The mapping of large-scale human movements is important for urban planning, traffic forecasting and epidemic prevention. Work in animals had suggested that their foraging might be explained in terms of a random walk, a mathematical rendition of a series of random steps, or a Levy flight, a random walk punctuated by occasional larger steps. The role of Levy statistics in animal behaviour is much debated — as explained in an accompanying News Feature — but the idea of extending it to human behaviour was boosted by a report in 2006 of Levy flight-like patterns in human movement tracked via dollar bills. A new human study, based on tracking the trajectory of 100,000 cell-phone users for six months, reveals behaviour close to a Levy pattern, but deviating from it as individual trajectories show a high degree of temporal and spatial regularity: work and other commitments mean we are not as free to roam as a foraging animal. But by correcting the data to accommodate individual variation, simple and predictable patterns in human travel begin to emerge. The cover photo (by Cesar Hidalgo) captures human mobility in New York's Grand Central Station. This study used a sample of 100,000 mobile phone users whose trajectory was tracked for six months to study human mobility patterns. Displacements across all users suggest behaviour close to the Levy-flight-like pattern observed previously based on the motion of marked dollar bills, but with a cutoff in the distribution. The origin of the Levy patterns observed in the aggregate data appears to be population heterogeneity and not Levy patterns at the level of the individual. Despite their importance for urban planning1, traffic forecasting2 and the spread of biological3,4,5 and mobile viruses6, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models7, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.

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Understanding individual human mobility patterns

We introduce a system for sensing complex social systems with data collected from 100 mobile phones over the course of 9 months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms.

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Reality mining: sensing complex social systems

Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors These sensors include GPS sensors, vision sensors (ie, cameras), audio sensors (ie, microphones), light sensors, temperature sensors, direction sensors (ie, magnetic compasses), and acceleration sensors (ie, accelerometers) The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals We then used the resulting training data to induce a predictive model for activity recognition This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (eg, sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise

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Activity recognition using cell phone accelerometers

Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.

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A survey of mobile phone sensing

Providing accurate and opportune information on people's activities and behaviors is one of the most important tasks in pervasive computing. Innumerable applications can be visualized, for instance, in medical, security, entertainment, and tactical scenarios. Despite human activity recognition (HAR) being an active field for more than a decade, there are still key aspects that, if addressed, would constitute a significant turn in the way people interact with mobile devices. This paper surveys the state of the art in HAR based on wearable sensors. A general architecture is first presented along with a description of the main components of any HAR system. We also propose a two-level taxonomy in accordance to the learning approach (either supervised or semi-supervised) and the response time (either offline or online). Then, the principal issues and challenges are discussed, as well as the main solutions to each one of them. Twenty eight systems are qualitatively evaluated in terms of recognition performance, energy consumption, obtrusiveness, and flexibility, among others. Finally, we present some open problems and ideas that, due to their high relevance, should be addressed in future research.

A Survey on Human Activity Recognition using Wearable Sensors

In addition to storing a plethora of sensitive personal and work information, smartphones also store sensor data about users and their daily activities. In order to understand users' behaviors and attitudes towards the security of their smartphone data, we conducted 28 qualitative interviews. We examined why users choose (or choose not) to employ locking mechanisms (e.g., PINs) and their perceptions and awareness about the sensitivity of the data stored on their devices. We performed two additional online experiments to quantify our interview results and the extent to which sensitive data could be found in a user's smartphone-accessible email archive. We observed a strong correlation between use of security features and risk perceptions, which indicates rational behavior. However, we also observed that most users likely underestimate the extent to which data stored on their smartphones pervades their identities, online and offline.

Are You Ready to Lock

In this article, we describe the design process of Reno, a location-enhanced, mobile coordination tool and person finder. The design process included three field experiments: a formative Experience Sampling Method (ESM) study, a pilot deployment and an extended user study. These studies were targeted at the significant personal security, privacy and data protection concerns caused by this application. We distil this experience into a small set of guidelines for designers of social mobile applications and show how these guidelines can be applied to a different application, called Boise. These guidelines cover issues pertaining to personal boundary definition, control, deception and denial, and group vs. individual communication. We also report on lessons learned from our evaluation experience, which might help practitioners in designing novel mobile applications, including the choice and characterization of users for testing security and privacy features of designs, the length of learning curves and their effect on evaluation and the impact of peculiar deployment circumstances on the results of these finely tuned user studies.

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Developing privacy guidelines for social location disclosure applications and services

We report on a two-week deployment of a peer-to-peer, mobile, location-enhanced messaging service. This study is specifically aimed at investigating the need for and effectiveness of automatic location disclosure mechanisms, the emerging strategies to achieve plausible deniability, and at understanding how place and activity are used to communicate plans, intentions and provide awareness. We outline the research that motivated this study, briefly describe the application we designed, and provide details of the evaluation process. The results show a lack of value of automatic messaging functions, confirm the need for supporting plausible deniability in communications, and highlight the prominent use of activity instead of place to indicate one's location. Finally, we offer suggestions for the development of social mobile applications.

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Control, deception, and communication: evaluating the deployment of a location-enhanced messaging service

Sleep is a basic physiological process essential for good health. However, 40 million people in the U.S. are diagnosed with sleep disorders, with many more undiagnosed. To help address this problem, we developed an application, ShutEye, which provides a peripheral display on the wall-paper of the user's mobile phone to promote awareness about recommended activities that promote good sleep quality. Based on preferences about the user's desired bed-time and activities' for example - consuming caffeine or performing vigorous exercise - ShutEye displays guidance about when engaging in those activities is likely to affect sleep without requiring any explicit interaction from the user. In this paper, we describe ShutEye and results from a four-week field study with 12 participants. Results indicate that a simple, recommendation-based peripheral display can be a very low-effort but still effective method for improving awareness of healthy sleep habits. We also provide recommendations about designing peripheral displays and extend insights for designing health-based mobile applications.

ShutEye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display

This paper explores end-user sensor installation for domestic ubiquitous computing applications and proposes five design principles to support this task. End-user sensor installation offers several advantages: it can reduce costs, enhance users' sense of control, accommodate diverse deployment environments, and increase users' acceptance of the technology. The five design principles are developed from the design and in situ evaluation of the sensor installation kit for the Home Energy Tutor, a domestic ubiquitous computing application. To generalize the design principles, factors affecting sensor installation are outlined, and the advantages of end-user sensor installation for three ubiquitous computing application domains are discussed.

Sunny Consolvo

Papers

Are You Ready to Lock

Developing privacy guidelines for social location disclosure applications and services

Control, deception, and communication: evaluating the deployment of a location-enhanced messaging service

ShutEye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display

Some Assembly Required: Supporting End-User Sensor Installation in Domestic Ubiquitous Computing Environments