Regularity and Predictability of Human Mobility in Personal Space

Fundamental laws governing human mobility have many important applications such as forecasting and controlling epidemics or optimizing transportation systems. These mobility patterns, studied in the context of out of home activity during travel or social interactions with observations recorded from cell phone use or diffusion of money, suggest that in extra-personal space humans follow a high degree of temporal and spatial regularity – most often in the form of time-independent universal scaling laws. Here we show that mobility patterns of older individuals in their home also show a high degree of predictability and regularity, although in a different way than has been reported for out-of-home mobility. Studying a data set of almost 15 million observations from 19 adults spanning up to 5 years of unobtrusive longitudinal home activity monitoring, we find that in-home mobility is not well represented by a universal scaling law, but that significant structure (predictability and regularity) is uncovered when explicitly accounting for contextual data in a model of in-home mobility. These results suggest that human mobility in personal space is highly stereotyped, and that monitoring discontinuities in routine room-level mobility patterns may provide an opportunity to predict individual human health and functional status or detect adverse events and trends.

[1]  Misha Pavel,et al.  Distributed Healthcare: Simultaneous Assessment of Multiple Individuals , 2007, IEEE Pervasive Computing.

[2]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[3]  S. Havlin,et al.  Scaling laws of human interaction activity , 2009, Proceedings of the National Academy of Sciences.

[4]  Diane J. Cook,et al.  Health Monitoring and Assistance to Support Aging in Place , 2006, J. Univers. Comput. Sci..

[5]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[6]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[7]  David Lazer,et al.  Inferring friendship network structure by using mobile phone data , 2009, Proceedings of the National Academy of Sciences.

[8]  Misha Pavel,et al.  Measuring changes in activity patterns during a norovirus epidemic at a retirement community , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  David J Kupfer,et al.  Morningness-Eveningness and Lifestyle Regularity , 2004, Chronobiology international.

[10]  Yu-Ru Lin,et al.  Mesoscopic Structure and Social Aspects of Human Mobility , 2012, PloS one.

[11]  K. Axhausen,et al.  Habitual travel behaviour: Evidence from a six-week travel diary , 2003 .

[12]  Hojung Cha,et al.  Evaluating mobility models for temporal prediction with high-granularity mobility data , 2012, 2012 IEEE International Conference on Pervasive Computing and Communications.

[13]  Andre Gustavo Adami,et al.  Unobtrusive assessment of activity patterns associated with mild cognitive impairment , 2008, Alzheimer's & Dementia.

[14]  Xin Lu,et al.  Approaching the Limit of Predictability in Human Mobility , 2013, Scientific Reports.

[15]  C. Carlsson Lessons learned from failed and discontinued clinical trials for the treatment of Alzheimer's disease: future directions. , 2008, Journal of Alzheimer's disease : JAD.

[16]  Misha Pavel,et al.  Unobtrusive and Ubiquitous In-Home Monitoring: A Methodology for Continuous Assessment of Gait Velocity in Elders , 2010, IEEE Transactions on Biomedical Engineering.

[17]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[18]  T H Monk,et al.  Differences over the life span in daily life-style regularity. , 1997, Chronobiology international.

[19]  Sandra Weintraub,et al.  Reducing case ascertainment costs in U.S. population studies of Alzheimer’s disease, dementia, and cognitive impairment—Part 2 , 2011, Alzheimer's & Dementia.

[20]  T. Hayes,et al.  One walk a year to 1000 within a year: continuous in-home unobtrusive gait assessment of older adults. , 2012, Gait & posture.

[21]  A. Pentland,et al.  Eigenbehaviors: identifying structure in routine , 2009, Behavioral Ecology and Sociobiology.

[22]  A. Barabasi,et al.  Quantifying social group evolution , 2007, Nature.

[23]  Jon Crowcroft,et al.  Human mobility models and opportunistic communications system design , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  R. E. Wilson,et al.  Mechanisms for spatio-temporal pattern formation in highway traffic models , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[25]  Pravin Varaiya Congestion, ramp metering and tolls , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[26]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[27]  Mikko Alava,et al.  Patterns, Entropy, and Predictability of Human Mobility and Life , 2012, PloS one.

[28]  T. Geisel,et al.  Forecast and control of epidemics in a globalized world. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[29]  M. Pavel,et al.  Intelligent Systems For Assessing Aging Changes: home-based, unobtrusive, and continuous assessment of aging. , 2011, The journals of gerontology. Series B, Psychological sciences and social sciences.

[30]  J. S. Long,et al.  Regression models for categorical dependent variables using Stata, 2nd Edition , 2005 .

[31]  A-L Barabási,et al.  Structure and tie strengths in mobile communication networks , 2006, Proceedings of the National Academy of Sciences.

[32]  James M. Keller,et al.  A smart home application to eldercare: current status and lessons learned. , 2009, Technology and health care : official journal of the European Society for Engineering and Medicine.

[33]  Aravind Srinivasan,et al.  Modelling disease outbreaks in realistic urban social networks , 2004, Nature.

[34]  G. Madey,et al.  Uncovering individual and collective human dynamics from mobile phone records , 2007, 0710.2939.

[35]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[36]  D. Macpherson,et al.  Population mobility and infectious diseases: the diminishing impact of classical infectious diseases and new approaches for the 21st century. , 2000, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[37]  J. Kaye Home-based technologies: A new paradigm for conducting dementia prevention trials , 2008, Alzheimer's & Dementia.

[38]  J. Guzmán Regression Models for Categorical Dependent Variables Using Stata , 2013 .