Exploring the effects of below-freezing temperatures on smartphone usage

While the use of smartphones in extreme temperatures does not necessarily occur every day nor in all parts of the world, numerous use cases can be highlighted where the use of smartphones in cold temperatures is mandatory. Modern smartphones are designed to function in a wide range of temperatures, but when exposed to extreme cold temperatures the performance and reliability can significantly suffer. This paper presents a controlled laboratory experiment, using a clinical cold chamber to expose seven smartphone models to both medium cold (0 degrees C to -20 degrees C) and extreme cold (-30 degrees C) environments. The results showcase the smartphones' sensing software's lack of awareness of the cold environment, as well as reliability issues in the form of device crashes across the whole range of tested devices. We present a strategy for implementing monitoring application designs to both appropriately sense the effect of cold environments, as well as predicting device shutdowns in extreme cold. (C) 2021 The Authors. Published by Elsevier B.V.Peer reviewe

Perceptions and Realities of Text-to-Image Generation

Generative artificial intelligence (AI) is a widely popular technology that will have a profound impact on society and individuals. Less than a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on many creative domains. In this paper, we present the findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their fears and concerns, and thoughts about risks and dangers of text-to-image generation to the individual and society. We find that while participants were aware of the risks and dangers associated with the technology, only few participants considered the technology to be a personal risk. The risks for others were more easy to recognize for participants. Artists were particularly seen at risk. Interestingly, participants who had tried the technology rated its future importance lower than those who had not tried it. This result shows that many people are still oblivious of the potential personal risks of generative artificial intelligence and the impending societal changes associated with this technology.Comment: ACM Academic Mindtrek 202

Eliciting Empathy towards Urban Accessibility Issues

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.</p

Text-to-Image Generation: Perceptions and Realities

Generative AI is an emerging technology that will have a profound impact on society and individuals. Only a decade ago, it was thought that creative work would be among the last to be automated - yet today, we see AI encroaching on creative domains. In this paper, we present the key findings of a survey study on people's perceptions of text-to-image generation. We touch on participants' technical understanding of the emerging technology, their ideas for potential application areas, as well as concerns, risks, and dangers of text-to-image generation to society and the individual. The study found that participants were aware of the risks and dangers associated with the technology, but only few participants considered the technology to be a risk to themselves. Additionally, those who had tried the technology rated its future importance lower than those who had not.Comment: Accepted at Generative AI in HCI workshop, CHI '2

When phones get personal : Predicting Big Five personality traits from application usage

As smartphones are increasingly an integral part of daily life, recent literature suggests a deeper relationship between personality traits and smartphone usage. However, this relationship depends on many complex factors such as geographic location, demographics, or cultural influence, just to name a few. These factors provide crucial knowledge for e.g. usage support, recommendations, marketing, general usage improvements. We use six months of application usage data from 739 Android smartphone user together with the IPIP 50-item Big Five personality traits questionnaire. As our main contribution, we show that even category-level aggregated application usage can predict Big Five traits at up to 86%-96% prediction fit in our sample. Our results show the effect of personality traits on application usage (mean error improvement on random guess 17.0%). We also identify which application usage data best describe the Big Five personality traits. Our work enables future personality-driven research, and shows that when studying personality, application categories can provide sufficient predictions in general traits. (C) 2020 The Authors. Published by Elsevier B.V.Peer reviewe

Smartphone based contextual symptom tracking and data gathering

Mobile devices are increasingly used for self-monitoring in areas of health, mood, and exercise tracking. The capabilities of modern devices also enable automation of this monitoring on a brand new scale. This thesis outlines the design, implementation, and evaluation via two-month long deployment of a system aimed to help both individual users and researchers to efficiently gather self-reported data. The name of the presented system is LifeTracker. LifeTracker is a two-tier system consisting of an Android application, used to gather data for personal or academic use, and a web dashboard, used to define parameters for studies. The main focus of the application is a novel input mechanism for self-reported data, leveraging notification popups as reactive input methods, that are presented to the user at opportune times. The thesis explains the design process of the system in detail in terms of use cases, requirements, and interface wireframes and the implementation process for both the web dashboard as well as the Android application. The Android application is evaluated in terms of usability and data gathering efficiency using interviews and the Standard Usability Survey. We also perform quantitative analysis of the machine learning classifiers used to predict user interruptibility. The results of the deployment show that users with prior experience with life logging applications appreciate the novel input mechanism and its strengths. Predicting user interruptibility can be performed at a reasonable rate, still considering personal variance of each individual.Mobiililaitteita käytetään suurenevissa määrin henkilökohtaisen terveyden, mielentilan ja fyysisten aktiviteettien seuraamiseen. Modernien laitteiden suorituskykykapasiteetti sallii myös tämänkaltaisen seurannan automatisoinnin. Tämä diplomityö hahmottaa suunnittelun, toteutuksen ja arvioinnin kahden kuukauden mittaisen käyttöönoton avulla järjestelmälle, jonka tarkoitus on auttaa yksittäisiä käyttäjiä ja tutkijoita keräämään tämänkaltaista seuraamistietoa. Työssä esitetyn järjestelmän nimi on LifeTracker. LifeTracker on kaksitasoinen järjestelmä, joka sisältää Android applikaation, jota käytetään tiedon keräämiseen henkilökohtaiseen ja tutkimuskäyttöön, ja Webkäyttöliittymän, jota käytetään tutkimusten parametrien määrittelemiseen. Applikaation fokus on uudenlaisessa syöttömekanismissa seurantatiedolle, joka käyttää ponnahdusikkunoita reaktiivisena syöttömekanismina, jotka esitetään käyttäjille sopivina aikoina. Työ selittää järjestelmän suunnittelun yksityiskohtaisesti käyttötapausten, vaatimusmäärittelyn ja käyttöliittymämallien avulla, ja toteutuksen sekä Web-käyttöliittymälle ja Android applikaatiolle. Android applikaatio arvioidaan käytettävyyden ja tiedonkeruun tehokkuuden suhteen käyttäen käyttäjähaastatteluja ja SUS-menetelmää. Suoritamme myös kvantitatiivisen analyysin koneoppimismalleista, joita käytetään käyttäjän keskeyttämisen ennakoimiseen. Käyttöönoton tulokset kertovat, että käyttäjät joilla on aikaisempaa kokemusta elämänhallintasovellusten kanssa ymmärtävät uudenlaisen syöttömekanismin edut ja vahvuudet. Käyttäjän keskeyttäminen voidaan myös ennakoida riittävällä tarkkuudella, ottaen huomioon muutokset yksittäisten käyttäjien välillä

Wear-IT:implications of mobile &amp; wearable technologies to human attention and interruptibility

Abstract This thesis explores different ways of leveraging mobile sensing to understand how end users use and interact with their smart technologies, namely smartphones and smartwatches. These topics are extensively explored in other parallel research; however, numerous gaps still exist within the literature. The use of mobile sensing to collect quantified ground-truth information of device use in-the-wild is critical to collect unbiased experiences and usage traces. This thesis covers three main themes: (a) the way our affect influences our smartphone use, and how our smartphone usage can also be analysed from our usage habits; (b) revealing quantified exploration of smartwatch usage traits, and how these relate to smartphone use, and (c) novel ways to mitigate interruptions during smartphone or smartwatch use. The thesis begins by explaining the related work and the overall theme of mobile sensing and how device usage influences attention; it then proceeds to elaborate on the contribution of each included article to the overall scope of the thesis. The thesis then concludes with a summary of how the presented articles tie together in a broader scope. Considering the vast amount of research in this field by this thesis’ author as well as other researchers, this type of work can potentially improve the use of novel wearable technologies in the future. By the end of the thesis, the reader should have a broad understanding of what mobile sensing is, and how it can be applied to comprehensively uncover technology use as well as leveraging mobile sensing to enhance the use of technology.Tiivistelmä Tässä väitöskirjassa tarkastellaan erilaisia tapoja hyödyntää mobiilikäytön tunnistamista ymmärtääkseen, miten loppukäyttäjät käyttävät ja ovat vuorovaikutuksessa älykkäiden teknologioidensa, esimerkiksi älypuhelimien ja älykellojen kanssa. Näitä aiheita tutkitaan laajasti muissa rinnakkaisissa tutkimuksissa, mutta kirjallisuudessa on vielä lukuisia aukkoja. Matkaviestinnän käytöstä kerätään kvantitatiiviset tiedot, jotka koskevat laitteen käyttöä luonnossa. Tämän tiedon kerääminen on kriittistä jotta voidaan kerätä puolueettomia kokemuksia ja käyttöjälkiä. Tässä työssä käsitellään kolmea pääteemaa; i) miten älypuhelinkäyttöömme vaikuttaa meidän mielialamme ja miten älypuhelinkäyttöämme voidaan analysoida käyttötapojen perusteella, ii) paljastaa älykellon käyttöominaisuuksien määrälliset tutkimukset ja miten nämä tulokset heijastuvat älypuhelimen käyttöön ja iii) uusia tapoja lieventää katkoksia älypuhelimen tai älykellon käytön aikana. Työ aloittaa selittämällä siihen liittyvää työtä ja mobiilin tunnistamisen yleistä teemaa ja sitä, miten laitteen käyttö vaikuttaa huomiokykyyn, ja jatkuu sitten yksityiskohtaisesti jokaisen mukana tulevan artikkelin osuuden yleiseen käsittelyyn. Työssä päädytään yhteenvetoon siitä, miten esitetyt artikkelit sitovat yhteen laajemman kokonaisuuden ja ottavat huomioon tämän alan tekijän ja muiden tutkijoiden tämän alan tutkimukset, ja miten tällaista työtä voitaisiin mahdollisesti parantaa edelleen tulevaisuudessa käyttämällä uusia tekniikoita. Työn päätyttyä lukijalla on laaja käsitys siitä, mitä mobiili-tunnistaminen on ja miten sitä voidaan soveltaa sekä teknologian käytön kattavaan paljastamiseen että mobiilidatan tunnistuksen hyödyntämiseen teknologian käytön tehostamiseksi

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

Proposing design recommendations for an intelligent recommender system logging stress

Abstract The connection between stress and smartphone usage behavior has been investigated extensively. While the prediction results using machine learning are encouraging, the challenge of how to cope with data loss remains. Addressing this problem, we propose an Intelligent Recommender System for logging stress based on adding a subjective user data-based validation to predictions made by intelligent algorithms. In a user study involving 731 daily stress self-reports from 30 participants we found discrepancies between subjective and smartphone usage data, i.e. battery, call information, or network usage. Despite the good prediction accuracy of 65% using a Random Forest classifier, combining both information would be beneficial for avoiding data and improving prediction accuracy. For realizing such a system (i.e., a mobile application), we propose three design recommendations, based on the capabilities of frequently used machine learning classifiers, enabling users to annotate their daily stress levels with a predict-and-validate methodology