scholarly journals Practical Differentially Private Modeling of Human Movement Data

2016 ◽  
pp. 170-178 ◽  
Author(s):  
Harichandan Roy ◽  
Murat Kantarcioglu ◽  
Latanya Sweeney
Keyword(s):  
Human Movement ◽  
Movement Data

scholarly journals Variation in human mobility and its impact on the risk of future COVID-19 outbreaks in Taiwan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Meng-Chun Chang ◽  
Rebecca Kahn ◽  
Yu-An Li ◽  
Cheng-Sheng Lee ◽  
Caroline O. Buckee ◽  
...  
Keyword(s):  
Disease Surveillance ◽  
Initial Conditions ◽  
Human Mobility ◽  
Human Movement ◽  
Disease Spread ◽  
Spatial Spread ◽  
Movement Data ◽  
Intercity Travel ◽  
Travel Restrictions ◽  
The Impact

Abstract Background As COVID-19 continues to spread around the world, understanding how patterns of human mobility and connectivity affect outbreak dynamics, especially before outbreaks establish locally, is critical for informing response efforts. In Taiwan, most cases to date were imported or linked to imported cases. Methods In collaboration with Facebook Data for Good, we characterized changes in movement patterns in Taiwan since February 2020, and built metapopulation models that incorporate human movement data to identify the high risk areas of disease spread and assess the potential effects of local travel restrictions in Taiwan. Results We found that mobility changed with the number of local cases in Taiwan in the past few months. For each city, we identified the most highly connected areas that may serve as sources of importation during an outbreak. We showed that the risk of an outbreak in Taiwan is enhanced if initial infections occur around holidays. Intracity travel reductions have a higher impact on the risk of an outbreak than intercity travel reductions, while intercity travel reductions can narrow the scope of the outbreak and help target resources. The timing, duration, and level of travel reduction together determine the impact of travel reductions on the number of infections, and multiple combinations of these can result in similar impact. Conclusions To prepare for the potential spread within Taiwan, we utilized Facebook’s aggregated and anonymized movement and colocation data to identify cities with higher risk of infection and regional importation. We developed an interactive application that allows users to vary inputs and assumptions and shows the spatial spread of the disease and the impact of intercity and intracity travel reduction under different initial conditions. Our results can be used readily if local transmission occurs in Taiwan after relaxation of border control, providing important insights into future disease surveillance and policies for travel restrictions.

Arm Movement Analysis in Human Daily Life for Improving Durability of Artificial Elbow Joint

2020 ◽  
Author(s):  
Chihiro Kamio ◽  
Tatsuhito Aihara ◽  
Gaku Minorikawa
Keyword(s):  
Daily Life ◽  
Human Life ◽  
Arm Movement ◽  
Movement Analysis ◽  
Human Movement ◽  
Healthcare Applications ◽  
Artificial Joints ◽  
Measuring Device ◽  
Movement Data ◽  
Set Up

Abstract Human movement data can contribute to the quality improvement of industrial and medical products affected by such movement. Such data can be used to improve the quality of industrial products as well as in healthcare applications, such as the development of artificial joints. To develop and design artificial joints with enhance durability, it is necessary to set up standards of durability using human movement data in daily life. The aim of this study is to obtain data that contributes to the improvement in durability of artificial elbow joints. We have developed a wearable device that can measure its self-acceleration, angular velocity, and quaternions to collect human movement data continuously for long-term. Additionally, we collected the arm movement data of 30 participants using the developed device. The participants of this study carried on with their normal lives with the measuring device worn on their wrist. This study calculated the posture of the wrist over time using quaternions and mainly analyzed posture changes. We clarified the characteristics and trends of the movement of bending the elbow in daily human life.

scholarly journals BIG BICYCLE DATA PROCESSING: FROM PERSONAL DATA TO URBAN APPLICATIONS

2016 ◽  
Vol III-2 ◽  
pp. 173-179
Author(s):  
C. J. Pettit ◽  
S. N. Lieske ◽  
S. Z. Leao
Keyword(s):  
Hot Spots ◽  
Smart Phone ◽  
Personal Data ◽  
Human Movement ◽  
Relevant Information ◽  
Policy Makers ◽  
Movement Data ◽  
The City ◽  
Smart Phone Application ◽  
Source Of Information

Understanding the flows of people moving through the built environment is a vital source of information for the planners and policy makers who shape our cities. Smart phone applications enable people to trace themselves through the city and these data can potentially be then aggregated and visualised to show hot spots and trajectories of macro urban movement. In this paper our aim is to develop procedures for cleaning, aggregating and visualising human movement data and translating this into policy relevant information. In conducting this research we explore using bicycle data collected from a smart phone application known as RiderLog. We focus on the RiderLog application initially in the context of Sydney, Australia and discuss the procedures and challenges in processing and cleaning this data before any analysis can be made. We then present some preliminary map results using the CartoDB online mapping platform where data are aggregated and visualised to show hot spots and trajectories of macro urban movement. We conclude the paper by highlighting some of the key challenges in working with such data and outline some next steps in processing the data and conducting higher volume and more extensive analysis.

Detecting high-dimensional determinism in time series with application to human movement data

2012 ◽  
Vol 13 (4) ◽  
pp. 1891-1903 ◽  
Author(s):  
Sofiane Ramdani ◽  
Frédéric Bouchara ◽  
Olivier Caron
Keyword(s):  
Time Series ◽  
Human Movement ◽  
High Dimensional ◽  
Movement Data

scholarly journals BIG BICYCLE DATA PROCESSING: FROM PERSONAL DATA TO URBAN APPLICATIONS

2016 ◽  
Vol III-2 ◽  
pp. 173-179 ◽  
Author(s):  
C. J. Pettit ◽  
S. N. Lieske ◽  
S. Z. Leao
Keyword(s):  
Hot Spots ◽  
Smart Phone ◽  
Personal Data ◽  
Human Movement ◽  
Relevant Information ◽  
Policy Makers ◽  
Movement Data ◽  
The City ◽  
Smart Phone Application ◽  
Source Of Information

Understanding the flows of people moving through the built environment is a vital source of information for the planners and policy makers who shape our cities. Smart phone applications enable people to trace themselves through the city and these data can potentially be then aggregated and visualised to show hot spots and trajectories of macro urban movement. In this paper our aim is to develop procedures for cleaning, aggregating and visualising human movement data and translating this into policy relevant information. In conducting this research we explore using bicycle data collected from a smart phone application known as RiderLog. We focus on the RiderLog application initially in the context of Sydney, Australia and discuss the procedures and challenges in processing and cleaning this data before any analysis can be made. We then present some preliminary map results using the CartoDB online mapping platform where data are aggregated and visualised to show hot spots and trajectories of macro urban movement. We conclude the paper by highlighting some of the key challenges in working with such data and outline some next steps in processing the data and conducting higher volume and more extensive analysis.

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