Monitoring patients’ pain is essential in clinical contexts, particularly post-surgery. However, collecting pain data is time-consuming and relies on manual data capture by clinical staff. Few studies assess the efficacy of pain-logging technology in a long-term, ecologically valid manner. We report on a 6-year deployment of a minimalist pain-logging device, the PainPad, in post-surgery orthopaedic wards in a large UK hospital. Our data show that patients (n > 500) tend to submit more pain scores that are at a higher rating compared to nurse-collected data. Interviews with the nursing staff using the device highlight the importance of understanding the complexities of the context of use when trying to integrate novel technology into clinical practice. Finally, we contribute reflections on the lessons learnt from running a significant long-term deployment of a prototype health technology in a naturalistic setting.

Loneliness is a growing public health concern, particularly among older adults, and has been linked to adverse physical and mental health outcomes. This study presents a machine learning approach to predict levels of loneliness using behavioural and emotional data collected from 124 participants through a mobile phone application over a 71-day period. The dataset includes 27 features derived from self-logged information such as wellbeing scores, mood fluctuations, and time spent in various home locations. Feature selection was applied to identify the most discriminative indicators, with classification and regression models evaluated using both Support Vector Machine (SVM), and Random Forest (RF). We applied feature selection to identify the most discriminative indicators and evaluated both Support Vector Machine (SVM) and Random Forest (RF) models for classification and regression. The highest classification accuracy—69.19% on a 7-point loneliness scale—was achieved using a five-fold SVM with the top 13 features. In the regression task, the best performance was observed using 26 features, resulting in a minimum Mean Squared Error (MSE) of 0.6752. These findings indicate that a selected subset of behavioural and emotional features can offer a meaningful estimation of loneliness levels. This has potential to inform the design of real-time, personalised digital tools aimed at identifying and supporting individuals at risk of loneliness.

Pain is an essential indicator of health and guides clinical treatments. Logging pain is important in supporting this. However, there is little research into pre-adolescent children's pain logging tools. Utilising the Bluebells method to engage children as co-designers, we gathered children's perspectives on pain-logging tools; in the first workshop by using tangible design approaches to support creative thinking, and in the second workshop by discussing developed prototypes based on the children's designs. Our findings highlight design concepts that the research team – despite many years of pain-related research – had not considered in the context of paediatric logging, namely a) prioritizing children's privacy in social settings while using pain-logging tools; b) emphasizing personalization to boost engagement; and c) logging general well-being of children alongside pain intensity to collect more insightful data. These findings thus demonstrate the value of co-designing pain-logging technologies with children.

This study evaluated the versatility of the MoJoXlab in conducting clinical movement analysis using inertial sensors from various manufacturers, including low-cost, non-proprietary, and open-protocol wearable options. Data were collected from 15 healthy participants who performed a range of clinically relevant activities and exercises using two sets of sensors. Dynamic time warping analysis of the sensor signals suggested that the collected dataset could be used for further algorithm development. The findings demonstrate that the current iteration of MoJoXlab can perform movement analysis using quaternions from sensors of any manufacturer. However, the accuracy of the resulting joint angles is not yet suitable for clinical applications across all sensor types, and only Xsens and NGIMU sensors are currently supported. This study also explored the potential for reducing the number of sensors required by MoJoXlab, which currently uses seven sensors to calculate joint angles for three joints (hip, knee and ankle) on both sides of the body. The creation of a comprehensive databank for lower limb movement analysis algorithms was an additional outcome of this work. Further research and development are necessary to expand MoJoXlab’s support for multiple sensor manufacturers and improve the accuracy of joint angle calculations for clinical applications.

In addressing the challenges of facial recognition in in the wild, our study initially investigated computationally efficient approaches for facial recognition in uncontrolled environments rather than conventional, computationally intensive techniques such as generative adversarial networks (GANs) and 3D reconstruction. We leveraged the capabilities of an off-the-shelf deep learning model, namely VGGNet, for efficiency and practical deployment in real-world scenarios. Our methodology included a dual phase training approach, starting with comprehensive training of the entire model, followed by selective fine-tuning of specific layers. This process was conducted using the CelebA dataset, known for its diversity and relevance to facial recognition research. The study demonstrates that this approach not only maintains robust generalisation across diverse conditions but also significantly reduces computational demands. Despite a slight trade-off in accuracy compared to more traditional methods, the benefits of the increased efficiency and the potential for real-time application deployment, such as in surveillance systems requiring quick processing, present a compelling case for further investigation and development within the field of facial recognition technology.

There are many design techniques to support the co-design of tangible technologies. However, few of these design methods allow the involvement of users at scale and across diverse geographic locations. While popular in psychology, the story completion method (SCM) has only recently started to be adopted within the HCI community. We explore whether SCM can generate meaningful design insights from large, diverse study populations for the design of Tangible User Interfaces (TUIs). Based on the results of two questionnaire studies using SCM, we conclude that the method can be used to generate meaningful design insights. Drawing on a systematic review of 870 TUI papers, we then contextualise the strengths and weaknesses of SCM against commonly used design methods, before reflecting on our experience of using the method across two distinct domains. We discuss the advantages of the method (particularly in terms of the scale and diversity of participation) and the challenges (particularly around constructing meaningful story stems, and developing the correct level of scaffolding to support creativity). We conclude that SCM is particularly suitable to be used in the early stages of the design process to understand the socio-cultural context of deployment.

Effective privacy protection in dynamic UbiComp environments requires users to be able to manage their privacy seamlessly across diverse contexts. To support this, designers need to go beyond GUI-based interactions and utilise tangible and embodied interactions. To help designers in such endeavours, we present the TTP toolkit: a card-based ideation kit to generate designs for tangible privacy management tools. The toolkit translates the Privacy Care framework for tangible-supported privacy management into a game intended to support designers in developing TUI privacy management tools. We demonstrate use of our toolkit through 10 online participatory workshops with 22 interaction designers. Our results demonstrate that the toolkit was effective in supporting the participants to creatively and collaboratively generate meaningful conceptual designs of tangible tools for privacy management.

Effective end-user privacy management in everyday ubiquitous computing environments requires giving users complex, contextual information about potential privacy breaches and enabling management of these breaches in a timely, engaging and intuitive manner. In this paper, we propose using empirically grounded image schema-based metaphors to help design these interactions. Results from our exploratory user study (N=22) demonstrate end users’ preferences for changes in physical attributes and spatial properties of objects for privacy awareness. For privacy control, end users prefer to exert force and create spatial movement. The study also explores user preferences for wearable vs. ambient form-factors for managing privacy and concludes that a hybrid solution would work for more users across more contexts. We thus provide a combination of form factor preferences, and a focused set of image schemas for designers to use when designing metaphor-based tangible privacy management tools.

The global population is ageing, leading to shifts in healthcare needs. It is well established that increased physical activity can improve the health and wellbeing of many older adults. However, motivation remains a prime concern. We report findings from a series of focus groups where we explored the concept of using community displays to promote physical activity to a local neighborhood. In doing so, we contribute both an understanding of the design space for community displays, as well as a discussion of the implications of our work for the broader CSCW community. We conclude that our work demonstrates the potential for developing community displays for increasing physical activity amongst older adults.

The emergence of ubiquitous computing (UbiComp) environments has increased the risk of undesired access to individuals’ physical space or their information, anytime and anywhere, raising potentially serious privacy concerns. Individuals lack awareness and control of the vulnerabilities in everyday contexts and need support and care in regulating disclosures to their physical and digital selves. Existing GUI-based solutions, however, often feel physically interruptive, socially disruptive, time-consuming and cumbersome. To address such challenges, we investigate the user interaction experience and discuss the need for more tangible and embodied interactions for effective and seamless natural privacy management in everyday UbiComp settings. We propose the Privacy Care interaction framework, which is rooted in the literature of privacy management and tangible computing. Keeping users at the center,AwarenessandControlare established as the core parts of our framework. This is supported with three interrelated interaction tenets:Direct, Ready-to-Hand,andContextual. Direct refers to intuitiveness through metaphor usage. Ready-to-Hand supports granularity, non-intrusiveness, and ad hoc management, through periphery-to-center style attention transitions. Contextual supports customization through modularity and configurability. Together, they aim to provide experience of an embodied privacy care with varied interactions that are calming and yet actively empowering. The framework provides designers of such care with a basis to refer to, to generate effective tangible tools for privacy management in everyday settings. Through five semi-structured focus groups, we explore the privacy challenges faced by a sample set of 15 older adults (aged 60+) across their cyber-physical-social spaces. The results show conformity to our framework, demonstrating the relevance of the facets of the framework to the design of privacy management tools in everyday UbiComp contexts.

Animals can be negatively affected by wearable tracking devices, even those marketed as ?animal friendly' and increasingly used with companion animals, such as cats. To understand the wearer experience of cats fitted with popular GPS trackers, we measured the behavior of 13 feline participants while they were wearing the devices during a field study. The aim of our behavioral analysis was twofold: investigating potential signs of discomfort generated by the devices to evaluate the impact that such interventions have on cat wearers; identifying wearability flaws that might account for the observed impact and wearability requirements to improve the design of the devices. Based on our findings, we propose a set of requirements that should inform the design of trackers to afford better wearability and thus provide better wearer experience for cat wearers.

The global population is ageing, leading to shifts in healthcare needs. Home healthcare monitoring systems currently focus on physical health, but there is an increasing recognition that psychological wellbeing also needs support. This raises the question of how to design devices that older adults can interact with to log their feelings. We designed three tangible prototypes, based on existing paper-based scales of affect. We report findings from a lab study in which participants used the prototypes to log the emotion from standardised emotional vignettes. We found that the prototypes allowed participants to accurately record identified emotions in a reasonable time. Our participants expressed a perceived need to record emotions, either to share with family/carers or for self-reflection. We conclude that our work demonstrates the potential for in-home tangible devices for recording the emotions of older adults to support wellbeing.

In this paper we present an approach to designing wearer-centered biotelemetry for non-human (and human) animal wearers. Drawing from fundamental values and principles of user-centered design, we describe a wearer-centered framework to heuristically establish design requirements, which was used during a series of workshops to perform a requirements analysis for a cat-tracking device. The resulting requirements informed a feline-centered prototype whose wearability was evaluated with cat wearers. Compared to the wearability of previously tested off-the-shelf devices, our findings show an improvement and suggest that our framework-based approach can help design teams with a range of skills to systematically design for wearability.

The world is facing an ageing population phenomenon, coupled with health and social problems, which affect older people’s ability to live independently. This situation challenges the viability of health and social services. Smart home technology can play a significant role in easing the pressure on caregivers, as well as reduce the financial costs of health and social services. Activity of Daily Living (ADL) recognition is an essential step to translate sensor data into activities at high semantic levels. Supervised Machine Learning (ML) algorithms are the most commonly used techniques for this application. However, a common problem is a lack of availability of enough annotated data to train these algorithms. Collecting annotated data is expensive, time consuming, and may violate people’s privacy. Intra- and inter-personal variation in performing complex activities is another challenge for an ML-based activity recognition approach. In this paper, a multi-layered knowledge-based architecture for recognising ADL in real-time is proposed. At the first stage, sensor data is pre-processed; events that describe changes in the environment are detected at the second stage, in which the sequence of events is used to recognise more semantically complex activities at the third stage. A new ADL ontology is proposed to model the knowledge related to the sensor platform and the targeted activities as the previously proposed ontologies were either designed to deal with specific sensor data, or they ignored the context environment information which is important in recognising complex activities.

For those with chronic conditions, such as Type 1 diabetes, smartphone apps offer the promise of an affordable, convenient, and personalized disease management tool. However, despite significant academic research and commercial development in this area, diabetes apps still show low adoption rates and underwhelming clinical outcomes. Through user-interaction sessions with 16 people with Type 1 diabetes, we provide evidence that commonly used interfaces for diabetes self-management apps, while providing certain benefits, can fail to explicitly address the cognitive and emotional requirements of users. From analysis of these sessions with eight such user interface designs, we report on user requirements, as well as interface benefits, limitations, and then discuss the implications of these findings. Finally, with the goal of improving these apps, we identify 3 questions for designers, and review for each in turn: current shortcomings, relevant approaches, exposed challenges, and potential solutions.

Type 1 diabetes is a potentially life-threatening chronic condition that requires frequent interactions with diverse data to inform treatment decisions. While mobile technologies such as blood glucose meters have long been an essential part of this process, designing interfaces that explicitly support decision-making remains challenging. Dual-process models are a common approach to understanding such cognitive tasks. However, evidence from the first of two studies we present suggests that in demanding and complex situations, some individuals approach disease management in distinctive ways that do not seem to fit well within existing models. This finding motivated, and helped frame our second study, a survey (n=192) to investigate these behaviors in more detail. On the basis of the resulting analysis, we posit Fluid Contextual Reasoning to explain how some people with diabetes respond to particular situations, and discuss how an extended framework might help inform the design of user interfaces for diabetes management.

Monitoring patients' pain is a critical issue for clinical caregivers, particularly among staff responsible for providing analgesic relief. However, collecting regularly scheduled pain readings from patients can be difficult and time-consuming for clinicians. In this paper we present Painpad, a tangible device that was developed to allow patients to engage in self-logging of their pain. We report findings from two hospital-based field studies in which Painpad was deployed to a total of 78 inpatients recovering from ambulatory surgery. We find that Painpad results in improved frequency and compliance with pain logging, and that self-logged scores may be more faithful to patients' experienced pain than corresponding scores reported to nurses. We also show that older adults may prefer tangible interfaces over tablet-based alternatives for reporting their pain, and we contribute design lessons for pain logging devices intended for use in hospital settings.

Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks. This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook.

Electric vehicles are an increasingly attractive option for households to reduce carbon emissions, especially when they are powered by renewable energy. In this paper we report the results of an 18-month field trial investigating the desirability and feasibility of powering electric vehicles (EVs) with domestic solar electricity. Based on extensive collection of data from 7 households including over 75,000 miles of daily EV use, home electricity consumption and generation, and in-depth interviews with householders we develop a detailed understanding of what drives EV decisions in households, quantify to what extent our participating households currently power their EVs with solar electricity, and investigate how feasible the vision of "self-sustaining electric mobility" is. We use this understanding to draw implications for future research into supporting emerging practices of EV drivers.

Domestic microgeneration is the onsite generation of low- and zero-carbon heat and electricity by private households to meet their own needs. In this paper we explore how an everyday household routine -- that of doing laundry -- can be augmented by digital technologies to help households with photovoltaic solar energy generation to make better use of self-generated energy. This paper presents an 8-month in-the-wild study that involved 18 UK households in longitudinal energy data collection, prototype deployment and participatory data analysis. Through a series of technology interventions mixing energy feedback, proactive suggestions and direct control the study uncovered opportunities, potential rewards and barriers for families to shift energy consuming household activities and highlights how digital technology can act as mediator between household laundry routines and energy demand-shifting behaviors. Finally, the study provides insights into how a "smart" energy-aware washing machine shapes organization of domestic life and how people "communicate" with their washing machine.

As mobile computing applications have become commonplace, it is increasingly important for them to address end-users’ privacy requirements. Privacy requirements depend on a number of contextual socio-cultural factors to which mobility adds another level of contextual variation. However, traditional requirements elicitation methods do not sufficiently account for contextual factors and therefore cannot be used effectively to represent and analyse the privacy requirements of mobile end users. On the other hand, methods that do investigate contextual factors tend to produce data that does not lend itself to the process of requirements extraction. To address this problem we have developed a Privacy Requirements Distillation approach that employs a problem analysis framework to extract and refine privacy requirements for mobile applications from raw data gathered through empirical studies involving end users. Our approach introduces privacy facets that capture patterns of privacy concerns which are matched against the raw data. We demonstrate and evaluate our approach using qualitative data from an empirical study of a mobile social networking application.

Privacy requirements for mobile applications offer a distinct set of challenges for requirements engineering. First, they are highly dynamic, changing over time and locations, and across the different roles of agents involved and the kinds of information that may be disclosed. Second, although some general privacy requirements can be elicited a priori, users often refine them at runtime as they interact with the system and its environment. Selectively disclosing information to appropriate agents is therefore a key privacy management challenge, requiring carefully formulated privacy requirements amenable to systematic reasoning. In this paper, we introduce privacy arguments as a means of analysing privacy requirements in general and selective disclosure requirements (that are both content- and context-sensitive) in particular. Privacy arguments allow individual users to express personal preferences, which are then used to reason about privacy for each user under different contexts. At runtime, these arguments provide a way to reason about requirements satisfaction and diagnosis. Our proposed approach is demonstrated and evaluated using the privacy requirements of BuddyTracker, a mobile application we developed as part of our overall research programme.

A growing body of research has been exploring the use of control mechanisms to address the privacy concerns raised by location-tracking technology. We report on a qualitative study of two family groups who used a custom-built tracking application for an extended period of time. Akin to sociological breaching experiments, the study focuses on the interferences between location tracking and relationship management. We analyze the tensions that can arise between affordances of the technology and uses that the contracts between family members legitimize. We describe how, by fostering misperceptions and 'nudging' behaviors, location-tracking technology can generate anxieties and conflicts even in close relationships. We discuss their vulnerability to the overreaching effects of tracking, against which the use of mechanisms such as location-sharing preferences and feedback may not be socially viable.

How can we best explore the range of users' reactions when developing future technologies that may be controversial, such as personal healthcare systems? Our approach -- ContraVision -- uses futuristic videos, or other narrative forms, that convey either negative or positive aspects of the proposed technology for the same scenarios. We conducted a user study to investigate what range of responses the different versions elicited. Our findings show that the use of two systematically comparable representations of the same technology can elicit a wider spectrum of reactions than a single representation can. We discuss why this is so and the value of obtaining breadth in user feedback for potentially controversial technologies.

We describe the "Privacy-Shake", a novel interface for managing coarse grained privacy settings. We built a prototype that enables users of Buddy Tracker, an example location sharing application, to change their privacy preferences by shaking their phone. Users can enable or disable location sharing and change the level of granularity of disclosed location by shaking and sweeping their phone. In this poster we present and motivate our work on Privacy-Shake and report on a lab-based evaluation of the interface with 16 participants.

Effective privacy management requires that mobile systems' users be able to make informed privacy decisions as their experience and knowledge of a system progresses. Prior work has shown that making such privacy decisions is a difficult task for users because systems do not provide support for awareness, visibility and accountability when sharing privacy-sensitive information. This paper reports results of our investigation into the efficacy of real-time feedback as a mechanism for incorporating these features of social translucence in location-sharing applications, in order to help users make better privacy decisions. We explored the role of real-time feedback in the context of Buddy Tracker, a mobile location-sharing application. Our work focuses on ways in which real-time feedback affects people's behaviour in order to identify the main criteria for acceptance of this technology. Based on the data from a three week field trial of Buddy Tracker, a focus group session, and interviews, we found that when using a system that provided real-time feedback, people were more accountable for their actions and reduced the number of unreasonable location requests. We have used the results of our study to propose high-level design criteria for incorporating real-time feedback into information sharing applications in a manner that ensures social acceptance of the technology.

Mobile privacy concerns are central to Ubicomp and yet remain poorly understood. We advocate a diversified approach, enabling the cross-interpretation of data from complementary methods. However, mobility imposes a number of limitations on the methods that can be effectively employed. We discuss how we addressed this problem in an empirical study of mobile social networking. We report on how, by combining a variation of experience sampling and contextual interviews, we have started focusing on a notion of context in relation to privacy, which is subjectively defined by emerging socio-cultural knowledge, functions, relations and rules. With reference to Gieryn's sociological work, we call this place, as opposed to a notion of context that is objectively defined by physical and factual elements, which we call space. We propose that the former better describes the context for mobile privacy.

No abstract available.

This paper considers how facilities afforded by electronic assignment handling can contribute to the quality of Internet-based teaching of programming. It reports a study comparing the nature, form, and quality of feedback provided by instructors on 90 paper and electronic assignments in an introductory Computing course and notes effective strategies for electronic marking.

article Enhancing teaching using the Internet: report of the working group on the World Wide Web as an interactive teaching resource Share on Authors: Stephen Hartley Drexel University Drexel UniversityView Profile , Jill Gerhardt-Powals Richard Stockton College Richard Stockton CollegeView Profile , David Jones Central Queensland University, Australia Central Queensland University, AustraliaView Profile , Colin McCormack University College Cork, Ireland University College Cork, IrelandView Profile , M. Dee Medley Augusta College Augusta CollegeView Profile , Blaine Price Open University, UK Open University, UKView Profile , Margaret Reek Rochester Institute of Technology Rochester Institute of TechnologyView Profile , Marguerite K. Summers University of Illinois at Springfield University of Illinois at SpringfieldView Profile Authors Info & Claims ACM SIGCUE OutlookVolume 24Issue 1-3Jan.-July, 1996 pp 218–228https://doi.org/10.1145/1013718.237649Published:01 January 1996 8citation901DownloadsMetricsTotal Citations8Total Downloads901Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The Open University, which teaches around 150,000 students at a distance, is examining the adaptation of its distance teaching methods for the Internet, and its Computing Department has undertaken a sequence of trials to investigate both the technical and educational issues involved.Two smallscale trials were run in 1995, and this paper will describe their successor trials being run in 1996: 300 students on an entry-Ievel Computing course, and 50 students on an upper-level course.This paper introduces the OU'S distance teaching structure; describes the trials investigating the effect of electronic communication on students, tutors, counselors, and administrators;and discusses the infrastructure and the electronic tutorials that have been developed.The paper concludes with a list of issues which arise.