Breaking the habit: measuring and predicting departures from routine in individual human mobility

Researchers studying daily life mobility patterns have recently shown that humans are typically highly predictable in their movements. However, no existing work has examined the boundaries of this predictability, where human behaviour transitions temporarily from routine patterns to highly unpredictable states. To address this shortcoming, we tackle two interrelated challenges. First, we develop a novel information-theoretic metric, called instantaneous entropy, to analyse an individual’s mobility patterns and identify temporary departures from routine. Second, to predict such departures in the future, we propose the first Bayesian framework that explicitly models breaks from routine, showing that it outperforms current state-of-the-art predictor

Human Mobility and Application Usage Prediction Algorithms for Mobile Devices

Mobile devices such as smartphones and smart watches are ubiquitous companions of humans’ daily life. Since 2014, there are more mobile devices on Earth than humans. Mobile applications utilize sensors and actuators of these devices to support individuals in their daily life. In particular, 24% of the Android applications leverage users’ mobility data. For instance, this data allows applications to understand which places an individual typically visits. This allows providing her with transportation information, location-based advertisements, or to enable smart home heating systems. These and similar scenarios require the possibility to access the Internet from everywhere and at any time. To realize these scenarios 83% of the applications available in the Android Play Store require the Internet to operate properly and therefore access it from everywhere and at any time. Mobile applications such as Google Now or Apple Siri utilize human mobility data to anticipate where a user will go next or which information she is likely to access en route to her destination. However, predicting human mobility is a challenging task. Existing mobility prediction solutions are typically optimized a priori for a particular application scenario and mobility prediction task. There is no approach that allows for automatically composing a mobility prediction solution depending on the underlying prediction task and other parameters. This approach is required to allow mobile devices to support a plethora of mobile applications running on them, while each of the applications support its users by leveraging mobility predictions in a distinct application scenario. Mobile applications rely strongly on the availability of the Internet to work properly. However, mobile cellular network providers are struggling to provide necessary cellular resources. Mobile applications generate a monthly average mobile traffic volume that ranged between 1 GB in Asia and 3.7 GB in North America in 2015. The Ericsson Mobility Report Q1 2016 predicts that by the end of 2021 this mobile traffic volume will experience a 12-fold increase. The consequences are higher costs for both providers and consumers and a reduced quality of service due to congested mobile cellular networks. Several countermeasures can be applied to cope with these problems. For instance, mobile applications apply caching strategies to prefetch application content by predicting which applications will be used next. However, existing solutions suffer from two major shortcomings. They either (1) do not incorporate traffic volume information into their prefetching decisions and thus generate a substantial amount of cellular traffic or (2) require a modification of mobile application code. In this thesis, we present novel human mobility and application usage prediction algorithms for mobile devices. These two major contributions address the aforementioned problems of (1) selecting a human mobility prediction model and (2) prefetching of mobile application content to reduce cellular traffic. First, we address the selection of human mobility prediction models. We report on an extensive analysis of the influence of temporal, spatial, and phone context data on the performance of mobility prediction algorithms. Building upon our analysis results, we present (1) SELECTOR – a novel algorithm for selecting individual human mobility prediction models and (2) MAJOR – an ensemble learning approach for human mobility prediction. Furthermore, we introduce population mobility models and demonstrate their practical applicability. In particular, we analyze techniques that focus on detection of wrong human mobility predictions. Among these techniques, an ensemble learning algorithm, called LOTUS, is designed and evaluated. Second, we present EBC – a novel algorithm for prefetching mobile application content. EBC’s goal is to reduce cellular traffic consumption to improve application content freshness. With respect to existing solutions, EBC presents novel techniques (1) to incorporate different strategies for prefetching mobile applications depending on the available network type and (2) to incorporate application traffic volume predictions into the prefetching decisions. EBC also achieves a reduction in application launch time to the cost of a negligible increase in energy consumption. Developing human mobility and application usage prediction algorithms requires access to human mobility and application usage data. To this end, we leverage in this thesis three publicly available data set. Furthermore, we address the shortcomings of these data sets, namely, (1) the lack of ground-truth mobility data and (2) the lack of human mobility data at short-term events like conferences. We contribute with JK2013 and UbiComp Data Collection Campaign (UbiDCC) two human mobility data sets that address these shortcomings. We also develop and make publicly available a mobile application called LOCATOR, which was used to collect our data sets. In summary, the contributions of this thesis provide a step further towards supporting mobile applications and their users. With SELECTOR, we contribute an algorithm that allows optimizing the quality of human mobility predictions by appropriately selecting parameters. To reduce the cellular traffic footprint of mobile applications, we contribute with EBC a novel approach for prefetching of mobile application content by leveraging application usage predictions. Furthermore, we provide insights about how and to what extent wrong and uncertain human mobility predictions can be detected. Lastly, with our mobile application LOCATOR and two human mobility data sets, we contribute practical tools for researchers in the human mobility prediction domain

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Understanding the Relationship Between People and Their Environments Using Smartphone Data: A Study of Personality, Places Visited, and Emotional Experiences

Much has been theorized about the relationship between people and their environments. Certain people may be more inclined to visit certain types of places (e.g., campus, pub) and display different patterns of mobility as they move among them (e.g., number of places visited, distances traveled). Moreover, even the same place may affect people differently, depending on their psychological characteristics (e.g., personality). In this dissertation, I draw upon recent technological advances in smartphone-sensing methods to investigate the relationship between people’s psychological characteristics and their physical movements through space. I begin by reviewing the existing psychological literature. I next describe features that can be extracted from GPS data and categorize them to provide a framework for collecting, analyzing, and discussing mobility. Then, I conduct an empirical investigation demonstrating this methodology at work. One-hundred and eighteen participants provided ecological momentary assessments, reporting their places visited and emotional states (e.g., feeling stressed, relaxed, sad) four times per day for two to four weeks. In addition to these ecological momentary assessments, place and mobility data were also automatically collected for forty students using their smartphone’s GPS sensors. I supplemented these data by collecting place attributions from an independent sample of 267 participants who evaluated the situational characteristics (e.g., sociality, positivity) of the most commonly visited locations. Lastly, I look at how people perceive places and whether their judgments about a location (e.g., predictions about the personality of those most likely to visit a location) demonstrate consensus or accuracy. A lens model analysis highlights the cues underlying these perceptions. The results show how places visited (based on self-reported places) and mobility patterns (based on sensed GPS data) are related to people’s in-the-moment emotional experiences and their enduring psychological characteristics, such as their personality and wellbeing. I also examine how one’s personality interacts with the situational characteristics of a place to affect emotional states. For instance, one key finding reveals that, in general, participants experienced more positive emotions in social places (e.g., common rooms, pubs) but that this was especially true for more extraverted individuals. Lastly, I find that though people demonstrate consensus in their judgments when virtually visiting a place, they do not show significant accuracy. My discussion focuses on the benefits of using place and GPS-based mobility measures to understand the relationship between people and their environments, as well as the unique methodological and logistical challenges inherent to this. I conclude by discussing potential implications for privacy and research ethics and point to promising directions for future research

Psychological research in the digital age

The smartphone has become an important personal companion in our daily lives. Each time we use the device, we generate data that provides information about ourselves. This data, in turn, is valuable to science because it objectively reflects our everyday behavior and experiences. In this way, smartphones enable research that is closer to everyday life than traditional laboratory experiments and questionnaire-based methods. While data collected with smartphones are increasingly being used in the field of personality psychology, new digital technologies can also be leveraged to collect and analyze large-scale unobtrusively sensed data in other areas of psychological research. This dissertation, therefore, explores the insights that smartphone sensing reveals for psychological research using two examples, situation and affect research, making a twofold research contribution. First, in two empirical studies, different data types of smartphone-sensed data, such as GPS or phone data, were combined with experience-sampled self-report, and classical questionnaire data to gain valuable insights into individual behavior, thinking, and feeling in everyday life. Second, predictive modeling techniques were applied to analyze the large, high-dimensional data sets collected by smartphones. To gain a deeper understanding of the smartphone data, interpretable variables were extracted from the raw sensing data, and the predictive performance of various machine learning algorithms was compared. In summary, the empirical findings suggest that smartphone data can effectively capture certain situational and behavioral indicators of psychological phenomena in everyday life. However, in certain research areas such as affect research, smartphone data should only complement, but not completely replace, traditional questionnaire-based data as well as other data sources such as neurophysiological indicators. The dissertation also concludes that the use of smartphone sensor data introduces new difficulties and challenges for psychological research and that traditional methods and perspectives are reaching their limits. The complexity of data collection, processing, and analysis requires established guidelines for study design, interdisciplinary collaboration, and theory-driven research that integrates explanatory and predictive approaches. Accordingly, further research is needed on how machine learning models and other big data methods in psychology can be reconciled with traditional theoretical approaches. Only in this way can we move closer to the ultimate goal of psychology to better understand, explain, and predict human behavior and experiences and their interplay with everyday situations

Affective value in the predictive mind

Although affective value is fundamental in explanations of behavior, it is still a somewhat alien concept in cognitive science. It implies a normativity or directionality that mere information processing models cannot seem to provide. In this paper we trace how affective value can emerge from information processing in the brain, as described by predictive processing. We explain the grounding of predictive processing in homeostasis, and articulate the implications this has for the concept of reward and motivation. However, at first sight, this new conceptualization creates a strong tension with conventional ideas on reward and affective experience. We propose this tension can be resolved by realizing that valence, a core component of all emotions, might be the reflection of a specific aspect of predictive information processing, namely the dynamics in prediction errors across time and the expectations we, in turn, form about these dynamics. Specifically, positive affect seems to be caused by positive rates of prediction error reduction, while negative affect is induced by a shift in a state with lower prediction errors to one with higher prediction errors (i.e., a negative rate of error reduction). We also consider how intense emotional episodes might be related to unexpected changes in prediction errors, suggesting that we also build (meta)predictions on error reduction rates. Hence in this account emotions appear as the continuous non-conceptual feedback on evolving —increasing or decreasing—uncertainties relative to our predictions. The upshot of this view is that the various emotions, from “basic” ones to the non-typical ones such as humor, curiosity and aesthetic affects, can be shown to follow a single underlying logic. Our analysis takes several cues from existing emotion theories but deviates from them in revealing ways. The account on offer does not just specify the interactions between emotion and cognition, rather it entails a deep integration of the two

Human mobility and social ties in context: from places to personality

Recent years saw an increasing proliferation of the use of digitally generated traces of data for understanding human behaviour. The quantitative understanding of social networks as well as patterns of human mobility benefited tremendously from these new sources of data. The main dynamics of both social networks and human mobility such as a propensity of humans for heterogeneous behaviour, how humans choose to explore new places, or the fact that both spheres are intrinsically linked are now fairly well understood. However, how various other factors mediate the observed dynamics is still relatively unknown, not least due to the difficulty in obtaining adequate data. Thus, for my thesis I focus on how a variety of factors---places, longer-term dynamics, the personality of individuals, or neighbourhoods---might be a driver of various aspects of social and mobility behaviour. I used data from the Copenhagen network study that tracked 847 students with smartphones and measured their social encounters as well as the locations they visited for a whole academic year. I further utilised a variety of methods for analysing the data ranging from applied machine learning over inferential statistics to social network analysis. Using this dataset, I found that the qualities of places were very informative for understanding future encounters between students, that the longer-term dynamics shaped both social and mobility behaviour, and that while personality had a significant effect on the observed regularity of behaviour, its effect was rather small

Shoulder pain in manual wheelchair users: towards a multi-disciplinary solution for a multi-faceted problem

It is estimated that there are over 2 million manual wheelchair users in the United States. Up to 70% of manual wheelchair users report upper limb pain, which is mainly manifested in the shoulder and wrist. Shoulder pain in wheelchair users is linked to difficulty performing activities of daily living, decreased physical activity and decreased quality of life. The main focus of this dissertation is to identify biomarkers from wheelchair propulsion data that are potentially related to shoulder pain in manual wheelchair users. Three biomarkers that distinguish between manual wheelchair users with and without shoulder pain are identified. The acceptability of the identified biomarkers are subjected to hypothesis testing using data collected from a sample of 30 experienced adult manual wheelchair users with and without shoulder pain. The results and their implications will be discussed. In this dissertation we will also discuss the interpretation and the physical significance of each of the results, a summary of limitations for the approaches adopted, and suggestions on the future course of research to address these limitations. While the past two decades of research on shoulder pain and wheelchair propulsion has led to the development of important clinical guidelines, it has failed to identify specific biomarkers that may be related to shoulder pain in manual wheelchair users. This could be in part due to employing a binary approach by focusing on just (1) the pure bio-mechanical aspects, and (2) wheelchair design aspects (ergonomics). The originality of this dissertation is in the adoption of a multidisciplinary approach. Methodologies integrating theories and analyses from fields related to human movement science such as human motor control theory, non-linear dynamics and human factors (occupational ergonomics) are adopted to identify potential biomarkers that relate to shoulder pain in manual wheelchair users. This dissertation concludes with preliminary results from a prototype wearable device, custom developed for manual wheelchair users. Wheelchair propulsion data obtained from the device will be benchmarked with data from the currently available technologies for tracking manual wheelchair propulsion (SMARTWheel and motion capture). This dissertation also proposes a framework for incorporating the research findings into the custom developed wearable technology for home-based rehabilitation training purposes