Quantifying epistemic actions in human-computer interaction

As digital systems move away from traditional desktop setups, new interaction paradigms are emerging that better integrate with users’ realworld surroundings, and better support users’ individual needs. While promising, these modern interaction paradigms also present new challenges, such as a lack of paradigm-specific tools to systematically evaluate and fully understand their use. This dissertation tackles this issue by framing empirical studies of three novel digital systems in embodied cognition – an exciting new perspective in cognitive science where the body and its interactions with the physical world take a central role in human cognition. This is achieved by first, focusing the design of all these systems on a contemporary interaction paradigm that emphasizes physical interaction on tangible interaction, a contemporary interaction paradigm; and second, by comprehensively studying user performance in these systems through a set of novel performance metrics grounded on epistemic actions, a relatively well established and studied construct in the literature on embodied cognition. The first system presented in this dissertation is an augmented Four-in-a-row board game. Three different versions of the game were developed, based on three different interaction paradigms (tangible, touch and mouse), and a repeated measures study involving 36 participants measured the occurrence of three simple epistemic actions across these three interfaces. The results highlight the relevance of epistemic actions in such a task and suggest that the different interaction paradigms afford instantiation of these actions in different ways. Additionally, the tangible version of the system supports the most rapid execution of these actions, providing novel quantitative insights into the real benefits of tangible systems. The second system presented in this dissertation is a tangible tabletop scheduling application. Two studies with single and paired users provide several insights into the impact of epistemic actions on the user experience when these are performed outside of a system’s sensing boundaries. These insights are clustered by the form, size and location of ideal interface areas for such offline epistemic actions to occur, as well as how can physical tokens be designed to better support them. Finally, and based on the results obtained to this point, the last study presented in this dissertation directly addresses the lack of empirical tools to formally evaluate tangible interaction. It presents a video-coding framework grounded on a systematic literature review of 78 papers, and evaluates its value as metric through a 60 participant study performed across three different research laboratories. The results highlight the usefulness and power of epistemic actions as a performance metric for tangible systems. In sum, through the use of such novel metrics in each of the three studies presented, this dissertation provides a better understanding of the real impact and benefits of designing and developing systems that feature tangible interaction.

Measurement and management of genotype-environment interaction (GxE) for the improvement of rainfed lowland rice yield in Cambodia

The magnitude and nature of genotype-by-environment interactions (G×E) for grain yield (GY) and days to flower (DTF) in Cambodia were examined using a random population of 34 genotypes taken from the Cambodian rice improvement program. These genotypes were evaluated in multi-environment trials (MET) conducted across three years (2000 to 2002) and eight locations in the rainfed lowlands. The G×E interaction was partitioned into components attributed to genotype-by-location (G×L), genotype-by-year (G×Y) and genotype-by-location-by-year (G×L×Y) interactions. The G×L×Y interaction was the largest component of variance for GY. The G×L interaction was also significant and comparable in size to the genotypic component (G). The G×Y interaction was small and non significant. A major factor contributing to the large G×L×Y interactions for GY was the genotypic variation for DTF in combination with environmental variation for the timing and intensity of drought. Some of the interactions for GY associated with timing of plant development and exposure to drought were repeatable across the environments enabling the identification of three-target populations of environments (TPE) for consideration in the breeding program. Four genotypes were selected for wide adaptation in the rainfed lowlands in Cambodia.

Formal analysis of human-computer interaction using model checking

Experiments with simulators allow psychologists to better understand the causes of human errors and build models of cognitive processes to be used in human reliability assessment (HRA). This paper investigates an approach to task failure analysis based on patterns of behaviour, by contrast to more traditional event-based approaches. It considers, as a case study, a formal model of an air traffic control (ATC) system which incorporates controller behaviour. The cognitive model is formalised in the CSP process algebra. Patterns of behaviour are expressed as temporal logic properties. Then a model-checking technique is used to verify whether the decomposition of the operator's behaviour into patterns is sound and complete with respect to the cognitive model. The decomposition is shown to be incomplete and a new behavioural pattern is identified, which appears to have been overlooked in the analysis of the data provided by the experiments with the simulator. This illustrates how formal analysis of operator models can yield fresh insights into how failures may arise in interactive systems.

Individual differences in human-computer interaction

This thesis initially presents an 'assay' of the literature pertaining to individual differences in human-computer interaction. A series of experiments is then reported, designed to investigate the association between a variety of individual characteristics and various computer task and interface factors. Predictor variables included age, computer expertise, and psychometric tests of spatial visualisation, spatial memory, logical reasoning, associative memory, and verbal ability. These were studied in relation to a variety of computer-based tacks, including: (1) word processing and its component elements; (ii) the location of target words within passages of text; (iii) the navigation of networks and menus; (iv) command generation using menus and command line interfaces; (v) the search and selection of icons and text labels; (vi) information retrieval. A measure of self-report workload was also included in several of these experiments. The main experimental findings included: (i) an interaction between spatial ability and the manipulation of semantic but not spatial interface content; (ii) verbal ability being only predictive of certain task components of word processing; (iii) age differences in word processing and information retrieval speed but not accuracy; (iv) evidence of compensatory strategies being employed by older subjects; (v) evidence of performance strategy differences which disadvantaged high spatial subjects in conditions of low spatial information content; (vi) interactive effects of associative memory, expertise and command strategy; (vii) an association between logical reasoning and word processing but not information retrieval; (viii) an interaction between expertise and cognitive demand; and (ix) a stronger association between cognitive ability and novice performance than expert performance.

Human-computer interaction and innovation in handheld, mobile and wearable technologies

Handheld and mobile technologies have witnessed significant advances in functionality, leading to their widespread use as both business and social networking tools. Human-Computer Interaction and Innovation in Handheld, Mobile and Wearable Technologies reviews concepts relating to the design, development, evaluation, and application of mobile technologies. Studies on mobile user interfaces, mobile learning, and mobile commerce contribute to the growing body of knowledge on this expanding discipline.

The influence of gene environment interaction on the risk of cognitive impairment: Reducing sexual risk behaviors and alcohol use in HIV-infected adults

Memory deficits and executive dysfunction are highly prevalent among HIV-infected adults. These conditions can affect their quality of life, antiretroviral adherence, and HIV risk behaviors. Several factors have been suggested including the role of genetics in relation to HIV disease progression. This dissertation aimed to determine whether genetic differences in HIV-infected individuals were correlated with impaired memory, cognitive flexibility and executive function and whether cognitive decline moderated alcohol use and sexual transmission risk behaviors among HIV-infected alcohol abusers participating in an NIH-funded clinical trial comparing the efficacy of the adapted Holistic Health Recovery Program (HHRP-A) intervention to a Health Promotion Control (HPC) condition in reducing risk behaviors. ^ A total of 267 individuals were genotyped for polymorphisms in the dopamine and serotonin gene systems. Results yielded significant associations for TPH2, GALM, DRD2 and DRD4 genetic variants with impaired executive function, cognitive flexibility and memory. SNPs TPH2 rs4570625 and DRD2 rs6277 showed a risk association with executive function (odds ratio = 2.5, p = .02; 3.6, p = .001). GALM rs6741892 was associated with impaired memory (odds ratio = 1.9, p = .006). At the six-month follow-up, HHRP-A participants were less likely to report trading sex for food, drugs and money (20.0%) and unprotected insertive or receptive oral (11.6%) or vaginal and/or anal sex (3.2%) than HPC participants (49.4%, p^

The Influence of Gene Environment Interaction on the Risk of Cognitive Impairment: Reducing Sexual Risk Behaviors and Alcohol Use in HIV-infected Adults

Memory deficits and executive dysfunction are highly prevalent among HIV-infected adults. These conditions can affect their quality of life, antiretroviral adherence, and HIV risk behaviors. Several factors have been suggested including the role of genetics in relation to HIV disease progression. This dissertation aimed to determine whether genetic differences in HIV-infected individuals were correlated with impaired memory, cognitive flexibility and executive function and whether cognitive decline moderated alcohol use and sexual transmission risk behaviors among HIV-infected alcohol abusers participating in an NIH-funded clinical trial comparing the efficacy of the adapted Holistic Health Recovery Program (HHRP-A) intervention to a Health Promotion Control (HPC) condition in reducing risk behaviors. A total of 267 individuals were genotyped for polymorphisms in the dopamine and serotonin gene systems. Results yielded significant associations for TPH2, GALM, DRD2 and DRD4 genetic variants with impaired executive function, cognitive flexibility and memory. SNPs TPH2 rs4570625 and DRD2 rs6277 showed a risk association with executive function (odds ratio = 2.5, p = .02; 3.6, p = .001). GALM rs6741892 was associated with impaired memory (odds ratio = 1.9, p = .006). At the six-month follow-up, HHRP-A participants were less likely to report trading sex for food, drugs and money (20.0%) and unprotected insertive or receptive oral (11.6%) or vaginal and/or anal sex (3.2%) than HPC participants (49.4%, p

CASIMIRO: a robot head for human-computer interaction

The physical appearance and behavior of a robot is an important asset in terms of Human-Computer Interaction. Multimodality is also fundamental, as we humans usually expect to interact in a natural way with voice, gestures, etc. People approach complex interaction devices with stances similar to those used in their interaction with other people. In this paper we describe a robot head, currently under development, that aims to be a multimodal (vision, voice, gestures,...) perceptual user interface.

Fast and accurate hand pose detection for human-robot interaction

[EN]Enabling natural human-robot interaction using computer vision based applications requires fast and accurate hand detection. However, previous works in this field assume different constraints, like a limitation in the number of detected gestures, because hands are highly complex objects difficult to locate. This paper presents an approach which integrates temporal coherence cues and hand detection based on wrists using a cascade classifier. With this approach, we introduce three main contributions: (1) a transparent initialization mechanism without user participation for segmenting hands independently of their gesture, (2) a larger number of detected gestures as well as a faster training phase than previous cascade classifier based methods and (3) near real-time performance for hand pose detection in video streams.

Using Research Methods in Human Computer Interaction to Design Technology for Resilience

Research in human computer interaction (HCI) covers both technological and human behavioural concerns. As a consequence, the contributions made in HCI research tend to be aware to either engineering or the social sciences. In HCI the purpose of practical research contributions is to reveal unknown insights about human behaviour and its relationship to technology. Practical research methods normally used in HCI include formal experiments, field experiments, field studies, interviews, focus groups, surveys, usability tests, case studies, diary studies, ethnography, contextual inquiry, experience sampling, and automated data collection. In this paper, we report on our experience using the evaluation methods focus groups, surveys and interviews and how we adopted these methods to develop artefacts: either interface’s design or information and technological systems. Four projects are examples of the different methods application to gather information about user’s wants, habits, practices, concerns and preferences. The goal was to build an understanding of the attitudes and satisfaction of the people who might interact with a technological artefact or information system. Conversely, we intended to design for information systems and technological applications, to promote resilience in organisations (a set of routines that allow to recover from obstacles) and user’s experiences. Organisations can here also be viewed within a system approach, which means that the system perturbations even failures could be characterized and improved. The term resilience has been applied to everything from the real estate, to the economy, sports, events, business, psychology, and more. In this study, we highlight that resilience is also made up of a number of different skills and abilities (self-awareness, creating meaning from other experiences, self-efficacy, optimism, and building strong relationships) that are a few foundational ingredients, which people should use along with the process of enhancing an organisation’s resilience. Resilience enhances knowledge of resources available to people confronting existing problems.

Human-Computer interaction methodologies applied in the evaluation of haptic digital musical instruments

Recent developments in interactive technologies have seen major changes in the manner in which artists, performers, and creative individuals interact with digital music technology; this is due to the increasing variety of interactive technologies that are readily available today. Digital Musical Instruments (DMIs) present musicians with performance challenges that are unique to this form of computer music. One of the most significant deviations from conventional acoustic musical instruments is the level of physical feedback conveyed by the instrument to the user. Currently, new interfaces for musical expression are not designed to be as physically communicative as acoustic instruments. Specifically, DMIs are often void of haptic feedback and therefore lack the ability to impart important performance information to the user. Moreover, there currently is no standardised way to measure the effect of this lack of physical feedback. Best practice would expect that there should be a set of methods to effectively, repeatedly, and quantifiably evaluate the functionality, usability, and user experience of DMIs. Earlier theoretical and technological applications of haptics have tried to address device performance issues associated with the lack of feedback in DMI designs and it has been argued that the level of haptic feedback presented to a user can significantly affect the user’s overall emotive feeling towards a musical device. The outcome of the investigations contained within this thesis are intended to inform new haptic interface.

A take on complexity: bio-molecules and human metabolism interaction modelling for health and nutrition with machine learning

The advent of omic data production has opened many new perspectives in the quest for modelling complexity in biophysical systems. With the capability of characterizing a complex organism through the patterns of its molecular states, observed at different levels through various omics, a new paradigm of investigation is arising. In this thesis, we investigate the links between perturbations of the human organism, described as the ensemble of crosstalk of its molecular states, and health. Machine learning plays a key role within this picture, both in omic data analysis and model building. We propose and discuss different frameworks developed by the author using machine learning for data reduction, integration, projection on latent features, pattern analysis, classification and clustering of omic data, with a focus on 1H NMR metabolomic spectral data. The aim is to link different levels of omic observations of molecular states, from nanoscale to macroscale, to study perturbations such as diseases and diet interpreted as changes in molecular patterns. The first part of this work focuses on the fingerprinting of diseases, linking cellular and systemic metabolomics with genomic to asses and predict the downstream of perturbations all the way down to the enzymatic network. The second part is a set of frameworks and models, developed with 1H NMR metabolomic at its core, to study the exposure of the human organism to diet and food intake in its full complexity, from epidemiological data analysis to molecular characterization of food structure.

Dynamic Human Robot Interaction Framework Using Deep Learning and Robot Operating System (ROS): a practical approach

Trying to explain to a robot what to do is a difficult undertaking, and only specific types of people have been able to do so far, such as programmers or operators who have learned how to use controllers to communicate with a robot. My internship's goal was to create and develop a framework that would make that easier. The system uses deep learning techniques to recognize a set of hand gestures, both static and dynamic. Then, based on the gesture, it sends a command to a robot. To be as generic as feasible, the communication is implemented using Robot Operating System (ROS). Furthermore, users can add new recognizable gestures and link them to new robot actions; a finite state automaton enforces the users' input verification and correct action sequence. Finally, the users can create and utilize a macro to describe a sequence of actions performable by a robot.