Obtrusiveness of smartphone applications for sleep health

Unobtrusiveness is one of the main issues concerning health-related systems. Many developers affirm that their systems do not burden users; however, this is not always achieved. This article evaluates the obtrusiveness of various systems developed to improve sleep quality. The systems analyzed are related to sleep hygiene, since it has become an interesting topic for researchers, physicians and people in general, mainly because it has become part of the methods used to estimate a persons’ health status A set of design elements are presented as keys to achieving unobtrusiveness. We propose a scale to measure the level of unobtrusiveness and use it to evaluate several systems, with a focus on smartphone applications.

Developing Unobtrusive Mobile Interactions: a Model Driven Engineering approach

In Ubiquitous computing environments, people are surrounded by a lot of embedded services. With the inclusion of pervasive technologies such as sensors or GPS receivers, mobile devices turn into an effective communication tool between users and the services embedded in their environment. All these services compete for the attentional resources of the user. Thus, it is essential to consider the degree in which each service intrudes the user mind when services are designed. In order to prevent service behavior from becoming overwhelming, this work, based on Model Driven Engineering foundations, is devoted to develop services according to user needs. In this thesis, we provide a systematic method for the development of mobile services that can be adapted in terms of obtrusiveness. That is, services can be developed to provide their functionality at different obtrusiveness levels by minimizing the duplication of efforts. For the system specification, a modeling language is defined to cope with the particular requirements of the context-aware user interface domain. From this specification, following a sequence of well-defined steps, a software solution is obtained.Gil Pascual, M. (2010). Developing Unobtrusive Mobile Interactions: a Model Driven Engineering approach. http://hdl.handle.net/10251/12745Archivo delegad

Protocol for PD SENSORS:Parkinson’s Disease Symptom Evaluation in a Naturalistic Setting producing Outcomes measuRes using SPHERE technology. An observational feasibility study of multi-modal multi-sensor technology to measure symptoms and activities of daily living in Parkinson’s disease

Introduction The impact of disease-modifying agents on disease progression in Parkinson’s disease is largely assessed in clinical trials using clinical rating scales. These scales have drawbacks in terms of their ability to capture the fluctuating nature of symptoms while living in a naturalistic environment. The SPHERE (Sensor Platform for HEalthcare in a Residential Environment) project has designed a multi-sensor platform with multimodal devices designed to allow continuous, relatively inexpensive, unobtrusive sensing of motor, non-motor and activities of daily living metrics in a home or a home-like environment. The aim of this study is to evaluate how the SPHERE technology can measure aspects of Parkinson’s disease.Methods and analysis This is a small-scale feasibility and acceptability study during which 12 pairs of participants (comprising a person with Parkinson’s and a healthy control participant) will stay and live freely for 5 days in a home-like environment embedded with SPHERE technology including environmental, appliance monitoring, wrist-worn accelerometry and camera sensors. These data will be collected alongside clinical rating scales, participant diary entries and expert clinician annotations of colour video images. Machine learning will be used to look for a signal to discriminate between Parkinson’s disease and control, and between Parkinson’s disease symptoms ‘on’ and ‘off’ medications. Additional outcome measures including bradykinesia, activity level, sleep parameters and some activities of daily living will be explored. Acceptability of the technology will be evaluated qualitatively using semi-structured interviews.Ethics and dissemination Ethical approval has been given to commence this study; the results will be disseminated as widely as appropriate

Analysis and design of individual information systems to support health behavior change

As a wide-ranging socio-technical transformation, the digitalization has significantly influenced the world, bringing opportunities and challenges to our lives. Despite numerous benefits like the possibility to stay connected with people around the world, the increasing dispersion and use of digital technologies and media (DTM) pose risks to individuals’ well-being and health. Rising demands emerging from the digital world have been linked to digital stress, that is, stress directly or indirectly resulting from DTM (Ayyagari et al. 2011; Ragu-Nathan et al. 2008; Tarafdar et al. 2019; Weil and Rosen 1997), potentially intensifying individuals’ overall exposure to stress. Individuals experiencing this adverse consequence of digitalization are at elevated risk of developing severe mental health impairments (Alhassan et al. 2018; Haidt and Allen 2020; Scott et al. 2017), which is why various scholars emphasize that research should place a stronger focus on analyzing and shaping the role of the individual in a digital world, pursuing instrumental as well as humanistic objectives (Ameen et al. 2021; Baskerville 2011b). Information Systems (IS) research has long placed emphasis on the use of information and communication technology (ICT) in organizations, viewing an information system as the socio-technical system that emerges from individuals’ interaction with DTM in organizations. However, socio-technical information systems, as the essence of the IS discipline (Lee 2004; Sarker et al. 2019), are also present in different social contexts from private life. Acknowledging the increasing private use of DTM, such as smartphones and social networks, IS scholars have recently intensified their efforts to understand the human factor of IS (Avison and Fitzgerald 1991; Turel et al. 2021). A framework recently proposed by Matt et al. (2019) suggests three research angles: analyzing individuals’ behavior associated with their DTM use, analyzing what consequences arise from their DTM use behavior, and designing new technologies that promote positive or mitigate negative effects of individuals’ DTM use. Various recent studies suggest that individuals’ behavior seems to be an important lever influencing the outcomes of their DTM use (Salo et al. 2017; Salo et al. 2020; Weinstein et al. 2016). Therefore, this dissertation aims to contribute to IS research targeting the facilitation of a healthy DTM use behavior. It explores the use behavior, consequences, and design of DTM for individuals' use with the objective to deliver humanistic value by increasing individuals' health through supporting a behavior change related to their DTM use. The dissertation combines behavioral science and design science perspectives and applies pluralistic methodological approaches from qualitative (e.g., interviews, prototyping) and quantitative research (e.g., survey research, field studies), including mixed-methods approaches mixing both. Following the framework from Matt et al. (2019), the dissertation takes three perspectives therein: analyzing individuals’ behavior, analyzing individuals’ responses to consequences of DTM use, and designing information systems assisting DTM users. First, the dissertation presents new descriptive knowledge on individuals’ behavior related to their use of DTM. Specifically, it investigates how individuals behave when interacting with DTM, why they behave the way they do, and how their behavior can be influenced. Today, a variety of digital workplace technologies offer employees different ways of pursuing their goals or performing their tasks (Köffer 2015). As a result, individuals exhibit different behaviors when interacting with these technologies. The dissertation analyzes what interactional roles DTM users can take at the digital workplace and what may influence their behavior. It uses a mixed-methods approach and combines a quantitative study building on trace data from a popular digital workplace suite and qualitative interviews with users of this digital workplace suite. The empirical analysis yields eight user roles that advance the understanding of users’ behavior at the digital workplace and first insights into what factors may influence this behavior. A second study adds another perspective and investigates how habitual behavior can be changed by means of DTM design elements. Real-time feedback has been discussed as a promising way to do so (Schibuola et al. 2016; Weinmann et al. 2016). In a field experiment, employees working at the digital workplace are provided with an external display that presents real-time feedback on their office’s indoor environmental quality. The experiment examines if and to what extent the feedback influences their ventilation behavior to understand the effect of feedback as a means of influencing individuals’ behavior. The results suggest that real-time feedback can effectively alter individuals’ behavior, yet the feedback’s effectiveness reduces over time, possibly as a result of habituation to the feedback. Second, the dissertation presents new descriptive and prescriptive knowledge on individuals’ ways to mitigate adverse consequences arising from the digitalization of individuals. A frequently discussed consequence that digitalization has on individuals is digital stress. Although research efforts strive to determine what measures individuals can take to effectively cope with digital stress (Salo et al. 2017; Salo et al. 2020; Weinert 2018), further understanding of individuals’ coping behavior is needed (Weinert 2018). A group at high risk of suffering from the adverse effects of digital stress is adolescents because they grow up using DTM daily and are still developing their identity, acquiring mental strength, and adopting essential social skills. To facilitate a healthy DTM use, the dissertation explores what strategies adolescents use to cope with the demands of their DTM use. Combining a qualitative and a quantitative study, it presents 30 coping responses used by adolescents, develops five factors underlying adolescents’ activation of coping responses, and identifies gender- and age-related differences in their coping behavior. Third, the dissertation presents new prescriptive knowledge on the design of individual information systems supporting individuals in understanding and mitigating their perceived stress. Facilitated by the sensing capabilities of modern mobile devices, it explores the design and development of mobile systems that assess stress and support individuals in coping with stress by initiating a change of stress-related behavior. Since there is currently limited understanding of how to develop such systems, this dissertation explores various facets of their design and development. As a first step, it presents the development of a prototype aiming for life-integrated stress assessment, that is, the mobile sensor-based assessment of an individual’s stress without interfering with their daily routines. Data collected with the prototype yields a stress model relating sensor data to individuals’ perception of stress. To deliver a more generalized perspective on mobile stress assessment, the dissertation further presents a literature- and experience-based design theory comprising a design blueprint, design requirements, design principles, design features, and a discussion of potentially required trade-offs. Mobile stress assessment may be used for the development of mobile coping assistants. Aiming to assist individuals in effectively coping with stress and preventing future stress, a mobile coping assistant should recommend adequate coping strategies to the stressed individual in real-time or execute targeted actions within a defined scope of action automatically. While the implementation of a mobile coping assistant is yet up to future research, the dissertation presents an abstract design and algorithm for selecting appropriate coping strategies. To sum up, this dissertation contributes new knowledge on the digitalization of individuals to the IS knowledge bases, expanding both descriptive and prescriptive knowledge. Through the combination of diverse methodological approaches, it delivers knowledge on individuals’ behavior when using DTM, on the mitigation of consequences that may arise from individuals’ use of DTM, and on the design of individual information systems with the goal of facilitating a behavior change, specifically, regarding individuals’ coping with stress. Overall, the research contained in this dissertation may promote the development of digital assistants that support individuals’ in adopting a healthy DTM use behavior and thereby contribute to shaping a socio-technical environment that creates more benefit than harm for all individuals

Ubiquitous Integration and Temporal Synchronisation (UbilTS) framework : a solution for building complex multimodal data capture and interactive systems

Contemporary Data Capture and Interactive Systems (DCIS) systems are tied in with various technical complexities such as multimodal data types, diverse hardware and software components, time synchronisation issues and distributed deployment configurations. Building these systems is inherently difficult and requires addressing of these complexities before the intended and purposeful functionalities can be attained. The technical issues are often common and similar among diverse applications. This thesis presents the Ubiquitous Integration and Temporal Synchronisation (UbiITS) framework, a generic solution to address the technical complexities in building DCISs. The proposed solution is an abstract software framework that can be extended and customised to any application requirements. UbiITS includes all fundamental software components, techniques, system level layer abstractions and reference architecture as a collection to enable the systematic construction of complex DCISs. This work details four case studies to showcase the versatility and extensibility of UbiITS framework’s functionalities and demonstrate how it was employed to successfully solve a range of technical requirements. In each case UbiITS operated as the core element of each application. Additionally, these case studies are novel systems by themselves in each of their domains. Longstanding technical issues such as flexibly integrating and interoperating multimodal tools, precise time synchronisation, etc., were resolved in each application by employing UbiITS. The framework enabled establishing a functional system infrastructure in these cases, essentially opening up new lines of research in each discipline where these research approaches would not have been possible without the infrastructure provided by the framework. The thesis further presents a sample implementation of the framework on a device firmware exhibiting its capability to be directly implemented on a hardware platform. Summary metrics are also produced to establish the complexity, reusability, extendibility, implementation and maintainability characteristics of the framework.Engineering and Physical Sciences Research Council (EPSRC) grants - EP/F02553X/1, 114433 and 11394

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Gesture-Controlled Interaction with Aesthetic Information Sonification

Information representation in augmented and virtual reality systems, and social physical (building) spaces can enhance the efficacy of interacting with and assimilating abstract, non-visual data. Sanification is the process of automatically generated real time information representation. There is a gap in our implementation and knowledge of auditory display systems used to enhance interaction in virtual and augmented reality. This paper addresses that gap by examining methodologies for mapping socio-spatial data to spatialised sanification manipulated with gestural controllers. This is a system of interactive knowledge representation that completes the human integration loop, enabling the user to interact with and manipulate data using 3D spatial gesture and 3D auditory display. Benefits include 1) added immersion in an augmented or virtual reality interface; 2) auditory display avoids visual overload in visually-saturated processes such as designing, evacuation in emergencies, flying aircraft; computer gaming; and 3) bi-modal or auditory representation, due to its time-based character, facilitates cognition of complex information