"Are we in this together?": embedding social identity detection in drones improves emergency coordination.

Autonomous systems, such as drones, are critical for emergency mitigation, management, and recovery. They provide situational awareness and deliver communication services which effectively guide emergency responders' decision making. This combination of technology and people comprises a socio-technical system. Yet, focusing on the use of drone technology as a solely operational tool, underplays its potential to enhance coordination between the different agents involved in mass emergencies, both human and non-human. This paper proposes a new methodological approach that capitalizes on social identity principles to enable this coordination in an evacuation operation. In the proposed approach, an adaptive drone uses sensor data to infer the group membership of the survivors it encounters during the operation. A corpus of 200 interactions of survivors' talk during real-life emergencies was computationally classified as being indicative of a shared identity or personal/no identity. This classification model, then, informed a game-theoretic model of human-robot interactions. Bayesian Nash Equilibrium analysis determined the predicted behavior for the human agent and the strategy that the drone needs to adopt to help with survivor evacuation. Using linguistic and synthetic data, we show that the identity-adaptive architecture outperformed two non-adaptive architectures in the number of successful evacuations. The identity-adaptive drone can infer which victims are likely to be helped by survivors and where help from emergency teams is needed. This facilitates effective coordination and adaptive performance. This study shows decision-making can be an emergent capacity that arises from the interactions of both human and non-human agents in a socio-technical system.

Digital Intervention in Loneliness in Older Adults: Qualitative Analysis of User Studies.

BACKGROUND: Loneliness is a significant well-being issue that affects older adults. Existing, commonly used social connection platforms do not contain facilities to break the cognitive cycle of loneliness, and loneliness interventions implemented without due processes could have detrimental effects on well-being. There is also a lack of digital technology designed with older adults. OBJECTIVE: We aimed to iteratively design a user-centered smartphone app that can address loneliness in older adults. The aim of this study was to investigate the loneliness-related psychological processes that our conceptual smartphone app promotes. We also identified the emergent needs and concerns that older adults raised regarding the potential benefits and detriments of the app. METHODS: We used technology probes to elicit older adults' reflections on the concept of using the app in 2 studies as follows: concept focus groups (n=33) and concept interviews (n=10). We then conducted a prototype trial with 1 week of use and follow-up interviews (n=12). RESULTS: Thematic analysis explored the experiences and emergent challenges of our app through the design process. This led to the development of 4 themes as follows occurring in all 3 qualitative data sets: reflection on a digital social map is reassuring; app features encourage socializing; the risk of compounding loneliness; and individuals feel more control with mutual, socially beneficial activities. CONCLUSIONS: Smartphone apps have the potential to increase older adults' awareness of the richness of their social connections, which may support loneliness reduction. Our qualitative approach to app design enabled the inclusion of older adults' experiences in technology design. Thus, we conclude that the older adults in our study most desired functionalities that can support mutual activities and maintain or find new connections rather than enable them to share an emotional state. They were wary of the app replacing their preferred in-person social interaction. Participants also raised concerns about making the user aware of the lack of support in their social network and wanted specific means of addressing their needs. Further user-centered design work could identify how the app can support mutual activities and socializing.

Significant Features for Human Activity Recognition Using Tri-Axial Accelerometers.

Activity recognition using wearable sensors has become essential for a variety of applications. Tri-axial accelerometers are the most widely used sensor for activity recognition. Although various features have been used to capture patterns and classify the accelerometer signals to recognise activities, there is no consensus on the best features to choose. Reducing the number of features can reduce the computational cost and complexity and enhance the performance of the classifiers. This paper identifies the signal features that have significant discriminative power between different human activities. It also investigates the effect of sensor placement location, the sampling frequency, and activity complexity on the selected features. A comprehensive list of 193 signal features has been extracted from accelerometer signals of four publicly available datasets, including features that have never been used before for activity recognition. Feature significance was measured using the Joint Mutual Information Maximisation (JMIM) method. Common significant features among all the datasets were identified. The results show that the sensor placement location does not significantly affect recognition performance, nor does it affect the significant sub-set of features. The results also showed that with high sampling frequency, features related to signal repeatability and regularity show high discriminative power.

Designing Tangibles to Support Emotion Logging for Older Adults: Development and Usability Study.

BACKGROUND: The global population is aging, leading to shifts in health care needs. In addition to developing technology to support physical health, there is an increasing recognition of the need to consider how technology can support emotional health. This raises the question of how to design devices that older adults can interact with to log their emotions. OBJECTIVE: We designed and developed 2 novel tangible devices, inspired by existing paper-based scales of emotions. The findings from a field trial of these devices with older adults are reported. METHODS: Using interviews, field deployment, and fixed logging tasks, we assessed the developed devices. RESULTS: Our results demonstrate that the tangible devices provided data comparable with standardized psychological scales of emotion. The participants developed their own patterns of use around the devices, and their experience of using the devices uncovered a variety of design considerations. We discuss the difficulty of customizing devices for specific user needs while logging data comparable to psychological scales of emotion. We also highlight the value of reflecting on sparse emotional data. CONCLUSIONS: Our work demonstrates the potential for tangible emotional logging devices. It also supports further research on whether such devices can support the emotional health of older adults by encouraging reflection of their emotional state.

Loneliness in older people and COVID-19: Applying the social identity approach to digital intervention design.

The COVID-19 pandemic is worsening loneliness for many older people through the challenges it poses in engaging with their social worlds. Digital technology has been offered as a potential aid, however, many popular digital tools have not been designed to address the needs of older adults during times of limited contact. We propose that the Social Identity Model of Identity Change (SIMIC) could be a foundation for digital loneliness interventions. While SIMIC is a well-established approach for maintaining wellbeing during life transitions, it has not been rigorously applied to digital interventions. There are known challenges to integrating psychological theory in the design of digital technology to enable efficacy, technology acceptance, and continued use. The interdisciplinary field of Human Computer Interaction has a history of drawing on models originating from psychology to improve the design of digital technology and to design technologies in an appropriate manner. Drawing on key lessons from this literature, we consolidate research and design guidelines for multidisciplinary research applying psychological theory such as SIMIC to digital social interventions for loneliness.

Public opinion of the Irish "COVID Tracker" digital contact tracing App: A national survey.

Objective: This study aims to gather public opinion on the Irish "COVID Tracker" digital contact tracing (DCT) App, with particular focus on App usage, usability, usefulness, technological issues encountered, and potential changes to the App. Methods: A 35-item online questionnaire was deployed for 10 days in October 2020, 3 months after the launch of the Irish DCT App. Results: A total of 2889 completed responses were recorded, with 2553 (88%) respondents currently using the App. Although four in five users felt the App is easy to download, is easy to use and looks professional, 615 users (22%) felt it had slowed down their phone, and 757 (28%) felt it had a negative effect on battery life. Seventy-nine percent of respondents reported the App's main function is to aid contact tracing. Inclusion of national COVID-19 trends is a useful ancillary function according to 87% of respondents, and there was an appetite for more granular local data. Overall, 1265 (44%) respondents believed the App is helping the national effort, while 1089 (38%) were unsure. Conclusions: DCT Apps may potentially augment traditional contact tracing methods. Despite some reports of negative effects on phone performance, just 7% of users who have tried the App have deleted it. Ancillary functionality, such as up-to-date regional COVID-19, may encourage DCT App use. This study describes general positivity toward the Irish COVID Tracker App among users but also highlights the need for transparency on effectiveness of App-enabled contact tracing and for study of non-users to better establish barriers to use.

Sentiment analysis of user feedback on the HSE's Covid-19 contact tracing app.

BACKGROUND: Digital Contact Tracing is seen as a key tool in reducing the propagation of Covid-19. But it requires high uptake and continued participation across the population to be effective. To achieve sufficient uptake/participation, health authorities should address, and thus be aware of, user concerns. AIM: This work manually analyzes user reviews of the Irish Heath Service Executive's (HSE) Contact Tracker app, to identify user concerns and to lay the foundations for subsequent, large-scale, automated analyses of reviews. While this might seem tightly scoped to the Irish context, the HSE app provides the basis for apps in many jurisdictions in the USA and Europe. METHODS: Manual analysis of (1287) user reviews from the Google/Apple playstores was performed, to identify the aspects of the app that users focused on, and the positive/negative sentiment expressed. RESULTS: The findings suggest a largely positive sentiment towards the app, and that users thought it handled data protection and transparency aspects well. But feedback suggests that users would appreciate more targeted feedback on the incidence of the virus, and facilities for more proactive engagement, like notifications that prompt users to submit their health status daily. Finally, the analysis suggests that the "android battery" issue and the backward-compatibility issue with iPhones seriously impacted retention/uptake of the app respectively. CONCLUSION: The HSE have responded to the public's desire for targeted feedback in newer versions, but should consider increasing the app's proactive engagement. The results suggest they should also raise the backward compatibility issue, regarding older iPhones, with Apple.

Digital Detectives: Websleuthing Reduces Eyewitness Identification Accuracy in Police Lineups.

Eyewitnesses to crimes sometimes search for a culprit on social media before viewing a police lineup, but it is not known whether this affects subsequent lineup identification accuracy. The present online study was conducted to address this. Two hundred and eighty-five participants viewed a mock crime video, and after a 15-20 min delay either (i) viewed a mock social media site including the culprit, (ii) viewed a mock social media site including a lookalike, or (iii) completed a filler task. A week later, participants made an identification from a photo lineup. It was predicted that searching for a culprit on social media containing the lookalike (rather than the culprit) would reduce lineup identification accuracy. There was a significant association between social media exposure and lineup accuracy for the Target Present lineup (30% more of the participants who saw the lookalike on social media failed to positively identify the culprit than participants in the other conditions), but for the Target Absent lineup (which also included the lookalike) there was no significant association with lineup identification accuracy. The results suggest that if an eyewitness sees a lookalike (where they are expecting to see the culprit) when conducting a self-directed search on social media, they are less likely to subsequently identify the culprit in the formal ID procedure.

Best Practice Guidance for Digital Contact Tracing Apps: A Cross-disciplinary Review of the Literature.

BACKGROUND: Digital contact tracing apps have the potential to augment contact tracing systems and disrupt COVID-19 transmission by rapidly identifying secondary cases prior to the onset of infectiousness and linking them into a system of quarantine, testing, and health care worker case management. The international experience of digital contact tracing apps during the COVID-19 pandemic demonstrates how challenging their design and deployment are. OBJECTIVE: This study aims to derive and summarize best practice guidance for the design of the ideal digital contact tracing app. METHODS: A collaborative cross-disciplinary approach was used to derive best practice guidance for designing the ideal digital contact tracing app. A search of the indexed and gray literature was conducted to identify articles describing or evaluating digital contact tracing apps. MEDLINE was searched using a combination of free-text terms and Medical Subject Headings search terms. Gray literature sources searched were the World Health Organization Institutional Repository for Information Sharing, the European Centre for Disease Prevention and Control publications library, and Google, including the websites of many health protection authorities. Articles that were acceptable for inclusion in this evidence synthesis were peer-reviewed publications, cohort studies, randomized trials, modeling studies, technical reports, white papers, and media reports related to digital contact tracing. RESULTS: Ethical, user experience, privacy and data protection, technical, clinical and societal, and evaluation considerations were identified from the literature. The ideal digital contact tracing app should be voluntary and should be equitably available and accessible. User engagement could be enhanced by small financial incentives, enabling users to tailor aspects of the app to their particular needs and integrating digital contact tracing apps into the wider public health information campaign. Adherence to the principles of good data protection and privacy by design is important to convince target populations to download and use digital contact tracing apps. Bluetooth Low Energy is recommended for a digital contact tracing app's contact event detection, but combining it with ultrasound technology may improve a digital contact tracing app's accuracy. A decentralized privacy-preserving protocol should be followed to enable digital contact tracing app users to exchange and record temporary contact numbers during contact events. The ideal digital contact tracing app should define and risk-stratify contact events according to proximity, duration of contact, and the infectiousness of the case at the time of contact. Evaluating digital contact tracing apps requires data to quantify app downloads, use among COVID-19 cases, successful contact alert generation, contact alert receivers, contact alert receivers that adhere to quarantine and testing recommendations, and the number of contact alert receivers who subsequently are tested positive for COVID-19. The outcomes of digital contact tracing apps' evaluations should be openly reported to allow for the wider public to review the evaluation of the app. CONCLUSIONS: In conclusion, key considerations and best practice guidance for the design of the ideal digital contact tracing app were derived from the literature.

The Case for Animal Privacy in the Design of Technologically Supported Environments.

Privacy is an essential consideration when designing interactive systems for humans. However, at a time when interactive technologies are increasingly targeted at non-human animals and deployed within multispecies contexts, the question arises as to whether we should extend privacy considerations to other animals. To address this question, we revisited early scholarly work on privacy, which examines privacy dynamics in non-human animals (henceforth "animals"). Then, we analysed animal behaviour literature describing privacy-related behaviours in different species. We found that animals use a variety of separation and information management mechanisms, whose function is to secure their own and their assets' safety, as well as negotiate social interactions. In light of our findings, we question tacit assumptions and ordinary practises that involve human technology and that affect animal privacy. Finally, we draw implications for the design of interactive systems informed by animals' privacy requirements and, more broadly, for the development of privacy-aware multispecies interaction design.

A national survey of attitudes to COVID-19 digital contact tracing in the Republic of Ireland.

BACKGROUND: Contact tracing remains a critical part of controlling COVID-19 spread. Many countries have developed novel software applications (Apps) in an effort to augment traditional contact tracing methods. AIM: Conduct a national survey of the Irish population to examine barriers and levers to the use of a contact tracing App. METHODS: Adult participants were invited to respond via an online survey weblink sent via e-mail and messaging Apps and posted on our university website and on popular social media platforms, prior to launch of the national App solution. RESULTS: A total of 8088 responses were received, with all 26 counties of the Republic of Ireland represented. Fifty-four percent of respondents said they would definitely download a contact-tracing App, while 30% said they would probably download a contact tracing App. Ninety-five percent of respondents identified at least one reason for them to download such an App, with the most common reasons being the potential for the App to help family members and friends and a sense of responsibility to the wider community. Fifty-nine percent identified at least one reason not to download the App, with the most common reasons being fear that technology companies or the government might use the App technology for greater surveillance after the pandemic. CONCLUSION: The Irish citizens surveyed expressed high levels of willingness to download a public health-backed App to augment contact tracing. Concerns raised regarding privacy and data security will be critical if the App is to achieve the large-scale adoption and ongoing use required for its effective operation.

Assessing the Privacy of mHealth Apps for Self-Tracking: Heuristic Evaluation Approach.

BACKGROUND: The recent proliferation of self-tracking technologies has allowed individuals to generate significant quantities of data about their lifestyle. These data can be used to support health interventions and monitor outcomes. However, these data are often stored and processed by vendors who have commercial motivations, and thus, they may not be treated with the sensitivity with which other medical data are treated. As sensors and apps that enable self-tracking continue to become more sophisticated, the privacy implications become more severe in turn. However, methods for systematically identifying privacy issues in such apps are currently lacking. OBJECTIVE: The objective of our study was to understand how current mass-market apps perform with respect to privacy. We did this by introducing a set of heuristics for evaluating privacy characteristics of self-tracking services. METHODS: Using our heuristics, we conducted an analysis of 64 popular self-tracking services to determine the extent to which the services satisfy various dimensions of privacy. We then used descriptive statistics and statistical models to explore whether any particular categories of an app perform better than others in terms of privacy. RESULTS: We found that the majority of services examined failed to provide users with full access to their own data, did not acquire sufficient consent for the use of the data, or inadequately extended controls over disclosures to third parties. Furthermore, the type of app, in terms of the category of data collected, was not a useful predictor of its privacy. However, we found that apps that collected health-related data (eg, exercise and weight) performed worse for privacy than those designed for other types of self-tracking. CONCLUSIONS: Our study draws attention to the poor performance of current self-tracking technologies in terms of privacy, motivating the need for standards that can ensure that future self-tracking apps are stronger with respect to upholding users' privacy. Our heuristic evaluation method supports the retrospective evaluation of privacy in self-tracking apps and can be used as a prescriptive framework to achieve privacy-by-design in future apps.

A hybrid model for automatic emotion recognition in suicide notes.

We describe the Open University team's submission to the 2011 i2b2/VA/Cincinnati Medical Natural Language Processing Challenge, Track 2 Shared Task for sentiment analysis in suicide notes. This Shared Task focused on the development of automatic systems that identify, at the sentence level, affective text of 15 specific emotions from suicide notes. We propose a hybrid model that incorporates a number of natural language processing techniques, including lexicon-based keyword spotting, CRF-based emotion cue identification, and machine learning-based emotion classification. The results generated by different techniques are integrated using different vote-based merging strategies. The automated system performed well against the manually-annotated gold standard, and achieved encouraging results with a micro-averaged F-measure score of 61.39% in textual emotion recognition, which was ranked 1st place out of 24 participant teams in this challenge. The results demonstrate that effective emotion recognition by an automated system is possible when a large annotated corpus is available.