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Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data
9 references·4 citing·28 second hop
Opportunistic Discovery of Personal Places Using Multi-Source Sensor Data (2018)Opportunistic Discovery of Personal Place…Hierarchical Cooperative Discovery of Personal Places from Location Traces (2017)Hierarchical Cooperative Disc…Discovering places of interest using sensor data from smartphones and wearables (2017)Discovering places of interes…Cooperative Discovery of Personal Places from Location Traces (2016)Cooperative Discovery of Pers…Assessing health trends of college students using smartphones (2016)Assessing health trends of co…Discovering places of interest in everyday life from smartphone data (2012)Discovering places of interes…Towards rich mobile phone datasets: Lausanne data collection campaign (2010)Towards rich mobile phone dat…Mining Individual Life Pattern Based on Location History (2009)Mining Individual Life Patter…Extracting places from traces of locations (2005)Extracting places from traces…Using GPS to learn significant locations and predict movement across multiple users (2003)Using GPS to learn significan…Visualizing College Students’ Geo-Temporal Context-Varying Significant Phone Call Patterns (2021)Visualizing College Students’…Understanding College Students’ Phone Call Behaviors Towards a Sustainable Mobile Health and Well-Being Solution (2021)Understanding College Student…Predicting Next Call Duration: A Future Direction to Promote Mental Health in the Age of Lockdown (2021)Predicting Next Call Duration…Deriving College Students’ Phone Call Patterns to Improve Student Life (2021)Deriving College Students’ Ph…Understanding individual human mobility patterns (2008)Understanding mobility based on GPS data (2008)Lab And/Or Field? Measuring Personality Processes and Their Social Consequences (2015)Using Mobile Phone Sensor Technology for Mental Health Research: Integrated Analysis to Identify Hidden Challenges and Potential Solutions (2018)Towards a comprehensive set of GPS-based indicators reflecting the multidimensional nature of daily mobility for applications in health and aging research (2019)The Museum Wearable: real-time sensor-driven understanding of visitors' interests for personalized visually-augmented museum experiences (2002)Inferring High-Level Behavior from Low-Level Sensors (2003)Learning and inferring transportation routines (2007)Discovering human places of interest from multimodal mobile phone data (2010)Personal data vaults (2010)Predestination: Inferring Destinations from Partial Trajectories (2006)What's Your Next Move: User Activity Prediction in Location-based Social Networks (2013)Analysis of human mobility patterns from GPS trajectories and contextual information (2015)Spatio-Temporal Analysis of Passenger Travel Patterns in Massive Smart Card Data (2017)Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits (2014)StudentLife (2014)Effects of COVID-19 on College Students’ Mental Health in the United States: Interview Survey Study (2020)Mental Health, Substance Use, and Suicidal Ideation During the COVID-19 Pandemic — United States, June 24–30, 2020 (2020)The psychological impact of the COVID-19 epidemic on college students in China (2020)Machine Learning, Wearable, and Smartphones for Student’s Mental Health Analysis (2023)Reality mining: sensing complex social systems (2005)Computational Social Science (2009)Inferring friendship network structure by using mobile phone data (2009)Discovering routines from large-scale human locations using probabilistic topic models (2011)Who's Who with Big-Five: Analyzing and Classifying Personality Traits with Smartphones (2011)A survey on deep learning for human mobility (2023)Opportunistic Discovery of Personal Places Using Smartphone and Fitness Tracker Data (2018)Assessing Older Adults’ Daily Mobility: A Comparison of GPS-Derived and Self-Reported Mobility Indicators (2019)
this work references cited by second hop

References

  • 2003Using GPS to learn significant locations and predict movement across multiple users1,060 cited
  • 2009Mining Individual Life Pattern Based on Location History281 cited
  • 2010Towards rich mobile phone datasets: Lausanne data collection campaign278 cited
  • 2005Extracting places from traces of locations186 cited
  • 2012Discovering places of interest in everyday life from smartphone data137 cited
  • 2017Hierarchical Cooperative Discovery of Personal Places from Location Traces43 cited
  • 2016Assessing health trends of college students using smartphones41 cited
  • 2017Discovering places of interest using sensor data from smartphones and wearables40 cited
  • 2016Cooperative Discovery of Personal Places from Location Traces38 cited

Cited by

  • 2021Deriving College Students’ Phone Call Patterns to Improve Student Life20 cited
  • 2021Visualizing College Students’ Geo-Temporal Context-Varying Significant Phone Call Patterns18 cited
  • 2021Predicting Next Call Duration: A Future Direction to Promote Mental Health in the Age of Lockdown17 cited
  • 2021Understanding College Students’ Phone Call Behaviors Towards a Sustainable Mobile Health and Well-Being Solution13 cited

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