease of implementation was identified.The aim of this thesis is to identify natural and intuitive hand gestures for conceptual design, performed by designers (3rd, 4th year product design engineering students and recent graduates) working on their own, without instruction and without limitations imposed by the facilitating technology. This was done via a user centred study including 44 participants. 1785 gestures were collected. Gestures were explored as a sole mean for shape creation and manipulation in virtual 3D space. Gestures were identified, described in writing, sketched, coded based on the taxonomy used, categorised based on hand form and the path travelled and variants identified. Then they were statistically analysed to ascertain agreement rates between the participants, significance of the agreement and the likelihood of number of repetitions for each category occurring by chance. The most frequently used and statistically significant gestures formed the consensus set of vocabulary for conceptual design. The effect of the shape of the manipulated object on the gesture performed, and if the sequence of the gestures participants proposed was different from the established CAD solid modelling practices were also observed.Vocabulary was evaluated by non-designer participants, and the outcomes have shown that the majority of gestures were appropriate and easy to perform. Evaluation was performed theoretically and in the VR environment. Participants selected their preferred gestures for each activity, and a variant of the vocabulary for conceptual design was created as an outcome, that aims to ensure that extensive training is not required, extending the ability to design beyond trained designers only