Search Results (8)

With the growing awareness of public health, urban parks have increasingly become popular venues for physical activities due to their accessibility and pleasant landscapes, among which walking, jogging, and running dominate. This study innovatively integrates exercise trajectory data from the Strava platform and semantic segmentation technology to analyze the interaction mechanisms among park view elements, physical activities, and physiological responses, based on empirical data from three representative parks in Shanghai. This study includes the following: (1) acquiring hotspot exercise paths and physiological data (heart rate and speed) of walking, jogging, and running users through the open Strava platform; (2) conducting semantic segmentation on real-word photos of three case parks to extract 17 types of park elements; (3) applying Spearman’s correlation analysis to reveal the differential impacts of park elements on physiological responses under walking, jogging, and running behaviors, demonstrating that combinations of elements such as trees, water bodies, fences, and sky influence exercise performance; and (4) constructing scene modules for site attraction, training improvement, and restorative relaxation for walking, jogging, and running, and proposing phased landscape scene sequence strategies to provide quantitative guidance for health-oriented park planning and design. This study breaks through the limitations of traditional subjective evaluations by coupling objective physiological data with spatial elements, offering novel insights for optimizing the exercise functionality of urban green spaces. Full article

Outdoor jogging plays a critical role in active mobility and transport-related physical activity (TPA), contributing to both urban health and sustainability. While existing studies have primarily focused on jogging participation volumes through survey data, they often overlook the real-time dynamics that shape jogging experiences. This study seeks to provide a data-driven analysis of both jogging volume and speed, exploring how environmental factors influence these behaviors. Utilizing a dataset of over 1000 crowd-sourced jogging trajectories in Shenzhen, we spatially linked these trajectories to road-section-level units to map the distribution of jogging volume and average speed. By depicting a bivariate map of both behavioral characteristics, we identified spatial patterns in jogging behavior, elucidating variations in the distribution of volume and speed. A random forest regression model was validated and employed to capture nonlinear relationships and assess the differential impacts of various environmental factors on jogging volume and speed. The results reveal distinct jogging patterns across the city, where jogging volume is shaped by the mixed interplay of natural, visual, and built environment factors, while jogging speed is primarily influenced by visual factors. Additionally, the analysis highlights nonlinear effects, particularly identifying a threshold beyond which incremental environmental improvements provide diminishing returns in jogging speed. These findings clarify the distinct roles of environmental factors in influencing jogging volume and speed, offering insights into the dynamics of active mobility. Ultimately, this study provides data-informed implications for urban planners seeking to create environments that support TPA and promote active lifestyles. Full article

In the post-pandemic era, outdoor jogging has become an increasingly popular form of exercise due to the growing emphasis on health. It is essential to comprehensively analyze the factors influencing the spatial distribution of outdoor jogging activities and to propose planning strategies with practical guidance. Using multi-source geospatial big data and multiple models, this study constructs a comprehensive analytical framework to examine the association between environmental variables and the frequency of outdoor jogging activities in Guangzhou. Firstly, outdoor jogging trajectory data were collected from a fitness app, and potential influencing factors were selected based on multi-source big data from the perspectives of the built environment, street perception, and natural environment. For example, using the street-view imagery, objective environmental elements such as greenery and subjective elements such as safety perception were extracted from a human-centric perspective. Secondly, the framework included three models: a backward stepwise regression, an optimal parameters-based geographical detector, and a geographically weighted regression (GWR) model. These models served, to screen significant variables, identify the synergistic effects among the variables, and quantify the spatial heterogeneity of the effects, respectively. Finally, the study area was clustered based on the results of the GWR model to propose urban planning strategies with clear spatial positions and practical significance. The results indicated the following: (1) Factors related to the built environment and street perception significantly influence jogging frequency distribution. (2) Public sports facilities, the level of greenery, and safety perception were identified as key factors influencing jogging activities, representing the three aspects of service facilities, objective perception, and subjective perception, respectively. (3) Specifically, the influence of each factor on jogging activities displayed significant spatial variation. For instance, sports facilities and greenery level were positively correlated with jogging frequency in the city center. (4) Lastly, the study area was divided into four clusters, each representing different local associative characteristics between variables and jogging activities. The zonal planning recommendations have significant implications for urban planners and policymakers aiming to create jogging-friendly environments. Full article

The design of safe and efficient pedestrian facilities necessitates the knowledge of complex human movements, such as intersecting pedestrian streams, under different conditions. This study aims to experimentally investigate the impact of intersecting angles on collective crowd dynamics under two different urgency levels. Data were collected from a controlled laboratory experiment with scenarios consisting of three intersection angles (30°, 90°, and 150°) and two desired speed levels (normal walking and slow running). Trajectory data of individual experiment participants were extracted from the recorded video footage. The results indicate that the 30° intersection has the lowest bottleneck effect compared to the other angles. Moreover, the time-to-target analysis shows that the 150° intersection has a higher waiting time at the intersection compared to the other angles for the jogging scenarios. The speed distribution and space utilization maps implied an asymmetrical reduction in speed in the two corridors of the intersection, even though the physical and geometrical configurations are symmetric. The lane-based analysis of collective speeds revealed that the inner lane (the lane that initially encounters the intersecting flow) has the maximum reduction in speed. The outcomes of this study may be useful to evaluate the congestion effects associated with crossing configurations and in calibrating and validating simulation tools to reproduce such effects accurately. Full article

In recent years, the impact of the urban environment on residents’ physical activity (PA) has received extensive attention, but whether this impact has differences in the jogging preferences of residents in different footpath environments and different genders requires further research. Therefore, based on jogging trajectory data, this paper uses the grouping multiple linear regression model to study the different influencing factors of different footpath environments on the jogging of residents of different genders. The results show that (1) jogging activities (JA) were mainly concentrated in the community footpath environment, and its peak was reached at night; (2) the rise and fall of elements in built environments, social environments, and natural environments significantly affected the relative jogging distance of residents; (3) Residential land density (RLD) has a positive impact on the JA of community and green land footpaths and has a negative impact on the JA of urban footpaths. However, arterial road density (ARD) and bus distance density (BDD) have opposite significant effects on the JA of communities and green land footpaths; (4) ARD has the significant opposite effect on the JA for residents of different genders on urban footpaths and community footpaths. Facilities diversity (FD), population density (PD), and bus stop density (BSD) also had significant opposite effects on the JA of residents of different genders on green land footpaths. In general, we put forward a method theory to identify the footpath environment and provide references for improving the layout and construction of different gender residents for different footpath environment elements. Full article

Most collaborative robots (cobots) can be taught by hand guiding: essentially, by manually jogging the robot, an operator teaches some configurations to be employed as via points. Based on those via points, Cartesian end-effector trajectories such as straight lines, circular arcs or splines are then constructed. Such methods can, in principle, be employed for cart-mounted cobots (i.e., when the jogging involves one or two linear axes, besides the cobot axes). However, in some applications, the sole imposition of via points in Cartesian space is not sufficient. On the contrary, albeit the overall system is redundant, (i) the via points must be reached at the taught joint configurations, and (ii) the undesirable singularity (and near-singularity) conditions must be avoided. The naive approach, consisting of setting the cart trajectory beforehand (for instance, by imposing a linear-in-time motion law that crosses the taught cart configurations), satisfies the first need, but does not guarantee the satisfaction of the second. Here, we propose an approach consisting of (i) a novel strategy for decoupling the planning of the cart trajectory and that of the robot joints, and (ii) a novel variational technique for computing the former in a singularity-aware fashion, ensuring the avoidance of a class of workspace singularity and near-singularity configurations. Full article

The purpose of this study was to compare the inter-limb joint kinematics, joint moments, muscle forces, and joint reaction forces in patients after an Achilles tendon rupture (ATR) via subject-specific musculoskeletal modeling. Six patients recovering from a surgically repaired unilateral ATR were included in this study. The bilateral Achilles tendon (AT) lengths were evaluated using ultrasound imaging. The three-dimensional marker trajectories, ground reaction forces, and surface electromyography (sEMG) were collected on both sides during self-selected speed during walking, jogging and running. Subject-specific musculoskeletal models were developed to compute joint kinematics, joint moments, muscle forces and joint reaction forces. AT lengths were significantly longer in the involved side. The side-to-side triceps surae muscle strength deficits were combined with decreased plantarflexion angles and moments in the injured leg during walking, jogging and running. However, the increased knee extensor femur muscle forces were associated with greater knee extension degrees and moments in the involved limb during all tasks. Greater knee joint moments and joint reaction forces versus decreased ankle joint moments and joint reaction forces in the involved side indicate elevated knee joint loads compared with reduced ankle joint loads that are present during normal activities after an ATR. In the frontal plane, increased subtalar eversion angles and eversion moments in the involved side were demonstrated only during jogging and running, which were regarded as an indicator for greater medial knee joint loading. It seems after an ATR, the elongated AT accompanied by decreased plantarflexion degrees and calf muscle strength deficits indicates ankle joint function impairment in the injured leg. In addition, increased knee extensor muscle strength and knee joint loads may be a possible compensatory mechanism for decreased ankle function. These data suggest patients after an ATR may suffer from increased knee overuse injury risk. Full article

The evaluation of trajectory reconstruction of the human body obtained by foot-mounted Inertial Pedestrian Dead-Reckoning (IPDR) methods has usually been carried out in controlled environments, with very few participants and limited to walking. In this study, a pipeline for trajectory reconstruction using a foot-mounted IPDR system is proposed and evaluated in two large datasets containing activities that involve walking, jogging, and running, as well as movements such as side and backward strides, sitting, and standing. First, stride segmentation is addressed using a multi-subsequence Dynamic Time Warping method. Then, detection of Toe-Off and Mid-Stance is performed by using two new algorithms. Finally, stride length and orientation estimation are performed using a Zero Velocity Update algorithm empowered by a complementary Kalman filter. As a result, the Toe-Off detection algorithm reached an F-score between 90% and 100% for activities that do not involve stopping, and between 71% and 78% otherwise. Resulting return position errors were in the range of 0.5% to 8.8% for non-stopping activities and 8.8% to 27.4% otherwise. The proposed pipeline is able to reconstruct indoor trajectories of people performing activities that involve walking, jogging, running, side and backward walking, sitting, and standing. Full article