Human Mobility Research Articles

A Comprehensive Review of Vision-Based Sensor Systems for Human Gait Analysis

Analysis of the human gait represents a fundamental area of investigation within the broader domains of biomechanics, clinical research, and numerous other interdisciplinary fields. The progression of visual sensor technology and machine learning algorithms has enabled substantial developments in the creation of human gait analysis systems. This paper presents a comprehensive review of the advancements and recent findings in the field of vision-based human gait analysis systems over the past five years, with a special emphasis on the role of vision sensors, machine learning algorithms, and technological innovations. The relevant papers were subjected to analysis using the PRISMA method, and 72 articles that met the criteria for this research project were identified. A detailing of the most commonly used visual sensor systems, machine learning algorithms, human gait analysis parameters, optimal camera placement, and gait parameter extraction methods is presented in the analysis. The findings of this research indicate that non-invasive depth cameras are gaining increasing popularity within this field. Furthermore, depth learning algorithms, such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, are being employed with increasing frequency. This review seeks to establish the foundations for future innovations that will facilitate the development of more effective, versatile, and user-friendly gait analysis tools, with the potential to significantly enhance human mobility, health, and overall quality of life. This work was supported by [GOBIERNO DE ESPANA/PID2023-150967OB-I00].

People and horses in the Late Roman Period - Late Migration Period (3rd-7th centuries) in Lithuania: origins and social context

This paper presents a study analysing the mobility and possible immigration of humans and horses in Lithuania in the Late Roman Period – Migration Period (3rd–7th centuries AD), based on 87Sr/86Sr stable isotopes analysis. During the study, we carried out strontium analysis of 40 human teeth from 11 burial sites and 13 horses from six burial sites. In addition, to reconstruct bioavailable 87Sr/86Sr ranges, we analysed 87Sr/86Sr of 41 animals from 15 prehistoric and historic sites. A human sample was collected to evaluate the presumed immigration directions and mobility of both sexes, different social groups, and in different regions of Lithuania. Stable isotope analysis, radiocarbon dating, biometric measurements, and other studies have provided new insights into the origins, mobility, and other aspects of lifestyle and horse breeding in the mid-1st millennium AD, and have also drawn prospects for future research on these topics.

Introduction of Vector-Borne Infections in Europe: Emerging and Re-Emerging Viral Pathogens with Potential Impact on One Health

The rise and resurgence of vector-borne diseases (VBDs) in Europe pose an expanding public health challenge, exacerbated by climate change, globalization, and ecological disruptions. Both arthropod-borne viruses (arboviruses) transmitted by ticks such as Crimean–Congo hemorrhagic fever and arboviruses transmitted by mosquitoes like dengue, Chikungunya, Zika, and Japanese encephalitis have broadened their distribution due to rising temperatures, changes in rainfall, and increased human mobility. By emphasizing the importance of interconnected human, animal, and environmental health, integrated One Health strategies are crucial in addressing this complex issue. Europe faces increased risk due to the expanding habitats of disease-carrying organisms, the spread of new species like Aedes albopictus since 2013, and increased movement of infected individuals between countries, leading European countries to implement strategies such as enhanced surveillance systems, public awareness campaigns, and prompt outbreak response strategies. However, the lack of both targeted antiviral therapies and vaccines for many arboviruses, together with undetected or asymptomatic cases, hamper containment efforts. Therefore, it is important to have integrated strategies that combine climate modeling, disease surveillance, and public health interventions to address expected changes in disease patterns due to global changes. This review explores the spread of arboviruses in Europe, highlighting their historical context, current transmission dynamics, and their impact on public health.

Mobile Sleep Lab: Comparison of polysomnographic parameters with a conventional sleep laboratory.

In remote areas, visiting a laboratory for sleep testing is inconvenient. We, therefore, developed a Mobile Sleep Lab in a bus powered by fuel cells with two sleep measurement chambers. As the environment in the bus could affect sleep, we examined whether sleep testing in the Mobile Sleep Lab was as feasible as in a conventional sleep laboratory (Human Sleep Lab). We tested 15 healthy adults for four nights using polysomnography (the first two nights at the Human Sleep Lab or Mobile Sleep Lab with a switch to the other facility for the next two nights). Sleep variables of the four measurements were used to assess the discrepancy of different places or different nights. No significant differences were found between the laboratories other than the percentage of total sleep time in stage N3. Next, we analyzed the intraclass correlation coefficient to evaluate the test-retest reliability. The intraclass correlation coefficient between these two measurements: the Human Sleep Lab and Mobile Sleep Lab showed similar reliability for the same sleep variables. The intraclass correlation coefficient revealed that several sleep indexes, such as total sleep time, sleep efficiency, wake after sleep onset, percentage of stage N1, and stage R latency, showed poor reliabilities (<0.5) based on Koo and Li's criteria. In contrast, the percentage of stage N3 showed moderate (0.5-0.75) or good (0.75-0.9) reliabilities. As almost all sleep variables showed no difference and same level of test-retest reliability between the Mobile Sleep Lab and Human Sleep Lab, the Mobile Sleep Lab might be suitable for conducting polysomnography as a conventional sleep laboratory. The reduction in N3 in the Mobile Sleep Lab should be scrutinized in the larger sample, including sleep disorders. Practical application of the Mobile Sleep Lab can transform sleep medicine in remote areas.

Multi-region infectious disease prediction modeling based on spatio-temporal graph neural network and the dynamic model.

Human mobility between different regions is a major factor in large-scale outbreaks of infectious diseases. Deep learning models incorporating infectious disease transmission dynamics for predicting the spread of multi-regional outbreaks due to human mobility have become a hot research topic. In this study, we incorporate the Graph Transformer Neural Network and graph learning mechanisms into a metapopulation SIR model to build a hybrid framework, Metapopulation Graph Transformer Neural Network (M-Graphormer), for high-dimensional parameter estimation and multi-regional epidemic prediction. The framework effectively solves the problem that existing models may lose some hidden spatial dependencies in the data when dealing with the dynamic graph structure of the network due to human mobility. We performed multi-wave infectious disease prediction in multiple regions based on real epidemic data. The results show that the framework is capable of performing high-dimensional parameter estimation and accurately predicting epidemic transmission dynamics in multiple regions even with low data quality. In addition, we retrospectively extrapolate the temporal evolution patterns of contact rate under different interventions implemented in different regions, reflecting the dynamics of intervention intensity and the need for flexibility in adjusting interventions in different regions. To provide early warning of infectious disease transmission, we retrospectively predicted the arrival time of infectious diseases using data from the early stages of outbreaks.

Socioeconomic correlates of urban mobility trends in two Australian cities during transitional periods of the COVID-19 pandemic

During the COVID-19 pandemic, both government-mandated lockdowns and discretionary changes in behaviour combined to produce dramatic and abrupt changes to human mobility patterns. To understand the socioeconomic determinants of intervention compliance and discretionary behavioural responses to epidemic threats, we investigate whether changes in human mobility showed a systematic variation by socioeconomic status during two distinct periods of the COVID-19 pandemic in Australia. We analyse mobility data from two major urban centres and compare the trends during mandated stay-at-home policies and after the full relaxation of nonpharmaceutical interventions, which coincided with a large surge of COVID-19 cases. We analyse data aggregated from de-identified global positioning system trajectories, collated from providers of mobile phone applications and aggregated to small spatial regions. Our results demonstrate systematic decreases in mobility relative to the pre-pandemic baseline with the index of education and occupation, for both pandemic periods. On the other hand, the index of economic resources was not correlated with mobility changes. This result contrasts with observations from other national contexts, where reductions in mobility typically increased strongly with indicators of wealth. Our analysis suggests that economic support policies in place during the initial period of stay-at-home orders in Australia facilitated broad reductions in mobility across the economic spectrum.

Random walks with long-range memory on networks.

We study an exactly solvable random walk model with long-range memory on arbitrary networks. The walker performs unbiased random steps to nearest-neighbor nodes and intermittently resets to previously visited nodes in a preferential way such that the most visited nodes have proportionally a higher probability to be chosen for revisit. The occupation probability can be expressed as a sum over the eigenmodes of the standard random walk matrix of the network, where the amplitudes slowly decay as power-laws at large times, instead of exponentially. The stationary state is the same as in the absence of memory, and detailed balance is fulfilled. However, the relaxation of the transient part becomes critically self-organized at late times, as it is dominated by a single power-law whose exponent depends on the second largest eigenvalue and on the resetting probability. We apply our findings to finite networks, such as rings, complete graphs, Watts-Strogatz, and Barabási-Albert networks, and to Barbell and comb-like graphs. Our study could be of interest for modeling complex transport phenomena, such as human mobility, epidemic spreading, or animal foraging.

Erratum to Discussing linkages between climate change, human mobility and health in the Caribbean: the case of Dominica. A qualitative study

Erratum to Discussing linkages between climate change, human mobility and health in the Caribbean: the case of Dominica. A qualitative study

Discussing the Development in the Real Estate Sector in the Framework of University-City Interaction

Universities exert a considerable influence on urban economies through the direct and indirect consumption they generate within the city. The former is attributable to the expenditure of students and staff, while the latter is a consequence of the demands they create for accommodation, transport, trade, and services. One of the most fundamental areas where these effects can be monitored is the housing and accommodation shaped by the demands of students and staff. Following the establishment of Kırklareli University in 2007, a notable increase in human mobility has been observed in the city center of Kırklareli, accompanied by significant changes in the spatial order. The principal objective of this study is to examine the housing-based spatial transformation and tendency in the city center of Kırklareli in terms of the defined periods following the establishment of the university. In this context, housing sale data and its distribution were analyzed in a spatial/structural dimension using the "Land Registry Transaction Statistics" and "Parcel Query-Analysis-Independent Section Sales Density Distribution database," which were published by the Republic of Turkey Ministry of Environment, Urbanization and Climate Change-General Directorate of Land Registry and Cadaster. The increase and tendency were evaluated. A statistical query and correlation analysis were conducted on the data, with a particular focus on establishing a comparison between the change in population and the number of students, as well as academic and administrative staff. The resulting data were then subjected to a process of interpretation. In addition, current master plans were evaluated, and suggestions were developed for the factors that should be considered in urban planning. The study revealed that, regardless of the current population development, student-oriented dynamics directly affect the real estate sector in terms of housing sales, mobility, and spatial transformation due to the growth of the urban population and the increase in demand.

Transculturality and Self-discovery in Alev Croutier’s Seven Houses

With the increased human mobility, people can no longer define themselves within a monocultural identity, leading to the emergence of multi-layered and fluid identities, a phenomenon known as transculturality. At this intersection of cultures, new possibilities, perspectives, and ways of being emerge. Analysing Alev Lytle Croutier’s Seven Houses (2003) is key to understanding how Croutier constructs her transcultural identity. Croutier, who describes her literary self as not belonging to any of the cultures in which she has lived, needs to uproot her established cultural truths and challenge them with a new understanding. Analysing the semi-fictional autobiography Seven Houses is crucial not only for understanding how Croutier constructed her transcultural feminist identity but also for exploring how she subverted the conventional norms of the Western novel and autobiography to forge a new literary self. This article aims to examine how Croutier crafted a transcultural space through her narrative style, positioned between East and West, spirituality and rationality, magical realism and reality. It also seeks to explore how she used this space to reconcile with her past and resolve the internal divisions within herself.

Optimizing county-level infectious respiratory disease forecasts: a pandemic case study integrating social media-based physical and social connectivity networks

ABSTRACT Forecasting infectious respiratory diseases is crucial for effective prevention and intervention strategies. However, existing time series forecasting models that incorporate human mobility data have faced challenges in making localized predictions on a large scale across the country due to data costs and constraints. Using the COVID-19 pandemic as a case study, this research explores whether integrating social media-based place and social connectivity networks can improve predictions of disease transmission at the county level across various regions. Place connectivity networks, derived from Twitter users and tweets, and social connectivity networks, based on Facebook interactions, were used to map spatial and social linkages between locations. These networks were integrated into weekly COVID-19 incidence data across 2,927 U.S. counties using Long Short-Term Memory (LSTM) models. The combined connectivity-weighted model significantly enhanced prediction accuracy, reducing Mean Absolute Percentage Error (MAPE) by 49.38% across 96.62% of the counties, with the greatest improvements observed in urban and Northeastern counties. The results demonstrate that combining connectivity networks enhances prediction accuracy, offering a scalable and sustainable solution for localized disease forecasting on a large scale across diverse geographic areas using publicly accessible social media data.

Revealing urban area from mobile positioning data

Researchers face the trade-off between publishing mobility data along with their papers while protecting the privacy of the individuals. In addition to the anonymization process, other techniques, such as spatial discretization and location concealing or removal, are applied to achieve these dual objectives. The primary research question is whether concealing the observation area is an adequate form of protection or whether human mobility patterns in urban areas are inherently revealing of location. The characteristics of the mobility data, such as the number of activity records in a given spatial unit, can reveal the silhouette of the urban landscape, which can be used to infer the identity of the city in question. The presented locating method was tested on multiple cities using different open datasets and coarser spatial discretization units. While publishing mobility data is essential for research, concealing the observation area is insufficient to prevent the identification of the urban area. Instead of obscuring the observation area, noise should be added to the trajectories to mitigate privacy risks regarding the individuals.

The Epidemiology of Dengue Fever: Comparative Analysis of Urban and Rural Transmission Dynamics

Background: Dengue fever poses a significant public health challenge, with over 3 billion individuals at risk globally. Traditionally, urban environments have been associated with higher dengue transmission rates, primarily due to dense populations and favorable conditions for Aedes aegypti proliferation. However, recent studies indicate a notable increase in dengue prevalence in rural areas. Methods: This study conducted a comprehensive literature review, employing Medical Subject Headings (MeSH) and keyword searches across multiple databases, including PubMed, Embase, and Web of Science. The analysis focused on defining rurality, examining transmission mechanisms, and comparing dengue incidence between urban and rural settings. Results: The findings reveal that dengue transmission risk in rural regions often equals or exceeds that in urban areas. Variability in definitions of rurality complicates the assessment of dengue dynamics. Many rural areas exhibit characteristics traditionally linked to urban settings, such as increased human mobility and altered land use patterns, which can enhance vector breeding sites and transmission rates. Conclusion: The study underscores the necessity for enhanced surveillance and understanding of dengue transmission in rural contexts. It emphasizes that while population size is relevant, environmental and infrastructural factors significantly influence dengue risk. Effective public health interventions must address these complexities, especially as rural areas increasingly contribute to dengue epidemiology.

Distorted insights from human mobility data

AbstractThe description of human mobility is at the core of many fundamental applications ranging from urbanism and transportation to epidemics containment. Data about human movements, once scarce, is now widely available thanks to new sources such as phone call detail records, GPS devices, or Smartphone apps. Nevertheless, it is still common to rely on a single dataset by implicitly assuming that the statistical properties observed are robust regardless of data gathering and processing techniques. Here, we test this assumption on a broad scale by comparing human mobility datasets obtained from 7 different data-sources, tracing 500+ millions individuals in 145 countries. We report wide quantifiable differences in the resulting mobility networks and in the displacement distribution. These variations impact processes taking place on these networks like epidemic spreading. Our results point to the need for disclosing the data processing and, overall, to follow good practices to ensure robust and reproducible results.

Behaviour-based dependency networks between places shape urban economic resilience

AbstractDisruptions, such as closures of businesses during pandemics, not only affect businesses and amenities directly but also influence how people move, spreading the impact to other businesses and increasing the overall economic shock. However, it is unclear how much businesses depend on each other during disruptions. Leveraging human mobility data and same-day visits in five US cities, we quantify dependencies between points of interest encompassing businesses, stores and amenities. We find that dependency networks computed from human mobility exhibit significantly higher rates of long-distance connections and biases towards specific pairs of point-of-interest categories. We show that using behaviour-based dependency relationships improves the predictability of business resilience during shocks by around 40% compared with distance-based models, and that neglecting behaviour-based dependencies can lead to underestimation of the spatial cascades of disruptions. Our findings underscore the importance of measuring complex relationships in patterns of human mobility to foster urban economic resilience to shocks.

Clinical Cases of Tick-Borne Diseases in Dogs During the Autumn-Winter Season in Poland.

Tick-borne diseases (TBDs) pose a growing threat to companion animals, especially dogs, due to the increasing abundance of tick populations in Europe, driven by climate change, urbanization, and the mobility of humans and animals. This study aimed to assess the prevalence of tick-borne pathogens in clinically ill dogs suspected of having developed TBDs during the autumn-winter season, as well as to detect pathogens in ticks collected during the same period in the Warmian-Masurian Voivodeship in Poland. A total of 30 dogs with clinical symptoms of babesiosis and 45 ticks from dogs were acquired for this study. Clinical symptoms in dogs included elevated body temperature > 39.0 °C (73.3%), anemia (56.7%), thrombocytopenia (80%), and dark urine (53.3%). Co-infections with Babesia spp. were identified in two combinations (Babesia spp. and Mycoplasma spp. (n = 5), Babesia spp. and Borrelia spp. (n = 2)) and one co-infection with Anaplasma spp. and Borrelia spp., highlighting the complexity of TBD diagnosis and treatment. The analyzed tick species were Ixodes ricinus (86.7%; n = 39; 18 females and 21 males) and Dermacentor reticulatus (13.3%; n = 6; 4 females and 2 males). In I. ricinus, Babesia spp. were identified in 7.7% (3/39), Mycoplasma spp. in 7.7% (3/39), Borrelia in 25.6% (10/39), and Anaplasma spp. in 10.3% (4/39). In D.reticulatus, only two pathogens-Borrelia spp. and Anaplasma spp.-were detected, both only once (16.7%; 1/6). No significant differences were observed between the prevalence of the studied pathogens and tick species, sex, or developmental stage. This study emphasizes the year-round risk of TBDs in dogs, particularly during the autumn-winter months, and underscores the need for continuous vigilance in tick prevention, broad-spectrum diagnostics, and treatment strategies.

AI-driven universal lower-limb exoskeleton system for community ambulation.

Exoskeletons offer promising solutions for improving human mobility, but a key challenge is ensuring the controller adapts to changing walking conditions. We present an artificial intelligence (AI)-driven universal exoskeleton system that dynamically switches assistance types between walking modes, modulates assistance levels corresponding to the ground slope, and delivers assistance timely based on the current gait phase in real-time. During treadmill validation, AI-based assistance reduced metabolic cost by 6.5% compared to 3.5% for conventional assistance. We expanded testing the controller in real-world walking, where AI-based assistance showed effective modulation and higher user preference compared to conventional assistance. Leveraging the AI-based approach and a comprehensive dataset, the controller achieved superior performance in environment- and user-state estimations. This approach does not require a separate mode classifier and operates on a user-independent basis, enabling immediate deployment across diverse conditions. This study highlights the potential of AI-driven exoskeletons in facilitating human locomotion in real-world ambulation.

A Survey to Capture the Mobility Behavior of Residents in the Republic of Cyprus

In today's highly mobile world, people of all age groups constantly move between various locations, such as their homes, grocery stores, schools, and workplaces, reflecting the dynamic nature of modern society. The recent COVID-19 pandemic has underscored the profound impact of human mobility on the rapid spread of the virus within and between urban areas. The current study investigates the daily mobility behaviors of residents of the Republic of Cyprus. Through a comprehensive survey, the research documents travel patterns, commuting habits, and transportation preferences across various age groups and districts of Cyprus. With a total of 787 responses from all districts of Cyprus, data was collected through an online survey from 20/11/2023 to 20/02/2024. Data analysis includes descriptive methods and statistical modeling techniques in order to provide insights into the patterns of human mobility. Key findings include insights into travel distances, frequencies, duration and means of transportation, and timelines of mobility activities during typical workdays, as well as during weekends.

Novel travel time aware metapopulation models and multi-layer waning immunity for late-phase epidemic and endemic scenarios.

In the realm of infectious disease control, accurate modeling of the transmission dynamics is pivotal. As human mobility and commuting patterns are key components of communicable disease spread, we introduce a novel travel time aware metapopulation model. Our model aims to enhance estimations of disease transmission. By providing more reliable assessments on the efficacy of interventions, curtailing personal rights or human mobility behavior through interventions can be minimized. The proposed model is an advancement over traditional compartmental models, integrating explicit transmission on travel and commute, a factor available in agent-based models but often neglected with metapopulation models. Our approach employs a multi-edge graph ODE-based (Graph-ODE) model, which represents the intricate interplay between mobility and disease spread. This granular modeling is particularly important when assessing the dynamics in densely connected urban areas or when heterogeneous structures across entire countries have to be assessed. The given approach can be coupled with any kind of ODE-based model. In addition, we propose a novel multi-layer waning immunity model that integrates waning of different paces for protection against mild and severe courses of the disease. As this is of particular interest for late-phase epidemic or endemic scenarios, we consider the late-phase of SARS-CoV-2 in Germany. The results of this work show that accounting for resolved mobility significantly influences the pattern of outbreaks. The improved model provides a refined tool for predicting outbreak trajectories and evaluating intervention strategies in relation to mobility by allowing us to assess the transmission that result on traveling. The insights derived from this model can serve as a basis for decisions on the implementation or suspension of interventions, such as mandatory masks on public transportation. Eventually, our model contributes to maintaining mobility as a social good while reducing exuberant disease dynamics potentially driven by travel activities.

Modeling the impacts of governmental and human responses on COVID-19 spread using statistical machine learning

ABSTRACT Understanding the impacts of governmental and human responses on the pandemic control is imperative for forecasting pandemic spread under various responsive scenarios and guiding localized interventions before pharmaceutical interventions are available. This study analyzed multiple data sets, including social media, mobility, policy evaluations, and COVID-19 infection reports, to delineate the interactions between governmental and human responses and COVID-19 spread in the United States in 2020 when vaccinations were unavailable. The contributions are (1) uncovering the spatiotemporal variations in governmental and human responses during COVID-19; (2) developing a statistical machine learning algorithm that incorporates spatiotemporal dependencies and temporal lag effects to model the relationships between governmental and human responses and the pandemic spread; (3) dissecting the impacts of human responses on the pandemic across space and time. Results reveal that the determinants of COVID-19 health impacts transitioned from human mobility during the initial outbreak phase to both human mobility and stay-at-home policies during the rapid spread phase, and ultimately to the compound of human mobility, stay-at-home policies and the public awareness in the full-blown phase. These findings furnish guidance for policymakers in implementing adaptive and phased strategies.