Unobtrusive stress detection on the basis of smartphone usage data

Peer reviewe

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

myStress: Unobtrusive Smartphone-Based Stress Detection

Life is becoming increasingly stressful in many aspects, e.g., due to technology-induced stress and stress in organizational context. The assessment of stress experienced by individuals enables stress management and prevention with the long-term aim to avoid psychological and physiological harm from excessive stress. Commonly this assessment is performed through questionnaires on perceived stress or physiological measurements evaluating body reactions to stress. We explore a third assessment method: Our design science approach aims to unobtrusively assess perceived stress based on smartphone data while waiving additional devices and explicit user input. The presented design artefact, myStress, reads 36 hardware and software sensors to infer users’ perceived stress levels. A prototypical instantiation of myStress for the Android platform is distributed to test users. For evaluation purposes, the stress level additionally is determined by a questionnaire consisting of the Perceived Stress Scale. By analyzing data from test users, we gain first insights into the feasibility of unobtrusive, continuous stress assessment considering exclusively data from smartphone sensors. We find that several sensors seem to correlate with perceived stress, e.g., the frequency of switching the display on/off. For future research, behavioral and situational prevention measures can build on this method of unobtrusive stress assessment

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

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

Emotions in context: examining pervasive affective sensing systems, applications, and analyses

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; “sensing”, “analysis”, and “application”. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing

From the Inside Out: A Literature Review on Possibilities of Mobile Emotion Measurement and Recognition

Information systems are becoming increasingly intelligent and emotion artificial intelligence is an important component for the future. Therefore, the measurement and recognition of emotions is necessary and crucial. This paper presents a state of the art in the research field of mobile emotion measurement and recognition. The aim of this structured literature analysis using the PRISMA statement is to collect and classify the relevant literature and to provide an overview of the current status of mobile emotion recording and its future trends. A total of 59 articles were identified in the relevant literature databases, which can be divided into four main categories of emotion measurement. There was an increase of publications over the years in all four categories, but with a particularly strong increase in the areas of optical and vital-data-based recording. Over time, both the speed as well as the accuracy of the measurement has improved considerably in all four categories

Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone

The number of individuals with mental disorders is increasing and they are commonly found among individuals who avoid social interaction and like to live alone. Amongst such mental health disorders is depression which is both common and serious. The present paper introduces a method to assess the depression level of an individual using a smartphone by monitoring their daily activities. The time domain characteristics from a smartphone acceleration sensor were used alongside a vector machine algorithm to classify physical activities. Additionally, the geographical location information was clustered using a smartphone GPS sensor to simplify movement patterns. A total of 12 features were extracted from individuals’ physical activity and movement patterns and were analyzed alongside their weekly depression scores using the nine-item Patient Health Questionnaire. Using a wrapper feature selection method, a subset of features was selected and applied to a linear regression model to estimate the depression score. The support vector machine algorithm was then used to classify the depression severity level among individuals (absence, moderate, severe) and had an accuracy of 87.2% in severe depression cases which outperformed other classification models including the k-nearest neighbor and artificial neural network. This method of identifying depression is a cost-effective solution for long-term use and can monitor individuals for depression without invading their personal space or creating other day-to-day disturbances

Opportunistic and Context-aware Affect Sensing on Smartphones: The Concept, Challenges and Opportunities

Opportunistic affect sensing offers unprecedented potential for capturing spontaneous affect ubiquitously, obviating biases inherent in the laboratory setting. Facial expression and voice are two major affective displays, however most affect sensing systems on smartphone avoid them due to extensive power requirement. Encouragingly, due to the recent advent of low-power DSP (Digital Signal Processing) co-processor and GPU (Graphics Processing Unit) technology, audio and video sensing are becoming more feasible. To properly evaluate opportunistically captured facial expression and voice, contextual information about the dynamic audio-visual stimuli needs to be inferred. This paper discusses recent advances of affect sensing on the smartphone and identifies the key barriers and potential solutions of implementing opportunistic and context-aware affect sensing on smartphone platforms