scholarly journals Integrating Insights About Human Movement Patterns From Digital Data Into Psychological Science

2021 ◽  
pp. 096372142110423
Author(s):  
Joanne Hinds ◽  
Olivia Brown ◽  
Laura G. E. Smith ◽  
Lukasz Piwek ◽  
David A. Ellis ◽  
...  
Keyword(s):  
Data Science ◽  
Wearable Sensors ◽  
Movement Patterns ◽  
Human Movement ◽  
Psychological Theory ◽  
Digital Data ◽  
Movement Data ◽  
Disease Information ◽  
Or Groups ◽  
Data Driven Modeling

Understanding people’s movement patterns has many important applications, from analyzing habits and social behaviors, to predicting the spread of disease. Information regarding these movements and their locations is now deeply embedded in digital data generated via smartphones, wearable sensors, and social-media interactions. Research has largely used data-driven modeling to detect patterns in people’s movements, but such approaches are often devoid of psychological theory and fail to capitalize on what movement data can convey about associated thoughts, feelings, attitudes, and behavior. This article outlines trends in current research in this area and discusses how psychologists can better address theoretical and methodological challenges in future work while capitalizing on the opportunities that digital movement data present. We argue that combining approaches from psychology and data science will improve researchers’ and policy makers’ abilities to make predictions about individuals’ or groups’ movement patterns. At the same time, an interdisciplinary research agenda will provide greater capacity to advance psychological theory.

scholarly journals The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility

2015 ◽  
Vol 22 (6) ◽  
pp. 1120-1125 ◽  
Author(s):  
Joy P Ku ◽  
Jennifer L Hicks ◽  
Trevor Hastie ◽  
Jure Leskovec ◽  
Christopher Ré ◽  
...  
Keyword(s):  
Data Science ◽  
Human Mobility ◽  
Wearable Sensors ◽  
Human Movement ◽  
Science Research ◽  
Science Methods ◽  
Social Modeling ◽  
Mobility Data ◽  
Using Data ◽  
And Training

Abstract Regular physical activity helps prevent heart disease, stroke, diabetes, and other chronic diseases, yet a broad range of conditions impair mobility at great personal and societal cost. Vast amounts of data characterizing human movement are available from research labs, clinics, and millions of smartphones and wearable sensors, but integration and analysis of this large quantity of mobility data are extremely challenging. The authors have established the Mobilize Center ( http://mobilize.stanford.edu ) to harness these data to improve human mobility and help lay the foundation for using data science methods in biomedicine. The Center is organized around 4 data science research cores: biomechanical modeling, statistical learning, behavioral and social modeling, and integrative modeling. Important biomedical applications, such as osteoarthritis and weight management, will focus the development of new data science methods. By developing these new approaches, sharing data and validated software tools, and training thousands of researchers, the Mobilize Center will transform human movement research.

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.

scholarly journals Ensemble model for estimating continental-scale patterns of human movement: a case study of Australia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karen McCulloch ◽  
Nick Golding ◽  
Jodie McVernon ◽  
Sarah Goodwin ◽  
Martin Tomko
Keyword(s):  
Spatial Scales ◽  
Predictive Ability ◽  
Movement Patterns ◽  
Human Movement ◽  
Continental Scale ◽  
Movement Characteristics ◽  
Human Movements ◽  
Movement Models ◽  
Epidemiology Data

AbstractUnderstanding human movement patterns at local, national and international scales is critical in a range of fields, including transportation, logistics and epidemiology. Data on human movement is increasingly available, and when combined with statistical models, enables predictions of movement patterns across broad regions. Movement characteristics, however, strongly depend on the scale and type of movement captured for a given study. The models that have so far been proposed for human movement are best suited to specific spatial scales and types of movement. Selecting both the scale of data collection, and the appropriate model for the data remains a key challenge in predicting human movements. We used two different data sources on human movement in Australia, at different spatial scales, to train a range of statistical movement models and evaluate their ability to predict movement patterns for each data type and scale. Whilst the five commonly-used movement models we evaluated varied markedly between datasets in their predictive ability, we show that an ensemble modelling approach that combines the predictions of these models consistently outperformed all individual models against hold-out data.

scholarly journals Control Strategies for Gait Tele-Rehabilitation System Based on Parallel Robotics

2021 ◽  
Vol 11 (23) ◽  
pp. 11095
Author(s):  
Antonio P. L. Bo ◽  
Leslie Casas ◽  
Gonzalo Cucho-Padin ◽  
Mitsuhiro Hayashibe ◽  
Dante Elias
Keyword(s):  
Degrees Of Freedom ◽  
Control Strategies ◽  
Subjective Evaluation ◽  
Wearable Sensors ◽  
Movement Data ◽  
Trunk Inclination ◽  
Limited Degree ◽  
Parallel Robotics ◽  
Limb Rehabilitation ◽  
Technical Capacity

Among end-effector robots for lower limb rehabilitation, systems based on Stewart–Gough platforms enable independent movement of each foot in six degrees of freedom. Nevertheless, control strategies described in recent literature have not been able to fully explore the potential of such a mechatronic system. In this work, we propose two novel approaches for controlling a gait simulator based on Stewart–Gough platforms. The first strategy provides the therapist direct control of each platform using movement data measured by wearable sensors. The following scheme is designed to improve the level of engagement of the patient by enabling a limited degree of control based on trunk inclination. Both strategies are designed to facilitate future studies in tele-rehabilitation settings. Experimental results have illustrated the feasibility of both control interfaces, either in terms of system performance or user subjective evaluation. Technical capacity to deploy in tele-rehabilitation was also verified in this work.

scholarly journals Excavating awareness and power in data science: A manifesto for trustworthy pervasive data research

2021 ◽  
Vol 8 (2) ◽  
pp. 205395172110407
Author(s):  
Katie Shilton ◽  
Emanuel Moss ◽  
Sarah A. Gilbert ◽  
Matthew J. Bietz ◽  
Casey Fiesler ◽  
...  
Keyword(s):  
Data Science ◽  
Methodological Approach ◽  
Digital Data ◽  
Colonial Legacy ◽  
Research Scholarship ◽  
History Of ◽  
Rich Information ◽  
The Rich ◽  
Relationship Of ◽  
The Relationship

Frequent public uproar over forms of data science that rely on information about people demonstrates the challenges of defining and demonstrating trustworthy digital data research practices. This paper reviews problems of trustworthiness in what we term pervasive data research: scholarship that relies on the rich information generated about people through digital interaction. We highlight the entwined problems of participant unawareness of such research and the relationship of pervasive data research to corporate datafication and surveillance. We suggest a way forward by drawing from the history of a different methodological approach in which researchers have struggled with trustworthy practice: ethnography. To grapple with the colonial legacy of their methods, ethnographers have developed analytic lenses and researcher practices that foreground relations of awareness and power. These lenses are inspiring but also challenging for pervasive data research, given the flattening of contexts inherent in digital data collection. We propose ways that pervasive data researchers can incorporate reflection on awareness and power within their research to support the development of trustworthy data science.

‘Personal data literacies’: A critical literacies approach to enhancing understandings of personal digital data

2018 ◽  
Vol 21 (2) ◽  
pp. 419-437 ◽  
Author(s):  
Luci Pangrazio ◽  
Neil Selwyn
Keyword(s):  
Digital Literacy ◽  
Data Science ◽  
Personal Data ◽  
Digital Data ◽  
Data Generation ◽  
Critical Literacies ◽  
And Control ◽  
Future Education ◽  
Socially Situated ◽  
Crucial Part

The capacity to understand and control one’s personal data is now a crucial part of living in contemporary society. In this sense, traditional concerns over supporting the development of ‘digital literacy’ are now being usurped by concerns over citizens’ ‘data literacies’. In contrast to recent data safety and data science approaches, this article argues for a more critical form of ‘personal data literacies’ where digital data are understood as socially situated and context dependent. Drawing on the critical literacies tradition, the article outlines a range of salient socio-technical understandings of personal data generation and processing. Specifically, the article proposes a framework of ‘Personal Data Literacies’ that distinguishes five significant domains: (1) Data Identification, (2) Data Understandings, (3) Data Reflexivity, (4) Data Uses, and (5) Data Tactics. The article concludes by outlining the implications of this framework for future education and research around the area of individuals’ understandings of personal data.

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