Attention computing:overview of mobile sensing applied to measuring attention

Abstract The measurement of participant attention is a frequent by-product of mobile sensing-based studies, which typically focus on user interruptibility or the effectiveness of notification deliveries. We note that, despite the popularity of interruptibility research within our discipline, research focused on attention is surprisingly scarce. This omission may be due to (a combination of) methodological, technological, or disciplinary constraints. In this paper, we argue how attention levels can be effectively measured with existing technologies and methodologies by adapting continuous measurements of attention fluctuations. Many clinically researched technologies, as well as sensing-based analysis methods, could be leveraged for this purpose. This paper invites co-researchers to assess the use of novel ways to measure attention in their future endeavours

Crowdsourcing personalized weight loss diets

Abstract The Diet Explorer is a lightweight system that relies on aggregated human insights for assessing and recommending suitable weight loss diets. We compared its performance against Google and suggest that the system, bootstrapped using a public crowdsourcing platform, provides comparable results in terms of overall satisfaction, relevance, and trustworthiness

Human-centred artificial intelligence:a contextual morality perspective

Abstract The emergence of big data combined with the technical developments in Artificial Intelligence has enabled novel opportunities for autonomous and continuous decision support. While initial work has begun to explore how human morality can inform the decision making of future Artificial Intelligence applications, these approaches typically consider human morals as static and immutable. In this work, we present an initial exploration of the effect of context on human morality from a Utilitarian perspective. Through an online narrative transportation study, in which participants are primed with either a positive story, a negative story or a control condition (N = 82), we collect participants’ perceptions on technology that has to deal with moral judgment in changing contexts. Based on an in-depth qualitative analysis of participant responses, we contrast participant perceptions to related work on Fairness, Accountability and Transparency. Our work highlights the importance of contextual morality for Artificial Intelligence and identifies opportunities for future work through a FACT-based (Fairness, Accountability, Context and Transparency) perspective

Developing guidelines for managing processes by objectives

Syftet med examensarbetet var att ta fram generella riktlinjer för att målstyra processer på Volvo Bussar AB. Dessa riktlinjer skulle sedan testas på tre pilotprocesser, som var utvalda så att de skulle representera olika typer av processer. Detta för att bekräfta att riktlinjerna verkligen var av generell natur. Resultatet av examensarbetet består huvudsakligen av slutversionen av riktlinjerna, som består av fem olika delar. Den första delen, metoden för implementering av prestandamätningar, beskriver steg för steg hur man implementerar mätningar på ett bra sätt. Del två, exempellistan, ger exempel på lämpliga mått inom områdena tid, kostnad och kvalitet. Dessa exempel är tänkta att fungera som en vägledning och inspiration vid val av mått. När man identifierat ett antal mått kan de jämföras mot måttegenskaperna i del tre av riktlinjerna, för att avgöra vilka som är lämpligast att införa. Faktordefinitionerna i fjärde delen fungerar som ett stöd vid funderingar över vad begreppen innebär och vilka mått som bäst beskriver dessa faktorer. Tillvägagångssättet för uppföljning i del fem ger ett företagsspecifikt förslag på hur man ska följa upp resultatet vid mätningar, så att målstyrning av processerna säkerställs. Tillvägagångssättet är också kompletterat med ett förslag på rapportformat för mätningsresultat som är lätt att använda och lämpar sig bra för analyser. De utvalda pilotprocesserna inkluderade ingen ledningsprocess, vilket gav upphov till tvekan angående riktlinjernas generella natur. Då det finns många likheter mellan stödprocesser och ledningsprocesser drogs ändå slutsatsen att riktlinjerna även kan användas på ledningsprocesser och därför kan betecknas som generella.Validerat; 20101217 (root

Gamification of mobile experience sampling improves data quality and quantity

Abstract The Experience Sampling Method is used to capture high-quality in situ data from study participants. This method has become popular in studies involving smartphones, where it is often adapted to motivate participation through the use of gamification techniques. However, no work to date has evaluated whether gamification actually affects the quality and quantity of data collected through Experience Sampling. Our study systematically investigates the effect of gamification on the quantity and quality of experience sampling responses on smartphones. In a field study, we combine event contingent and interval contingent triggers to ask participants to describe their location. Subsequently, participants rate the quality of these entries by playing a game with a purpose. Our results indicate that participants using the gamified version of our ESM software provided significantly higher quality responses, slightly increased their response rate, and provided significantly more data on their own accord. Our findings suggest that gamifying experience sampling can improve data collection and quality in mobile settings

Capturing contextual morality:applying game theory on smartphones

Abstract In order to build more fair Artificial Intelligence applications, a thorough understanding of human morality is required. Given the variable nature of human moral values, AI algorithms will have to adjust their behaviour based on the moral values of its users in order to align with end user expectations. Quantifying human moral values is, however, a challenging task which cannot easily be completed using e.g. surveys. In order to address this problem, we propose the use of game theory in longitudinal mobile sensing deployments. Game theory has long been used in disciplines such as Economics to quantify human preferences by asking participants to choose between a set of hypothetical options and outcomes. The behaviour observed in these games, combined with the use of mobile sensors, enables researchers to obtain unique insights into the effect of context on participant convictions

Eliciting empathy towards urban accessibility issues

Abstract Empathy is an integral part of what it means to be human. Empathy refers to the ability to sense other people’s emotions, coupled with the ability to imagine what they might be thinking and feeling. Architectural and urban design have identified empathy as a crucial factor in the design process and especially in user-centered participatory methods. Although empathy has been recognized as important for relating to other people’s issues, current research has not explored how urban accessibility issues elicit empathy. We conducted a between-subjects online study where 202 participants observed five scenarios on different accessibility issues. Our results show that empathic traits and previous experience are significant factors in empathizing with accessibility issues. Additionally, storytelling and photos can influence perceptions of accessibility issues. The study highlights the importance of empathic traits and personal experience in understanding and addressing accessibility issues, as well as the potential of storytelling and photos in shaping perceptions of accessibility issues and evoking empathy. Our contribution demonstrates the advantages of incorporating narrative multimedia into design processes for improved urban accessibility

“Nice to see you virtually”:thoughtful design and evaluation of virtual avatar of the other user in AR and VR based telexistence systems

Abstract This paper presents two studies investigating how physically remote telexistence users wish to see other users visualized as virtual avatars in a) augmented reality, and b) immersive virtual reality while conducting a collaborative task. To answer this research question, a telexistence system was designed and implemented with simple avatar designs. After that, visual examples of alternative avatar representations for both use cases were designed by thoughtfully altering the visual parameters of 36 virtual avatar examples. The avatar designs were first evaluated in a user study with 16 participants in conjunction with using an implemented telexistence system. As a follow-up an online survey with 43 respondents was used to record their preferences regarding virtual avatar appearance. The results suggest that users prefer the other user to be represented in a photorealistic full-body human avatar in both augmented reality and virtual reality due to its humanlike representation and affordances for interaction. In augmented reality, the choice for a hologram full body avatar was also popular due to its see-through appearance, which prevents a mix-up with a real person in the physical space

Mobile decision support and data provisioning for low back pain

AbstractThe authors present Back Pain Buddy, a mobile application offering decision support and coaching for people with low back pain (LBP). The application takes advantage of smartphones powerful capabilities and provides a crowd-sourced decision support system for discovering treatments and a mobile sensing solution for collecting data about users activities that are crucial in LBP research

Understanding smartphone notifications’ user interactions and content importance

Abstract We present the results of our experiment aimed to comprehensively understand the combination of 1) how smartphone users interact with their notifications, 2) what notification content is considered important, 3) the complex relationship between the interaction choices and content importance, and lastly 4) establish an intelligent method to predict user’s preference to seeing an incoming notification. We use a dataset of notifications received by 40 anonymous users in-the-wild, which consists of 1) qualitative user-labelled information about their preferences on notification’s contents, 2) notification source, and 3) the context in which the notification was received. We assess the effectiveness of personalised prediction models generated using a combination of self-reported content importance and contextual information. We uncover four distinct user types, based on the number of daily notifications and interaction choices. We showcase how usage traits of these groups highlight the requirement for notification filtering approaches, e.g., when specific users habitually neglect to manually filter out unimportant notifications. Our machine learning-based predictor, based on both contextual sensing and notification contents can predict the user’s preference for successfully acknowledging an incoming notification with 91.1% mean accuracy, crucial for time-critical user engagement and interventions