Search Results (168)

Background/Objectives: Gait dysfunction occurs in 80% of stroke survivors. It increases the risk of falls, reduces functional independence, and thus affects the quality of life. Therefore, it is very important to restore the gait function in post-stroke survivors. The purpose of this study was to investigate the functional changes of gait biomechanics in patients with hemiplegia in the subacute stage of ischemic stroke based on spatiotemporal, kinematic, and EMG parameters. Methods: Initial biomechanical gait analyses of 31 patients and 34 controls were selected. The obtained parameters were assessed and compared within and across the study groups (post-stroke hemiparetic patients and healthy controls) to determine the pathognomonic features of the hemiplegic gait. Results: The gait function asymmetry was characterized by reciprocal changes, i.e., harmonic sequences of gait cycles. The most significant changes were in the kinematics of the knee joint and the EMG activity in the anterior tibialis, gastrocnemius, and hamstring muscles on the paretic side. The movements in the lower extremity joints ranged from a typical amplitude decrease to an almost complete lack of movement or involuntary excessive movement, as can occur in the ankle joint. The knee joint showed two distinct patterns: a slight flexion throughout the entire gait cycle and knee hyperextension during the middle stance phase. Conclusions: The gait function asymmetry is characterized by reciprocal changes (in temporal gait parameters). The most significant changes included decreased amplitude in the knee joint and decreased amplitude of EMG of all muscles under study, except for the m. quadriceps femoris. Full article

The giant panda, a rare and iconic species endemic to China, has attracted significant attention from both domestic and international researchers due to its crucial ecological role, unique cultural value, and distinct evolutionary history. While substantial progress has been made in the field of individual identification, behavior recognition remains underdeveloped, facing challenges such as the lack of dynamic temporal features and insufficient extraction of behavioral characteristics. To address these challenges, we propose the Swin-Panda model, which leverages transfer learning based on the Video Swin Transformer architecture within the mmaction2 framework. In addition, we introduce two novel modules: the Comprehensive Perception Auxiliary Module and the Spatiotemporal Shift Attention Module. These modules facilitate the extraction of local and spatiotemporal information, allowing the model to more effectively capture the behavioral and movement patterns of giant pandas. Experimental results on the PACV-8 dataset demonstrate that our model achieves an accuracy of 88.02%, outperforming several benchmark models. This approach significantly enhances behavior recognition accuracy, thereby contributing to the advancement of panda welfare and species conservation efforts. Full article

To address the problem of overlooking target movement characteristics and historical activity patterns in conventional path estimation methods, we propose a method based on the principle of multi-source spatio-temporal data fusion. It integrates optical image data with navigation and positioning data and improves the A* algorithm. While seeking the shortest path, the algorithm prioritizes points within hotspot areas to achieve accurate target path estimation. The algorithm extracts hotspot areas using spatial analysis methods such as kernel density analysis and uses them as the basis for path estimation. Through many simulation experiments, it is verified that the proposed improved the A* algorithm is more consistent with the actual path than the traditional A* algorithm. Full article

Introduction: Gait analysis is a vital tool in the assessment of human movement and has been widely used in clinical settings to identify potential abnormalities in individuals. However, there is a lack of consensus on the normative values for gait metrics in large populations. The primary objective of this study is to establish a normative database of spatiotemporal gait metrics across various age groups, contributing to a broader understanding of human gait dynamics. By doing so, we aim to enhance the clinical utility of gait analysis in diagnosing and managing health conditions. Methods: We conducted an observational case–control study involving 313 healthy participants. The MetaMotionC IMU by Mbientlab Inc., equipped with a triaxial accelerometer, gyroscope, and magnetometer, was used to capture gait data. The IMU was placed at the sternal angle of each participant to ensure optimal data capture during a 50 m walk along a flat, unobstructed pathway. Data were collected through a Bluetooth connection to a smartphone running a custom-developed application and subsequently analysed using IMUGaitPY, a specialised version of the GaitPY Python package. Results: The data showed that gait speeds decrease with ageing for males and females. The fastest gait speed is observed in the 41–50 age group at $1.35\pm 0.23\mathrm{m}/\mathrm{s}$. Males consistently exhibit faster gait speeds than females across all age groups. Step length and cadence do not have clear trends with ageing. Gait speed and step length increase consistently with height, with the tallest group (191–200 cm) walking at an average speed of $1.49\pm 0.12\mathrm{m}/\mathrm{s}$, with an average step length of $0.91\pm 0.05\mathrm{m}$. Cadence, however, decreases with increasing height, with the tallest group taking $103.52\pm 5.04\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{p}\mathrm{s}/\mathrm{m}\mathrm{i}\mathrm{n}$ on average. Conclusions: This study has established a comprehensive normative database for the spatiotemporal gait metrics of gait speed, step length, and cadence, highlighting the complexities of gait dynamics across age and sex groups and the influence of height. Our findings offer valuable reference points for clinicians to distinguish between healthy and pathological gait patterns, facilitating early detection and intervention for gait-related disorders. Moreover, this database enhances the clinical utility of gait analysis, supporting more objective diagnoses and assessments of therapeutic interventions. The normative database provides a valuable reference future research and clinical practice. It enables a more nuanced understanding of how gait evolves with age, gender, and physical stature, thus informing the development of targeted interventions to maintain mobility and prevent falls in older adults. Despite potential selection bias and the cross-sectional nature of the study, the insights gained provide a solid foundation for further longitudinal studies and diverse sampling to validate and expand upon these findings. Full article

Parkinson’s disease (PD) is characterized by a slow, short-stepping, shuffling gait pattern caused by a combination of motor control limitations due to a reduction in dopaminergic neurons. Gait disorders are indicators of global health, cognitive status, and risk of falls and increase with disease progression. Therefore, the use of quantitative information on the gait mechanisms of PD patients is a promising approach, particularly for monitoring gait disorders and potentially informing therapeutic interventions, though it is not yet a well-established tool for early diagnosis or direct assessment of disease progression. Over the years, many studies have investigated the spatiotemporal parameters that are altered in the PD gait pattern, while kinematic and kinetic gait parameters are more limited. A scoping review was performed according to the PRISMA guidelines. The Scopus and PubMed databases were searched between 1999 and 2023. A total of 29 articles were included that reported gait changes in PD patients under different gait conditions: single free walking, sequential motor task, and dual task. The main findings of our review highlighted the use of optoelectronic systems for recording kinematic parameters and force plates for measuring kinetic parameters, due to their high accuracy. Most gait analyses in PD patients have been conducted at self-selected walking speeds to capture natural movement, although studies have also examined gait under various conditions. The results of our review indicated that PD patients experience alterations in the range of motion of the hip, knee, and ankle joints, as well as a reduction in the power generated/absorbed and the extensor/flexor moments. These findings suggest that the PD gait pattern may be more effectively understood using kinematic and kinetic parameters. Full article

Crowd behavior presents significant challenges due to intricate interactions. This research proposes an approach that combines the power of 3D Convolutional Neural Networks (ConvNet) and Multi-Support Vector Machines (Multi-SVM) to study and analyze crowd behavior in highly dense crowd videos. The proposed approach effectively utilizes the temporal information captured by the 3D ConvNet, which accounts for the spatiotemporal characteristics of crowd movement. By incorporating the third dimension as a temporal stack of images forming a clip, the network can learn and comprehend the dynamics and patterns of crowd behavior over time. In addition, the learned features from the 3D ConvNet are classified and interpreted using Multi-SVM, enabling a comprehensive analysis of crowd behavior. This methodology facilitates the identification and categorization of various crowd dynamics, including merging, diverging, and dense flows. To evaluate the effectiveness of the approach, experiments are conducted on the Crowd-11 dataset, which comprises over 6000 video sequences with an average length of 100 frames per sequence. The dataset defines a total of 11 crowd motion patterns. The experimental results demonstrate promising recognition rates and achieve an accuracy of 89.8%. These findings provide valuable insights into the complex dynamics of crowd behavior, with potential applications in crowd management. Full article

Burst morphology is a crucial indicator for evaluating the effectiveness of blasting, as it directly reflects the actual state of the blasting results. The results of rock displacement following blasting partially reflect the effectiveness of throw blasting, while the rock ejection process serves as the macroscopic manifestation of the blasting method. To accurately assess the impact of different delay times on burst formation, this study addressed the issues of rock movement and ejection in underground blasting. Using three-dimensional modeling, we constructed a FEM–SPH model and utilized LS-DYNA numerical simulation software to investigate the movement patterns of rock in precise delayed blasting scenarios underground. This study explored the spatiotemporal evolution characteristics of rock movement post-blasting. Digital electronic detonators were used to set precise inter-row delay times of 25 ms, 50 ms, and 75 ms. The results revealed that the ejection distance of blasted rock in underground mining increased with longer inter-row delay times, while the slope angle of the blasted muck pile decreased as the delay time increased. Furthermore, at a micro level, the study found that a 75 ms delay created new free surfaces, providing effective compensation space for subsequent blasts, thereby improving blasting outcomes. Analysis of the 25 ms and 50 ms delay periods indicated a clamping effect on rock movement. Field comparisons of blasting results were conducted to validate the influence of precise delay times on the movement patterns and spatiotemporal evolution characteristics of blasted rock. Full article

Background and Purpose: activities-based locomotor training (AB-LT) is a restorative therapeutic approach to the treatment of movement deficits in people with non-progressive neurological conditions, including cerebral palsy (CP). Transcutaneous spinal stimulation (TSS) is an emerging tool in the rehabilitation of individuals with sensorimotor deficits caused by neurological dysfunction. This non-invasive technique delivers electrical stimulation over the spinal cord, leading to the modulation of spinal sensorimotor networks. TSS has been used in combination with AB-LT and has been shown to improve muscle activation patterns and enhance motor recovery. However, there are no published studies comparing AB-LT + TSS to AB-LT alone in children with CP. The purpose of this case study was to compare the impact of AB-LT alone versus AB-LT combined with TSS on functional movement and quality of life in a child with CP. Methods: A 13-year-old male with quadriplegic CP participated in this pilot study. He was classified in the Gross Motor Function Classification System (GMFCS) at Level III. He completed 20 sessions of AB-LT (5x/week), then a 2-week washout period, followed by 20 sessions of body-AB-LT + TSS. Treatment sessions consisted of 1 h of locomotor training with body weight support and manual facilitation and 30 min of overground play-based activities. TSS was applied using the RTI Xcite^®, with stimulation at the T11 and L1 vertebral levels. Assessments including the Gross Motor Function Measure (GMFM), 10-m walk test (10 MWT), and Pediatric Balance Scale (PBS) were performed, while spatiotemporal gait parameters were assessed using the Zeno Walkway^®. All assessments were performed at three time points: before and after AB-LT, as well as after AB-LT + TSS. OUTCOMES: After 19/20 sessions of AB-LT alone, the participant showed modest improvements in the GMFM scores (from 86.32 to 88), 10 MWT speed (from 1.05 m/s to 1.1 m/s), and PBS scores (from 40 to 42). Following the AB-LT combined with TSS, scores improved to an even greater extent compared with AB-LT alone, with the GMFM increasing to 93.7, 10 MWT speed to 1.43 m/s, and PBS to 44. The most significant gains were observed in the GMFM and 10 MWT. Additionally, improvements were noted across all spatiotemporal gait parameters, particularly at faster walking speeds. Perhaps most notably, the child transitioned from the GMFCS level III to level II by the end of the study. Discussion: Higher frequency and intensity interventions aimed at promoting neuroplasticity to improve movement quality in children with CP are emerging as a promising alternative to traditional physical therapy approaches. This case study highlights the potential of TSS to augment neuroplasticity-driven treatment approaches, leading to improvements in neuromotor function in children with CP. These findings suggest that TSS could be a valuable addition to rehabilitation strategies, warranting further research to explore its efficacy in larger populations. Full article

Frazil ice is the foundation for all other ice phenomena, and its spatiotemporal evolution is critical for regulating ice conditions in rivers and channels, as well as for preventing and controlling ice damage. This paper investigates the dynamic transport pattern of frazil ice during the early stages of winter freezing in water conveyance channels based on a CFD-DEM coupled numerical model, and derives predictive formulae for the spatiotemporal evolution of frazil ice and floating ice. First, static repose angle simulations and slope sliding simulations were used to calibrate the contact parameters between frazil ice particles and between frazil ice and the channel bed, ensuring the accurate calculation of contact forces in the model. On this basis, the processes of frazil ice transport, aggregation, and upward movement in water transfer channels were simulated, and the influence of contact parameters on simulation results was analyzed, showing a significant effect when the ice concentration was high. Numerical results indicate that the amount of suspended frazil ice is positively correlated with the frazil ice generation rate and water depth, with minimal influence from the flow velocity; the amount of floating ice increases linearly along the channel, with growth positively correlated with the frazil ice generation rate and water depth, and negatively correlated with the flow velocity. Predictive formulae correlating frazil ice and floating ice amounts with the flow velocity, water depth, and other factors were proposed based on numerical results. There is good agreement between the predictive and numerical results: the maximum APE between the predicted and simulated values of suspended frazil ice is 13.24%, and the MAPE is 6.32%; the maximum APE between the predicted and simulated values of floating ice increment is 7.80%, and the MAPE is 2.89%. The proposed prediction formulae can provide a theoretical basis for accurately predicting ice conditions during the early stages of winter freezing in rivers and channels. Full article

The reconstruction of land spatial planning and the increasing severity of carbon emissions pose significant challenges to carbon peak and carbon neutrality strategies. To establish low-carbon and sustainable agricultural spatial planning while achieving dual carbon strategy goals, it is essential to accurately analyze the mechanisms of agricultural spatial transfer and their carbon emission effects, as well as the key factors influencing carbon emissions from agricultural spatial transfer. Therefore, this study, based on land use remote sensing data from 2000 to 2020, proposes a carbon emission accounting system for agricultural space transfer. The carbon emission total from agricultural space transfer in the Harbin-Changchun urban agglomeration over the 20-year period is calculated using the carbon emission coefficient method. Additionally, the spatiotemporal patterns and influencing factors are analyzed using the standard deviation ellipse method and the geographical detector model. The results indicate that: (1) The agricultural space in the Harbin-Changchun urban agglomeration has increased, with a reduction in living space and an expansion of production space. Among land type conversions, the conversion between cultivated land and forest land has been the most intense. (2) The conversion of agricultural space to grassland and built-up land has been the primary source of net carbon emissions. The carbon emission center has shown a migration path characterized by “eastward movement and southward progression,” with a high-north to low-south distribution pattern. Significant carbon emission differences were observed at different spatial scales. (3) Natural environmental factors dominate the carbon emissions from agricultural space transfer, while socioeconomic and policy factors act as driving forces. Elevation is the primary factor influencing carbon emissions from agricultural space transfer. Interactions between factors generally exhibit nonlinear enhancement, with the interaction between elevation, annual precipitation, and industrial structure showing a strong explanatory power. Notably, the interactions between elevation, average annual precipitation, and industrial structure demonstrate significant explanatory power. These findings highlight the necessity for government action to balance agricultural spatial use with ecological protection and economic development, thereby providing scientific references for optimizing future land spatial structures and formulating regional carbon balance policies. Full article

Pneumatophore fringes and mudflats are extremely valuable habitats and provide structures on which many fish species benefit in terms of food and reduced predation risk. We analyzed the spatiotemporal patterns in feeding habits, reproductive aspects and effects of predatory fish presence to assess the ecological drivers of the common halfbeak, Hyporhamphus unifasciatus, in a Brazilian estuary. Sampling was conducted during the rainy and dry periods. In summary, the results demonstrated that the number of predatory fishes was a strong predictor of population abundance and biomass, followed by pneumatophore complexity. The abundance and biomass values tended to increase with increasing habitat structural complexity towards the upper estuary. There was evidence that fish exhibited movement during the rainy season related to spawning events and subsequent juvenile recruitment in this area. Hymnoptera was the item most frequently ingested and made the greatest contributions to the volume of diet in habitat types throughout the year. There was an increase in the condition factor in the rainy season, which was associated with energy reserves, reproduction and growth (fitness). We concluded that predation is an important ecological process that operates at local spatial scales and that, together with the density of pneumatophores, it could affect the abundance of common halfbeak populations associated with estuarine habitats. Full article

To provide new insights into the integrated management of carbon and heat for sustainable urban development, this study systematically investigates the complex relationship between atmospheric CO₂ concentrations and land surface temperature (LST). Utilizing OCO-2 and OCO-3 satellite observations, combined with meteorological conditions, air pollutants, and spatial characteristics, a high-resolution (0.1° × 0.1°) monthly CO₂ column concentration (XCO₂) dataset for China spanning 2015 to 2022 was generated using the Random Forest algorithm. The study focuses on urban agglomerations, conducting centroid migration and coupling analyses of XCO₂ and LST to elucidate their spatiotemporal distribution patterns and evolution. Results reveal significant seasonal variations in XCO₂, which has exhibited a gradual increase over the years. The spatiotemporal distributions of XCO₂ and LST in urban agglomerations show a high degree of consistency, with centroids either converging or following similar movement trajectories. Additionally, the degree of coupling and coordination between XCO₂ and LST has improved annually, indicating a closer interrelationship. These findings enhance our understanding of climate system dynamics and provide essential scientific evidence and decision-making support for addressing climate change. By clarifying the connection between atmospheric CO₂ and LST, this study contributes to the development of more effective strategies for carbon reduction and urban heat island mitigation, thereby advancing cities towards greener, lower-carbon, and more sustainable development pathways. Full article

Today, urban areas across the world are increasingly vulnerable to emergencies due to expanding populations and the impact of climate change. This paper presents a data-driven method for assessing the susceptibility of urban regions to emergencies, using publicly available data and a clustering-based algorithm. The study incorporates both spatial and temporal dynamics, capturing the fluctuating nature of urban infrastructure and patterns of human movement over time. By introducing the notion of Points of Temporal Influence (PTIs) and a new “susceptibility level” parameter, the proposed model offers an innovative approach to understanding urban susceptibility. Experiments conducted in London, the UK, demonstrated the effectiveness of the Spatiotemporal K-means Clustering algorithm in identifying areas with heightened time-sensitive susceptibility. The findings highlight the value of incorporating both spatial and temporal data to enhance emergency response strategies and optimize urban planning efforts. This study contributes to the literature on smart cities by providing a scalable and adaptable method for improving urban resilience in the face of evolving challenges. Full article

Mining spatiotemporal mobility patterns is crucial for optimizing urban planning, enhancing transportation systems, and improving public safety by providing useful insights into human movement and behavior over space and time. As an unsupervised learning technique, time series clustering has gained considerable attention due to its efficiency. However, the existing literature has often overlooked the inherent characteristics of mobility data, including high-dimensionality, noise, outliers, and time distortions. This oversight can lead to potentially large computational costs and inaccurate patterns. To address these challenges, this paper proposes a novel neural network-based method integrating temporal autoencoder and dynamic time warping-based K-means clustering algorithm to mutually promote each other for mining spatiotemporal mobility patterns. Comparative results showed that our proposed method outperformed several time series clustering techniques in accurately identifying mobility patterns on both synthetic and real-world data, which provides a reliable foundation for data-driven decision-making. Furthermore, we applied the method to monthly county-level mobility data during the COVID-19 pandemic in the U.S., revealing significant differences in mobility changes between rural and urban areas, as well as the impact of public response and health considerations on mobility patterns. Full article

Since China’s reform and opening up in 1978, the reclamation and abandonment of cropland in Xinjiang have become significant features of the land use change in the arid land of Northwest China. However, the spatiotemporal changes and driving mechanisms of cropland reclamation and abandonment over long time periods are still unclear, but this is crucial in understanding cropland changes in inland arid land, providing important insights for land management and agricultural development. Based on 40 years of remote sensing data on resources and the environment, this study examines the spatiotemporal characteristics of cropland reclamation and abandonment in Xinjiang over four periods since 1980. Additionally, it uses an optimal parameter geographical detector model to quantify the driving factors for each period. The results indicate that cropland reclamation experiences a “slow decrease–rapid increase” trend, forming a “V-shaped” pattern, while abandonment shows a “rapid decrease–slow decrease–slow increase” trend, forming a “U-shaped” pattern. These trends can be divided into three periods: 1980–1990 (unstable growth), 1990–2010 (stable growth), and 2010–2020 (growth with constraints). The movement pattern of cropland reclamation’s center of gravity is “slightly southeast–slightly northeast–southwest”, whereas the abandonment’s center of gravity shifts “northeast–southwest–northeast”. Further analysis reveals that the impact of agricultural technological investment and infrastructure on cropland reclamation has increased, while the influence of natural environmental factors has decreased. Although climate and water resources remain key factors in cropland abandonment, the influence of economic and social factors has gradually diminished, and the impact of agricultural mechanization has steadily risen. Full article