Human Trajectories Research Articles

Low-cost guide dog robot navigation using Dueling DQN

Abstract. Traditional aids for the visually impaired, such as guide dogs, have limitations. They are expensive to train, require a long preparation time, and are not accessible to most people. Guide robots have the potential to address these issues. However, most existing navigation methods focus solely on the robot's trajectory, neglecting the movement of the person. As a result, the paths of the visually impaired individual and the robot may not overlap or may intersect with obstacles.To overcome these challenges, we propose an intelligent guide dog system based on a Deep Q-network (DQN). First, a kinematic model is constructed to calculate the relative positions of the guide robot and the human. Then, the reward function is designed based on this kinematic model. Finally, the Dueling DQN is trained to plan a path and predict the human's possible trajectory, ensuring collision avoidance for both the robot and the human.Our experiments demonstrated that our system performs well in both simulated environments and in real-world scenarios, such as climbing stairs. The robot can accurately find a safe path that considers the trajectories of both the robot and the human, successfully completing tasks safely.

An Efficient Attentive-GRU Structure for Uncertainty Modelling of Crowdsourced Human Trajectories Under Building-obscured Urban Scenes

Abstract. Uncertainty modelling is regarded as one of the core components in the field of human mobility analysis and urban navigation, that can affect the performance of human behaviour modelling and location information acquisition. Existing uncertainty modelling algorithms towards the human movement trajectory are subjected to random and highly dynamic human motion characteristics and sampling and observation errors of Global Navigation Satellite System (GNSS) signals caused by the occlusion of buildings in urban scenes, which lead to the insufficient spatiotemporal correlation and poor accuracy of uncertainty modelling. To fill in this gap, this paper proposes an efficient attentive-GRU structure for uncertainty modelling of crowdsourced human trajectories under building-obscured urban scenes, that takes into account both temporal correlation and spatial correlation of human-originated GNSS trajectories and related motion features. A period of human motion data is modelled instead of only one or adjacent location points to avoid interference factors caused by the obstruction of urban buildings, and time-varying measurement and sampling errors are further estimated and combined with comprehensive human motion features to improve the accuracy of final uncertainty modelling. Comprehensive experiments indicate that compared with existing uncertainty modelling methods including physical models and deep-learning models, the proposed attentive-GRU structure realizes much better performance under different accuracy indexes.

GeoGail: A Model-based Imitation Learning Framework for Human Trajectory Synthesizing

Synthesized human trajectories are crucial for a large number of applications. Existing solutions are mainly based on the generative adversarial network (GAN), which is limited due to the lack of modeling the human decision-making process. In this paper, we propose a novel imitation learning based method to synthesize human trajectories. This model utilizes a novel semantics-based interaction mechanism between the decision-making strategy and visitations to diverse geographical locations to model them in the semantic domain in a uniform manner. To augment the modeling ability to the real-world human decision-making policy, we propose a feature extraction model to extract the internal latent factors of variation of different individuals, and then propose a novel self-attention based policy net to capture the long-term correlation of mobility and decision-making patterns. Then, to better reward users’ mobility behavior, we propose a novel multi-scale reward net combined with mutual information to model the instant reward, long-term reward, and individual characteristics in a cohesive manner. Extensive experimental results on two real-world trajectory datasets show that our proposed model can synthesize the most high-quality trajectory data compared with six state-of-the-art baselines in terms of a number of key usability metrics, and can well support practical applications based on trajectory data, demonstrating its effectiveness. Furthermore, our proposed method can learn explainable knowledge automatically from data, including explainable statistical features of trajectories and statistical relation between decision-making policy and features.

Statistical Law between Areas and Perimeters Created by a Moving Trajectory

Based on our interest in properties of human movement, we investigated Japanese GPS data, and arrived at the following three observations: (1) there is a strong correlation between the area of polygons created by human movement trajectories and their perimeters; (2) short-distance movement trajectories less than 5 km tend to enclose a large area like a circle; and (3) long-distance movement trajectories over 5 km tend to be straight. We also clarified the following four observations on individual attributes and external factors related to long-distance movements: (1) women tend to travel more linearly than men; (2) linearity is stronger on weekends and national holidays in areas with a large theme park; (3) linearity is weaker on weekends and holidays in areas with many historical tourist attractions; and (4) these variations are created by people visiting such areas. These properties should be incorporated when modeling the movement trajectories of humans.

The timing and mode of southern Andean human migrations

Abstract While recent genomic and isotopic information show that migration has been pervasive along human history, southern Andean archaeology has largely overlooked its importance in shaping human trajectories of sociocultural change. Building on previous isotopic research that identified the presence of migrant farmers in the Uspallata Valley (Mendoza, Argentina), we present chronological and bioarchaeological results that help to characterize the timing and mode of human migrations in the southern Andes. The burials with migrants show the representation of the different age classes, including a high abundance of children, as well as both men and women, suggesting that family groups were likely involved. The Bayesian modeling of 16 direct dates for migrants indicates that these migrations started between 1210–1275 CE (median 1255 CE) and finished at 1320–1425 CE (median 1360 CE), indicating that there is nearly no overlap between the commencement of this migration phase and the southwards expansion of the Inka Empire. The model defines a diachronic process that lasted between 55 and 195 years, implying that migration to Uspallata was a multi-generational process that involved between two and eight generations (median of four generations). Our contextual, bioarchaeological and chronological evidence indicates that the conditions fostering migration to Uspallata were sustained through time, inviting to explore persisting push-pull dynamics acting during this period. 87Sr/86Sr results show that migration occurred across the daily territories of these groups and may have involved movement across social or ethnic frontiers.

Regional patterns of human cortex development correlate with underlying neurobiology

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of the variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

Analysis of the watershed social-ecological system trajectory in Copalita-Huatulco, Mexico: The impact of drivers on hydrological ecosystem services.

In watersheds, which function as social-ecological systems (SESs), hydrological ecosystem services (HESs) are crucial flows connecting natural and social components. The internal and external drivers that shape HESs and watershed SES trajectories create complexities in watershed management. In this study, the adaptive cycle framework was adopted, and a qualitative method is proposed to analyze the impacts of drivers on the HES and the watershed SES trajectory. Empirical evidence from the Copalita-Huatulco watershed in Mexico was used to study the relationships between drivers and HESs. The results reveal changes in regional hydrodynamics due to natural shocks, cumulative social stressors, and SES-related human trajectories. The SES trajectory was in a growth phase when stressors dominated, a collapse when shocks occurred, and a conservation phase when human agency was strong. Understanding the impact of drivers on HESs and their integration into SES trajectories is a key element of HES resilience.

Enhanced Crime Prediction with Computer Vision-Yolov4 Approach

This research paper presents the development of an artificial intelligence safety application on an HP Pavilion gaming machine, utilizing criminal footage from reputable databases like the UCF-Crime open-source dataset. The system underwent meticulous data annotation to identify five distinct classes crucial for anomaly detection: Person, Short Gun, Handgun, Knife, and Rifle. Supervised machine learning techniques were applied, focusing on monitoring human trajectories and employing deep-SORT and Euclidean distance computations to track individuals, simulating real-world crime scenarios. The AI safety model showcased outstanding performance with an average precision rate of approximately 86.43%, exceeding 90% after 2000 iterations, demonstrating versatility across all categories with notable average precision accuracies for rifles (98.90%), handguns (96.93%), and knives (97.66%). Enhancements to the Python script improved the system's ability to detect weapons sub-objects in human subjects and classify potential perpetrators as high risk, a novel aspect of this study.

Rethinking convolutional neural networks for trajectory refinement

In this work, we revisit CNN architectures for sequence modeling, focusing on human trajectory prediction tasks. Forecasting human trajectories has been extensively explored as a sequence modeling problem, with many studies utilizing CNNs. Unlike conventional approaches that apply 1D convolution or 2D convolution over heatmap representations, we propose a novel architecture that applies 2D convolution directly over raw trajectory coordinates. Our method employs a coarse-to-fine strategy to refine trajectory predictions. We evaluated our approach on the ETH/UCY and Stanford Drone Datasets, demonstrating significant improvements. Our method sets a new state-of-the-art on the Stanford Drone Dataset, improving prediction accuracy and outperforming existing methods.

Online dual robot–human collaboration trajectory generation by convex optimization

For dynamic collision-free trajectory planning in dual-robot and human collaborative tasks, this paper develops an online dual-robot Mutual Collision Avoidance (MCA) scheme based on convex optimization. A novel convex optimization formulation model, named Disciplined Convex programming by Shifting reference paths (DCS), is proposed for solving the single-robot trajectory optimization problem. Furthermore, a new dual-robot trajectory convex optimization algorithm is presented for online adjustment of the dual-robot trajectories according to the collaborative task priority. The overall pipeline, named DCS-MCA, generates collision-free and time-optimal dual-robot trajectories, while prioritizing the task accessibility of the high-priority robot. Simulation experiments demonstrate that DCS exhibits comparable performance to the current state-of-the-art single-robot motion planner, while the DCS-MCA outperforms common algorithms by up to 30% in time optimality for dual-robot collaborative tasks. The feasibility and dynamic performance of the proposed approach are further validated in a real collaborative cell, illustrating its suitability for collaborative dual-robot tasks in moderately dynamic environments.

Regional patterns of human cortex development correlate with underlying neurobiology.

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cortical thickness development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical thickness change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

Isotopic distribution of bioavailable Sr, Nd, and Pb in Chungcheongbuk-do Province, Korea

BackgroundMapping the distribution of bioavailable isotope ratios across landscapes serves as an efficient geochemical tool for delineating the origins and migration trajectories of humans and animals. Chungcheongbuk-do Province in central Korea, known for its geological diversity and inland location isolated from coastal influences, provides an ideal area to study the contributions of geological and environmental factors to the isotope landscape (isoscape). This study analyzed the distribution of bioavailable Sr, Nd, and Pb isotopes in the province using plant and soil data obtained in this study and from previous works.FindingsChungcheongbuk-do features diverse geological elements, including Precambrian basement, Paleozoic metasedimentary rocks, and Mesozoic granitoids and volcano-sedimentary sequences. The 87Sr/86Sr ratios of bulk soil samples from 44 sites primarily range from 0.781 to 0.706, with two ratios exceeding 0.9 originating from Precambrian basement and Cretaceous granitoid areas. Fractions of soils treated with 1 M ammonium nitrate and acetic acid exhibit indistinguishable 87Sr/86Sr ratios (R2 = 0.99, except for one point), spanning from 0.804 to 0.707. Plant 87Sr/86Sr ratios demonstrate a robust positive correlation with leachate ratios (for ammonium nitrate data, (87Sr/86Sr)plant = 0.938 × (87Sr/86Sr)leachate + 0.045, R2 = 0.98). The εNd values of bulk soils from Precambrian basement areas (–18 to –30) plot against Sm/Nd ratios around the reference line corresponding to 3 Ga, while other bulk soil samples (εNd = –1 to –21) align with a younger (~ 2 Ga) reference line. Plant εNd values, ranging from –4 to –24, exhibit a prominent positive correlation with ammonium nitrate leachates (plant εNd = 0.77 × leachate εNd – 3.83, R2 = 0.89). Plant samples do not show consistent variation between 87Sr/86Sr and εNd. The 206Pb/204Pb and 207Pb/204Pb ratios of bulk soils show a ~ 2 Ga trend, typical for Korean basement rocks. The Pb isotopic ratios of ammonium nitrate and acetic acid leachates match perfectly with each other (R2 = 0.99). The 206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb ratios of plant samples vary narrowly (19.2–17.9; 15.8–15.5; 39.0–38.0) and are distinctly different from those of bulk soils (24.4–17.9; 16.5–15.6; 42.5–37.9) and their leachates (23.1–17.7; 16.2–15.6; 41.0–38.1). Plant and soil data from this study and previous works were used to construct Sr and Nd isoscapes, employing interpolation models based on inverse distance weighting, simple kriging, empirical Bayesian kriging, and geology and topography-considered empirical Bayesian kriging regression prediction. These maps await validation through analysis of additional archives.ConclusionsThe isotope data obtained in this study highlight a strong geological control over bioavailable Sr and Nd, in contrast to a dominant environmental influence on bioavailable Pb. The Sr and Nd isoscapes presented here are potentially valuable for addressing archaeological or forensic inquiries in their current state. Nevertheless, the maps would benefit from additional refinement with increased sample density and enhanced interpolation models.

Learning Semantic Behavior for Human Mobility Trajectory Recovery

Learning Semantic Behavior for Human Mobility Trajectory Recovery

Inducible pluripotent stem cells to study human mast cell trajectories

Mast cells (MCs) are derived from CD34+ hematopoietic progenitors, consist of different subtypes, and are involved in several inflammatory conditions. However, our understanding of human MC developmental trajectories and subtypes has been limited by a scarcity of suitable cellular model systems. Herein, we developed an in vitro model of human MC differentiation from induced pluripotent stem cells (iPSC) to study human MC differentiation trajectories. Flow cytometry characterization of hemopoietic cells derived from the myeloid cells-forming complex (MCFC) revealed an initial increase in Lin- CD34+ hematopoietic progenitors within Weeks 1–3, followed by an increase in CD34- CD45RA- SSClow and SSChigh hematopoietic cells. The Lin- CD34+ hematopoietic progenitors consisted of SSClow CD45RA- CD123± c-Kit+ FcεRI+ populations that were β7-integrinhigh CD203c+ and β7-integrinhigh CD203c- cells consistent with CMPFcεRI+ cells. Flow cytometry and cytologic analyses of the CD34- Lin- (SSClow) population revealed hypogranular cell populations, predominantly characterized by CD45RA- CD123± c-Kit+ FcεRI- β7-integrinlow and CD45RA- CD123± c-Kit- FcεRI+ β7-integrinMid cells. Analyses of hypergranular SSChigh cells identified Lin- CD34- CD45RA- c-Kit+ FcεRI- and Lin- CD34- CD45RA- c-Kit+ FcεRI+ cells. scRNA-seq analysis of the cells harvested at week 4 of the MCFC culture revealed the presence of monocyte and granulocyte progenitors (n = 547 cells, 26.7 %), Erythrocyte / unknown (n = 85, 4.1 %), neutrophils / myelocytes (n = 211 cells, 10.2 %), mast cell progenitor 1 (n = 599, 29.1 %), mast cell progenitor 2 (n = 152, 7.4 %), committed mast cell precursor (n = 113, 5.5 %), and MCs (n = 353, 17.1 %). In silico analyses of the MC precursor and mature MC populations revealed transcriptionally distinct MC precursor subtype and mature MC states (CMA1+ and CMA1- subtypes). Culturing MC precursor populations in MC maturation media (mast cell media II) led to homogenous mature MC populations as evidenced by high expression of high-affinity IgE receptor, metachromatic granules, presence of MC granule proteins (Tryptase and Chymase) and activation following substance P stimulation and FcεRI crosslinking. This human iPSC-based approach generates MC precursors and phenotypically mature and functional MC populations. This system will be a useful model to generate human MC populations and broaden our understanding of MC biology and transcriptional regulation of MC differentiation trajectories.

Cloud IoT-Oriented Secure College Physical Education Teaching Platform Vased on Deep Learning

In physical education (PE) teaching, the teaching platform is comprehensively applied to provide students with high-quality PE teaching resources to meet their learning needs in different forms. A human motion trajectory detection method is proposed based on deep learning and superpixels. The initial positioning of human body is performed through the attention mechanism and the YOLOv5 model, and the human target tracking is performed through superpixels. Aiming at the difficulty of cross-domain security management due to the lack of security infrastructure in multi-domain cloud IoT, a lightweight certificateless cross-domain authentication scheme is designed. Simulation results show that the proposed detection method has significantly improved accuracy and running time compared with traditional methods and can adapt to different detection scenarios. Furthermore, the proposed certificateless cross-domain authentication scheme can securely access sensor-generated data under the cloud IoT of the PE teaching platform in colleges.

Content-Aware Human Mobility Pattern Extraction.

Extracting meaningful patterns of human mobility from accumulating trajectories is essential for understanding human behavior. However, previous works identify human mobility patterns based on the spatial co-occurrence of trajectories, which ignores the effect of activity content, leaving challenges in effectively extracting and understanding patterns. To bridge this gap, this study incorporates the activity content of trajectories to extract human mobility patterns, and proposes acontent-aware mobility pattern model. The model first embeds the activity content in distributed continuous vector space by taking point-of-interest as an agent and then extracts representative and interpretable mobility patterns from human trajectory sets using a derived topic model. To investigate the performance of the proposed model, several evaluation metrics are developed, including pattern coherence, pattern similarity, and manual scoring. A real-world case study is conducted, and its experimental results show that the proposed model improves interpretability and helps to understand mobility patterns. This study provides not only a novel solution and several evaluation metrics for human mobility patterns but also a method reference for fusing content semantics of human activities for trajectory analysis and mining.

In Silico Human Mobility Data Science: Leveraging Massive Simulated Mobility Data (Vision Paper)

Human mobility data science using trajectories or check-ins of individuals has many applications. Recently, we have seen a plethora of research efforts that tackle these applications. However, research progress in this field is limited by a lack of large and representative datasets. The largest and most commonly used dataset of individual human trajectories captures fewer than 200 individuals, while datasets of individual human check-ins capture fewer than 100 check-ins per city per day. Thus, it is not clear if findings from the human mobility data science community would generalize to large populations. Since obtaining massive, representative, and individual-level human mobility data is hard to come by due to privacy considerations, the vision of this work is to embrace the use of data generated by large-scale socially realistic microsimulations. Informed by both real data and leveraging social and behavioral theories, massive spatially explicit microsimulations may allow us to simulate entire megacities at the person level. The simulated worlds, which do not capture any identifiable personal information, allow us to perform “in silico” experiments using the simulated world as a sandbox in which we have perfect information and perfect control without jeopardizing the privacy of any actual individual. In silico experiments have become commonplace in other scientific domains such as chemistry and biology, permitting experiments that foster the understanding of concepts without any harm to individuals. This work describes challenges and opportunities for leveraging massive and realistic simulated alternate worlds for in silico human mobility data science.

Evidence that the cold- and menthol-sensing functions of the human TRPM8 channel evolved separately.

Transient receptor potential melastatin 8 (TRPM8) is a temperature- and menthol-sensitive ion channel that contributes to diverse physiological roles, including cold sensing and pain perception. Clinical trials targeting TRPM8 have faced repeated setbacks predominantly due to the knowledge gap in unraveling the molecular underpinnings governing polymodal activation. A better understanding of the molecular foundations between the TRPM8 activation modes may aid the development of mode-specific, thermal-neutral therapies. Ancestral sequence reconstruction was used to explore the origins of TRPM8 activation modes. By resurrecting key TRPM8 nodes along the human evolutionary trajectory, we gained valuable insights into the trafficking, stability, and function of these ancestral forms. Notably, this approach unveiled the differential emergence of cold and menthol sensitivity over evolutionary time, providing a fresh perspective on complex polymodal behavior. These studies provide a paradigm for understanding polymodal behavior in TRPM8 and other proteins with the potential to enhance our understanding of sensory receptor biology and pave the way for innovative therapeutic interventions.

Identification of a Person in a Trajectory Based on Wearable Sensor Data Analysis.

Human trajectories can be tracked by the internal processing of a camera as an edge device. This work aims to match peoples' trajectories obtained from cameras to sensor data such as acceleration and angular velocity, obtained from wearable devices. Since human trajectory and sensor data differ in modality, the matching method is not straightforward. Furthermore, complete trajectory information is unavailable; it is difficult to determine which fragments belong to whom. To solve this problem, we newly proposed the SyncScore model to find the similarity between a unit period trajectory and the corresponding sensor data. We also propose a Likelihood Fusion algorithm that systematically updates the similarity data and integrates it over time while keeping other trajectories in mind. We confirmed that the proposed method can match human trajectories and sensor data with an accuracy, a sensitivity, and an F1 of 0.725. Our models achieved decent results on the UEA dataset.

Examining behavioral settings and affordative space for the case of autism spectrum conditions in embodied cognition

ABSTRACT The aim of the paper is to deepen into a theoretical account on the role of behavioral settings and relational affordative space and how this perspective can be used to reconceptualize various human cognitive developmental trajectories and conditions. In the course of this theoretical work, we want to contribute in rethinking developmental trajectories based on the role of behavioral settings and relational affordances from an embodied perspective. Behavioral settings emerge from joint actions in conjunction with the affordances available. These affordances will have multiple levels and kinds of meanings for the organism which is why we need to distinguish between the general affordances for a form of life and the particular affordances for particular agents that are generated through sensorimotor interactions. We propose to apply this theoretical frame in different developmental conditions, taking as a leading example the Autistic Spectrum, extending it to other lived experiences of other groups marginalized by “normative behavioral settings” – and the ensuing ethical concerns.