Journal: Personal and Ubiquitous Computing

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Publisher

Springer

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ISSN

1617-4909
1617-4917
0949-2054

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Publications 1 - 10 of 26
  • Towards Long Term Monitoring of Electrodermal Activity in Daily Life
    Item type: Journal Article
    Kappeler-Setz, Cornelia; Gravenhorst, Franz; Schumm, Johannes; et al. (2013)
    Personal and Ubiquitous Computing
  • LuxTrace: indoor positioning using building illumination
    Item type: Journal Article
    Randall, Julian; Amft, Oliver; Bohn, Jürgen; et al. (2007)
    Personal and Ubiquitous Computing
  • A toolbox for managing organisational issues in the early stage of the development of a ubiquitous computing application
    Item type: Journal Article
    Boos, Daniel; Grote, Gudela; Guenter, Hannes (2013)
    Personal and Ubiquitous Computing
  • Functionality-power-packaging considerations in context aware wearable systems
    Item type: Conference Paper
    Bharatula, Nagendra B.; Lukowicz, Paul; Tröster, Gerhard (2008)
    Personal and Ubiquitous Computing
  • User experiences with simulated cyber-physical attacks on smart home IoT
    Item type: Journal Article
    Huijts, Nicole M.A.; Haans, Antal; Budimir, Sanja; et al. (2023)
    Personal and Ubiquitous Computing
    With the Internet of Things (IoT) becoming increasingly prevalent in people’s homes, new threats to residents are emerging such as the cyber-physical attack, i.e. a cyber-attack with physical consequences. In this study, we aimed to gain insights into how people experience and respond to cyber-physical attacks to their IoT devices. We conducted a naturalistic field experiment and provided 9 Dutch and 7 UK households, totalling 18 and 13 participants respectively, with a number of smart devices for use in their home. After a period of adaptation, simulated attacks were conducted, leading to events of varying noticeability (e.g., the light going on or off once or several times). After informing people simulated attacks had occurred, the attacks were repeated one more time. User experiences were collected through interviews and analysed with thematic analyses. Four relevant themes were identified, namely (1) the awareness of and concern about privacy and security risks was rather low, (2) the simulated attacks made little impression on the participants, (3) the participants had difficulties with correctly recognizing simulated attacks, and (4) when informed about simulated attacks taking place; participants noticed more simulated attacks and presented decision rules for them (but still were not able to identify and distinguish them well—see Theme 3). The findings emphasise the need for training interventions and an intrusion detection system to increase detection of cyber-physical attacks.
  • Mental health and the impact of ubiquitous technologies
    Item type: Other Journal Item
    Arnrich, Bert; Osmani, Venet; Bardram, Jakob. (2013)
    Personal and Ubiquitous Computing
  • Prototypical implementation of location-aware services based on a middleware architecture for super-distributed RFID tag infrastructures
    Item type: Conference Paper
    Bohn, Jürgen (2007)
    Personal and Ubiquitous Computing
  • Exercise repetition detection for resistance training based on smartphones
    Item type: Journal Article
    Pernek, Igor; Hummel, Karin Anna; Kokol, Peter (2013)
    Personal and Ubiquitous Computing
  • An update on privacy in ubiquitous computing
    Item type: Other Journal Item
    Spiekermann, Sarah; Langheinrich, Marc (2009)
    Personal and Ubiquitous Computing
  • Classifying watermelon ripeness by analysing acoustic signals using mobile devices
    Item type: Journal Article
    Zeng, Wei; Huang, Xianfeng; Müller Arisona, Stefan; et al. (2013)
    Personal and Ubiquitous Computing
    This work addresses the problem of distinguishing between ripe and unripe watermelons using mobile devices. Through analysing ripeness-related features extracted by thumping watermelons, collecting acoustic signals by microphones on mobile devices, our method can automatically identify the ripeness of watermelons. This is possible in real time, making use of machine learning techniques to provide good accuracy. We firstly collect a training dataset comprising acoustic signals generated by thumping both ripe and unripe watermelons. Audio signal analysis on this helps identify features related to watermelon ripeness. These features are then used to construct a classification model for future signals. Based on this, we developed a crowdsourcing application for Android which allows users to identify watermelon ripeness in real time while submitting their results to us allowing continuous improvement of the classification model. Experimental results show that our method is currently able to correctly classify ripe and unripe watermelons with an overall accuracy exceeding 89 %.
Publications 1 - 10 of 26