Life-space characterization from cellular telephone collected GPS data

Abstract Life-space is an emerging method for measuring older adults’ functional status. Although global positioning system (GPS)-enabled smartphones can collect life-space data passively and accurately, researchers lack an effective process to derive activity information from the raw GPS data. In addition, the influence of GPS retrieving frequency on life-space characterization is unknown. We describe a GPS data processing procedure to estimate life-space. A cellular telephone was used to collect GPS data by a subject during a 4-month period. The GPS data processing procedure was then implemented and evaluated in terms of classification accuracy, reliability, and sensitivity to observation frequency. The proposed scheme generated sufficient zone-based activity information to characterize an individual’s life-space. The speed-based sensitivity assessment suggests 75 s as an appropriate GPS observation interval for smartphone based life-space data collection.

[1]  E. H. Goulding,et al.  Cellular Telephones Measure Activity and Lifespace in Community‐Dwelling Adults: Proof of Principle , 2011, Journal of the American Geriatrics Society.

[2]  K. W. Axhausen,et al.  Processing GPS raw data without additional information , 2022 .

[3]  Xing Xie,et al.  Collaborative location and activity recommendations with GPS history data , 2010, WWW '10.

[4]  Jun Wu,et al.  Automated time activity classification based on global positioning system (GPS) tracking data , 2011, Environmental health : a global access science source.

[5]  Cynthia Owsley,et al.  The Life Space Questionnaire: A Measure of the Extent of Mobility of Older Adults , 1999 .

[6]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD 2000.

[7]  Cynthia Brown,et al.  Trajectories of Life-Space Mobility After Hospitalization , 2009, Annals of Internal Medicine.

[8]  A. Cooper,et al.  Greenspace and children's physical activity: a GPS/GIS analysis of the PEACH project. , 2010, Preventive medicine.

[9]  J. Fortenberry,et al.  International Journal of Health Geographics Open Access Using Gps-enabled Cell Phones to Track the Travel Patterns of Adolescents , 2022 .

[10]  Pascal Poupart,et al.  VALMA : VOICE , ACTIVITY , AND LOCATION MONITORING FOR ALZHEIMER ’ S DISEASE AND RELATED DEMENTIAS , 2011 .

[11]  Fahui Wang,et al.  A Scale-Space Clustering Method: Mitigating the Effect of Scale in the Analysis of Zone-Based Data , 2008 .

[12]  M. Tinetti,et al.  The Nursing Home Life‐Space Diameter , 1990, Journal of the American Geriatrics Society.

[13]  Andrew P. Witkin,et al.  Scale-Space Filtering , 1983, IJCAI.

[14]  Thomas W Scott,et al.  Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus , 2009 .

[15]  Charles E Matthews,et al.  The built environment and location-based physical activity. , 2010, American journal of preventive medicine.

[16]  A. Bowling.  Mode of questionnaire administration can have serious effects on data quality. , 2005, Journal of public health.

[17]  Scott Duncan,et al.  Combining GPS, GIS, and accelerometry: methodological issues in the assessment of location and intensity of travel behaviors. , 2010, Journal of physical activity & health.

[18]  Joshua Auld,et al.  An automated GPS-based prompted recall survey with learning algorithms , 2009 .

[19]  R. Allman,et al.  Measuring Life‐Space Mobility in Community‐Dwelling Older Adults , 2003, Journal of the American Geriatrics Society.

[20]  Chengsheng Jiang,et al.  Title Automated time activity classification based on global positioning system ( GPS ) tracking data Permalink , 2011 .

[21]  D. Bennett,et al.  Association Between Life Space and Risk of Mortality in Advanced Age , 2010, Journal of the American Geriatrics Society.

[22]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[23]  C. Peel,et al.  Assessing mobility in older adults: the UAB Study of Aging Life-Space Assessment. , 2005, Physical therapy.

[24]  J. Koenderink.  The structure of images , 2004, Biological Cybernetics.

[25]  D. Bennett,et al.  Correlates of Life Space in a Volunteer Cohort of Older Adults , 2007, Experimental aging research.

[26]  Michael May,et al.  Semantic Annotation of GPS Trajectories , 2008 .

[27]  F. Craik,et al.  Age differences in recall and recognition , 1987 .

[28]  Catherine T. Lawson,et al.  A GPS/GIS method for travel mode detection in New York City , 2012, Comput. Environ. Urban Syst..

[29]  Nadine Schüssler.  Processing GPS raw data without additional information , 2009 .

[30]  Timothy C. Matisziw,et al.  Inferring network paths from point observations , 2012, Int. J. Geogr. Inf. Sci..

[31]  Henry A. Kautz,et al.  Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields , 2007, Int. J. Robotics Res..

[32]  Yee Leung,et al.  Clustering by Scale-Space Filtering , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  Michael Batty,et al.  Environment and Planning B , 1982 .

[34]  Mitch J Duncan,et al.  Applying GPS to enhance understanding of transport-related physical activity. , 2009, Journal of science and medicine in sport.

[35]  David Ogilvie,et al.  Use of global positioning systems to study physical activity and the environment: a systematic review. , 2011, American journal of preventive medicine.

[36]  Edmund Seto,et al.  A study of community design, greenness, and physical activity in children using satellite, GPS and accelerometer data. , 2012, Health & place.

[37]  Lian Duan,et al.  A Local Density Based Spatial Clustering Algorithm with Noise , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[38]  David Martin,et al.  Zone design for environment and health studies using pre-aggregated data. , 2005, Social science & medicine.

[39]  Thad Starner,et al.  Using GPS to learn significant locations and predict movement across multiple users , 2003, Personal and Ubiquitous Computing.

[40]  Minsoo Kang,et al.  Development and validation of a movement and activity in physical space score as a functional outcome measure. , 2011, Archives of physical medicine and rehabilitation.

[41]  Monika Sester,et al.  Multi-stage approach to travel-mode segmentation and classification of gps traces , 2012 .

[42]  Gleb Haynatzki,et al.  Treadmill gait speeds correlate with physical activity counts measured by cell phone accelerometers. , 2012, Gait & posture.

[43]  Christian S. Jensen,et al.  Mining significant semantic locations from GPS data , 2010, Proc. VLDB Endow..

[44]  Kai Elgethun,et al.  Time-location analysis for exposure assessment studies of children using a novel global positioning system instrument. , 2003, Environmental health perspectives.

[45]  Neng Wan,et al.  Assessing Smart Phones for Generating Life-Space Indicators , 2013, Environment and planning. B, Planning & design.

[46]  Xing Xie,et al.  Learning transportation mode from raw gps data for geographic applications on the web , 2008, WWW.

[47]  Miguel A. Labrador,et al.  Automating Mode Detection Using Neural Networks and Assisted GPS Data Collected Using GPS-Enabled Mobile Phones , 2008 .

[48]  Scott Duncan,et al.  Using global positioning systems in health research: a practical approach to data collection and processing. , 2011, American journal of preventive medicine.

[49]  U S Nayak,et al.  The life-space diary: a measure of mobility in old people at home. , 1985, International rehabilitation medicine.

[50]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .