Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities

There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectively used contextual information, such as mobility, communication and mobile phone usage patterns for quantifying individuals' mood and wellbeing. In this paper, we investigate the effectiveness of neural network models for predicting users' level of stress by using the location information collected by smartphones. We characterize the mobility patterns of individuals using the GPS metrics presented in the literature and employ these metrics as input to the network. We evaluate our approach on the open-source StudentLife dataset. Moreover, we discuss the challenges and trade-offs involved in building machine learning models for digital mental health and highlight potential future work in this direction.Comment: 6 pages, 2 figures, In Proceedings of the NIPS Workshop on Machine Learning for Healthcare 2017 (ML4H 2017). Colocated with NIPS 201

Designing and evaluating mobile multimedia user experiences in public urban places: Making sense of the field

The majority of the world’s population now lives in cities (United Nations, 2008) resulting in an urban densification requiring people to live in closer proximity and share urban infrastructure such as streets, public transport, and parks within cities. However, “physical closeness does not mean social closeness” (Wellman, 2001, p. 234). Whereas it is a common practice to greet and chat with people you cross paths with in smaller villages, urban life is mainly anonymous and does not automatically come with a sense of community per se. Wellman (2001, p. 228) defines community “as networks of interpersonal ties that provide sociability, support, information, a sense of belonging and social identity.” While on the move or during leisure time, urban dwellers use their interactive information communication technology (ICT) devices to connect to their spatially distributed community while in an anonymous space. Putnam (1995) argues that available technology privatises and individualises the leisure time of urban dwellers. Furthermore, ICT is sometimes used to build a “cocoon” while in public to avoid direct contact with collocated people (Mainwaring et al., 2005; Bassoli et al., 2007; Crawford, 2008). Instead of using ICT devices to seclude oneself from the surrounding urban environment and the collocated people within, such devices could also be utilised to engage urban dwellers more with the urban environment and the urban dwellers within. Urban sociologists found that “what attracts people most, it would appear, is other people” (Whyte, 1980, p. 19) and “people and human activity are the greatest object of attention and interest” (Gehl, 1987, p. 31). On the other hand, sociologist Erving Goffman describes the concept of civil inattention, acknowledging strangers’ presence while in public but not interacting with them (Goffman, 1966). With this in mind, it appears that there is a contradiction between how people are using ICT in urban public places and for what reasons and how people use public urban places and how they behave and react to other collocated people. On the other hand there is an opportunity to employ ICT to create and influence experiences of people collocated in public urban places. The widespread use of location aware mobile devices equipped with Internet access is creating networked localities, a digital layer of geo-coded information on top of the physical world (Gordon & de Souza e Silva, 2011). Foursquare.com is an example of a location based 118 Mobile Multimedia – User and Technology Perspectives social network (LBSN) that enables urban dwellers to virtually check-in into places at which they are physically present in an urban space. Users compete over ‘mayorships’ of places with Foursquare friends as well as strangers and can share recommendations about the space. The research field of Urban Informatics is interested in these kinds of digital urban multimedia augmentations and how such augmentations, mediated through technology, can create or influence the UX of public urban places. “Urban informatics is the study, design, and practice of urban experiences across different urban contexts that are created by new opportunities of real-time, ubiquitous technology and the augmentation that mediates the physical and digital layers of people networks and urban infrastructures” (Foth et al., 2011, p. 4). One possibility to augment the urban space is to enable citizens to digitally interact with spaces and urban dwellers collocated in the past, present, and future. “Adding digital layer to the existing physical and social layers could facilitate new forms of interaction that reshape urban life” (Kjeldskov & Paay, 2006, p. 60). This methodological chapter investigates how the design of UX through such digital placebased mobile multimedia augmentations can be guided and evaluated. First, we describe three different applications that aim to create and influence the urban UX through mobile mediated interactions. Based on a review of literature, we describe how our integrated framework for designing and evaluating urban informatics experiences has been constructed. We conclude the chapter with a reflective discussion on the proposed framework

Understanding face and eye visibility in front-facing cameras of smartphones used in the wild

Commodity mobile devices are now equipped with high-resolution front-facing cameras, allowing applications in biometrics (e.g., FaceID in the iPhone X), facial expression analysis, or gaze interaction. However, it is unknown how often users hold devices in a way that allows capturing their face or eyes, and how this impacts detection accuracy. We collected 25,726 in-the-wild photos, taken from the front-facing camera of smartphones as well as associated application usage logs. We found that the full face is visible about 29% of the time, and that in most cases the face is only partially visible. Furthermore, we identified an influence of users' current activity; for example, when watching videos, the eyes but not the entire face are visible 75% of the time in our dataset. We found that a state-of-the-art face detection algorithm performs poorly against photos taken from front-facing cameras. We discuss how these findings impact mobile applications that leverage face and eye detection, and derive practical implications to address state-of-the art's limitations

Feasibility Study of Ubiquitous Interaction Concepts

AbstractThere are all sorts of consumer electronics in a home environment. Using “apps” to interact with each device is neither feasible nor practical in an ubicomp future. Prototyping and evaluating interaction concepts for this future is a challenge. This paper proposes four concepts for device discovery and device interaction implemented in a virtual environment. The interaction concepts were compared in a controlled experiment for evaluation and comparison.Some statistically significant differences and subjective preferences could be observed in the quantitative and qualitative data respectively.Overall, the results indicate that the proposed interaction concepts were found natural and easy to use