Search Results (273)

In the context of promoting sustainable mobility, walking and cycling have been widely recognized for their environmental and health benefits. However, a notable gap often exists between residents’ motivation to engage in these modes and their actual behavior. This study focuses on this motivation–behavior discrepancy and explores how heterogeneous information interventions—within the constraints of the existing built environment—can effectively influence residents’ travel psychology and behavior. Drawing on Protection Motivation Theory, this study aims to uncover the psychological mechanisms behind travel-mode choices and quantify the relative impacts of different types of information interventions. A travel survey was conducted in Yangzhou, China, collecting data from 1052 residents. Cluster analysis was performed using travel psychology data to categorize travel motivations and examine their alignment with actual travel behavior. A random forest model was then employed to assess the effects of individual attributes, travel characteristics, and information intervention attributes on the choice of walking and cycling. The results reveal a significant motivation–behavior gap: while 76% of surveyed residents expressed motivation to walk or cycle, only 30% actually adopted these modes. Based on this, further research shows that informational attributes exhibit a stronger effect in terms of promoting walking and cycling behavior compared to individual attributes and travel characteristics. Among these, health-related information demonstrates the maximum efficacy in areas with well-developed infrastructure. Specifically, health-related information has a greater impact on cycling (21.4%), while environmental information exerts a stronger influence on walking (7.31%). These findings suggest that leveraging information to promote walking and cycling should be more targeted. Full article

This study employs Hannah Arendt’s (1958) the human condition as a philosophical framework to examine walking not merely as a physical activity but as a meaningful form of environmental consciousness. Homo faber, which denotes tool making, corresponds to the nature-based railway regeneration exemplified by the Gyeongui Line Forest Park in Seoul City, South Korea. By applying walking as a method, bifurcated themes are explored: a pedestrian-provision focus on walkability and an environmentally oriented focus consisting of nature and culture, supporting the notion that environmental elements are co-experienced through the embodied activity of walking. Thematic findings are supported by generalized additive models, grounded in a between-method triangulation attempt. The results confirm the interdependencies among the park’s environment, pedestrian satisfaction, and environmental consciousness. Specifically, the environment surrounding the park, which traverses natural and cultural elements, is strongly associated with both pedestrian satisfaction and environmental sensitivity. The research reifies walking as a fundamental human condition, encompassing labor, work, and action, while arguing for heuristic reciprocity between homo faber and nature, as well as framing walking as a sustainably meaningful urban intervention. This study contributes to maturing the theoretical understanding of walking as a vital human condition and suggests practical insights for pedestrian-centered spatial transformation. Full article

Background/Objectives: The main objective of the present study is to assess the short-term effects of Forest Bathing (FB) conducted in a Mediterranean forest on individuals with fibromyalgia (FM) and/or chronic fatigue syndrome/myalgia encephalomyelitis (CFS/ME) on perceived pain, fatigue, state anxiety, positive and negative affect, mood states, and state mindfulness. Methods: A total of 44 participants with FM and/or CSF/ME agreed to participate in this study. The FB session consisted of a 3 km silent walk, lasting three hours and guided by a specialized psychologist and a mountain guide to guarantee the safety of the activity. Paired-sample t-tests were used to analyze the pre–post changes in perceived pain, fatigue, state anxiety, positive and negative affect, mood states, and mindfulness. Results: All reported variables but self-reported pain showed statistically significant pre–post variations after the FB session. Particularly, large-to-very-large improvements in positive and negative affect, state anxiety, tension, depression, anger, and vigor were found. Small-to-moderate effect sizes for fatigue, friendliness, and state mindfulness were also reported. Conclusions: This study provides preliminary evidence of the short-term benefits of FB in individuals with FM and/or CFS/ME, especially on state anxiety and negative affect. Full article

Since its introduction into the USA, the spotted lanternfly (SLF), Lycorma delicatula, (White) (Hemiptera: Fulgoridae) has spread across the landscape relatively unchecked. With a wide host range, it is considered a serious pest of native forest species, as well as agricultural crops. Circle traps placed on Ailanthus altissima (Miller) Swingle (Sapindales: Simaroubaceae) are passive traps collecting SLF as they walk up and down the tree trunk. These traps are successful at detecting new populations of SLF, but this can be challenging to implement at a large scale due to costs and host availability. To improve and facilitate SLF trapping practices, we investigated three key trapping components: improved collection containers, placement on alternative hosts, and lure (methyl salicylate) impact. In initial trials comparing collection jars to removable plastic bags, the adult SLF catch was four times higher using the bag design. In a multi-state survey at varying population densities, the bag traps were comparable to the jar traps but were significantly more effective than BugBarrier^® tree bands, especially during the adult stage. Catch and detection in circle traps placed on alternative hosts, Acer spp. L. (Sapindales: Sapindalaceae) and Juglans nigra L. (Fagales: Juglandaceae), were comparable to those placed on the preferred host A. altissima, especially in the earlier life stages. Additionally, detection rates of methyl salicylate-baited traps on all three hosts were comparable to those on non-baited traps. These results suggest that circle traps fitted with bags provide higher trap catch and an improvement in sample quality. In addition, circle traps were equally effective when placed on maple and black walnut, while methyl salicylate lures do not enhance trap catch or detection. Full article

Motor deficits, including atypical gait, are common in individuals with autism spectrum disorder (ASD), although the precise nature and cause of this co-occurrence is unclear. Because walking is a natural activity and gait timing is a metric that is relatively accessible to measurement, we explored whether autistic gait could be described solely in terms of the timing of gait parameters. The aim was to establish whether temporal analysis, including machine learning models, could be used as a group classifier between ASD and typically developing (TD) individuals. Thus, we performed a high-resolution temporal analysis of gait on two age-matched groups of male participants: one group with high-functioning ASD and a comparison TD group (each N = 16, age range 7 to 35 years). The primary data were collected using a VICON^® 3D motion analysis system. Significant increased temporal variability of all gait parameters tested was observed for the ASD group compared to the TD group (p < 0.001). Further machine learning analysis showed that the temporal variability of gait could be used as a group classifier for ASD. Of the twelve models tested, the best-fitting model type was random forest. The temporal analysis of gait with machine learning algorithms may be useful as a future ASD diagnostic aid. Full article

The use of gait analysis to differentiate among paediatric populations with neurological and developmental conditions such as idiopathic toe walking (ITW), cerebral palsy (CP), and hereditary spastic paraplegia (HSP) remains challenging due to the insufficient precision of current diagnostic approaches, leading in some cases to misdiagnosis. Existing methods often isolate the analysis of gait variables, overlooking the whole complexity of biomechanical patterns and variations in motor control strategies. While previous studies have explored the use of statistical physics principles for the analysis of impaired gait patterns, gaps remain in integrating both kinematic and kinetic information or benchmarking these approaches against Deep Learning models. This study evaluates the robustness of statistical physics metrics in differentiating between normal and abnormal gait patterns and quantifies how the data source affects model performance. The analysis was conducted using gait data sets from two research institutions in Madrid and Dublin, with a total of 81 children with ITW, 300 with CP, 20 with HSP, and 127 typically developing children as controls. From each kinematic and kinetic time series, Shannon’s entropy, permutation entropy, weighted permutation entropy, and time irreversibility metrics were derived and used with Random Forest models. The classification accuracy of these features was compared to a ResNet Deep Learning model. Further analyses explored the effects of inter-laboratory comparisons and the spatiotemporal resolution of time series on classification performance and evaluated the impact of age and walking speed with linear mixed models. The results revealed that statistical physics metrics were able to differentiate among impaired gait patterns, achieving classification scores comparable to ResNet. The effects of walking speed and age on gait predictability and temporal organisation were observed as disease-specific patterns. However, performance differences across laboratories limit the generalisation of the trained models. These findings highlight the value of statistical physics metrics in the classification of children with different toe walking conditions and point towards the need of multimetric integration to improve diagnostic accuracy and gain a more comprehensive understanding of gait disorders. Full article

In recent years, research on human activity recognition (HAR) on smartphones has received extensive attention due to its portability. However, the discrimination issues between similar activities such as leaning forward and walking forward, as well as going up and down stairs, are hard to deal with. This paper conducts HAR based on the sensors of smartphones, i.e., accelerometers and gyroscopes. First, a feature extraction method for sensor data from both the time domain and frequency domain is designed to obtain more than 300 features, aiming to enhance the accuracy and stability of recognition. Then, the LightGBM (version 4.5.0) algorithm is utilized to comprehensively analyze the above-mentioned extracted features, with the goal of improving the accuracy of similar activity recognition. Through simulation experiments, it is demonstrated that the feature extraction method proposed in this paper has improved the accuracy of HAR. Compared with classical machine learning algorithms such as random forest (version 1.5.2) and XGBoost (version 2.1.3), the LightGBM algorithm shows improved performance in terms of the accuracy rate, which reaches 94.98%. Moreover, after searching for the model parameters using grid search, the prediction accuracy of LightGBM can be increased to 95.35%. Finally, using feature selection and dimensionality reduction, the efficiency of the model is further improved, achieving a 70.14% increase in time efficiency without reducing the accuracy rate. Full article

Forestry is recognized as one of the most physically demanding professions. Walking in presents unique biomechanical challenges due to complex, irregular terrain, with several possible risks. This study investigated how human gait adapts across solid surfaces, forest trails, and natural forest environments. Fifteen healthy adult participants (average age 38.3; ten males and five females) completed 150 walking trials, with full-body motion captured via a 17 Inertial Measurement Unit (IMU) sensors (Xsens MVN Awinda system). The analysis focused on spatial and temporal gait parameters, including cadence, step length, foot strike pattern, and center of mass variability. Statistical methods (ANOVA and Kruskal–Wallis) revealed that surface type significantly influenced gait mechanics. On forest terrain, participants exhibited wider steps, reduced cadence, increased step and stride variability, and a substantial shift from heel-to-toe strikes. Gait adaptations reflect compensatory neuromuscular strategies to maintain body balance. The findings confirm that forestry terrain complexity compromises human gait stability and increases physical demands, supporting step variability and slip, trip, and fall risk. By identifying key biomechanical markers of instability, this study contributes to understanding human locomotion principles. Understanding these changes can help design safety measures for outdoor professions, particularly forestry. Full article

Accurate information on trail difficulty is essential for ensuring safety and enhancing the effectiveness of forest-based health and recreational activities. This study examined the physiological responses of middle-aged adults to varying trail difficulty levels across both controlled indoor and natural outdoor walking environments. A total of ten healthy individuals aged 40–50 years participated in walking tasks across three designated trail difficulty levels: Moderate, Difficult, and Very Difficult. Physiological indicators assessed included step speed (SS), step count (SC), rate of perceived exertion (RPE), heart rate (HR), oxygen saturation (OS), energy expenditure (EE), metabolic equivalents (MET), and oxygen consumption (VO₂). As trail difficulty increased, HR, RPE, VO₂, EE, and MET consistently showed upward trends, whereas SS and SC demonstrated significant decreases. Additionally, the outdoor setting imposed generally greater physiological demands compared to the indoor condition, suggesting that terrain complexity and elevation changes amplify physical exertion during real-world trail use. The findings contribute valuable empirical evidence for the design of individualized exercise programs, improved trail difficulty classifications, and the advancement of forest-based health promotion policies. Full article

Background: Parkinson Disease (PD) is a progressive neurodegenerative disorder. Current therapeutic trials investigate treatments that can potentially modify the disease course. Testing their efficiency requires outcome assessments that are relevant to patients’ daily lives, which include gait and balance. Home-based examinations may enhance patient compliance and, in addition, produce more reliable results by assessing patients more regularly in their familiar surroundings. Objective: The objective of this pilot study was to assess the feasibility of a home-based outcome assessment designed to video record the Timed up and Go (vTUG) test via a study-specific smartphone app for patients with PD. Methods: 28 patients were recruited and asked to perform at home each week a set of three consecutive vTUG tests, over a period of 12 weeks using an app. The videos were subjected to a manual review to ascertain the durations of the individual vTUG phases, as well as to identify any errors or deviations in the setup that might have influenced the result. To evaluate the usability and user-friendliness of the vTUG and app, the System Usability Scale (SUS) and User Experience Questionnaire (UEQ) were administered to patients at the study end. Results: 19 patients completed the 12-week study, 17 of which recorded 10 videos or more. A total of 706 vTUGs with complete timings were recorded. Random Forest Regression yielded “time to walk up” as the most important segment of the vTUG for predicting the total time. Variance of vTUG total time was significantly higher between weeks than it was between the three consecutive vTUGs at one time point [F(254,23) = 6.50, p < 0.001]. The correlation between vTUG total time and UPDRS III total score was weak (r = 0.24). The correlation between vTUG and a derived gait subscore (UPDRS III items 9–13) was moderate (r = 0.59). A linear mixed-effects model revealed a significant effect of patient-reported motion status on vTUG total time. Including additional variables such as UPDRS III gait subscore, footwear and chairs used further improved the model fit. Conclusions: Assessment of gait and balance by home-based vTUG is feasible. Factors influencing the read-out were identified and could be better controlled for future use and longitudinal trials. Full article

Hip flexion range of motion (ROM) during gait is an important surgery outcome for patients undergoing total hip arthroplasty (THA) that could help patient monitoring and rehabilitation. To allow systematic measurements during patients’ clinical pathways, hip ROM measurement should be as simple and cheap as possible to ensure patient and clinician acceptance. Single IMU options can match these requirements and offer measurements both during daily living conditions and standardized clinical tests (e.g., 10 m walk, timed up-and-go). However, single-IMU approaches to measure hip ROM have been limited. Thus, the objective of this study was to explore the accuracy of one IMU in measuring hip ROM during gait and to determine whether a single-IMU approach can provide results comparable to those of multi-IMU systems. To assess this, machine learning models were employed, ranging from the simplest (linear regression) to more complex approaches (artificial neural networks). Eighteen patients undergoing THA and seven controls were measured using a 3D opto-electronic motion capture system and one thigh-mounted IMU. Hip ROM was predicted from thigh ROM using regression and classification models and was compared to the reference hip ROM. Multiple regression was the best-performing model, with limits of agreement (LoA) of ±13° and a systematic bias of 0. Random forest, RNN, GRU and LSTM models yielded LoA ranges > 27.8°, exceeding the threshold of acceptable error. These results showed that one IMU can measure hip ROM with errors comparable to those of two-IMU methods, with potential for improvement. Using multiple linear regression was sufficient and more appropriate than employing complex ANN models. This approach offers simplicity and acceptance to users in clinical settings. Full article

This study presents an artificial intelligence-based approach for the pose detection of passengers’ skeletons when boarding and alighting from an urban bus in Valparaíso, Chile. Using the AlphaPose pose estimator and an activity recognition model based on Random Forest, video data were processed to analyze the poses and activities of passengers. The results obtained allow for an evaluation of safety and ergonomics in public transportation, providing valuable information for improving design and accessibility in buses. This approach not only enhances understanding of passenger behavior but also contributes to the optimization of bus systems to accommodate diverse needs, ensuring a safer and more comfortable environment for all users. AlphaPose accurately estimates the posture of passengers, offering insights into their movements when interacting with the bus. In addition, the Random Forest model recognizes a variety of activities, from walking to sitting, helping to analyze how passengers interact with the space. The analysis helps identify areas where improvements can be made in terms of accessibility, comfort, and safety, contributing to the overall design of public transport systems. This study opens up new possibilities for AI-driven urban transportation analysis and can serve as a foundation for future improvements in transportation planning. Full article

Dog activities recognition, especially dog motion status recognition, is an active research area. Although several machine learning and deep learning approaches have been used for dog motion states recognition, the use of ensemble learning methods is rather missing, as well as a comparison with deep learning ones. This paper focuses on the use of deep learning neural networks and ensemble classifiers in recognizing dog motion states and their comparison. A dataset from the Kaggle database, which includes measures by accelerometer and gyroscope and concerns seven dog motion states (galloping, sitting, standing, trotting, walking, lying on chest, and sniffing), was used for our experiments. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbors (kNN), Random Forest, a Bagging Tree-Based Classifier, a Stacking Classifier, a Compound Stacking Model (CSM), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Hybrid Cascading Model (HCM) were used in our experiments. Results showed a 1.78% superiority in accuracy (92.64% vs. 90.86%) of deep learning (RNN) vs. stacking (CSTAM) best classifier, but at the cost of larger complexity and training time for the deep learning classifier, which makes ensemble techniques still attractive. Finally, HCM gave the best result (96.82% accuracy). Full article

Broiler gait score (GS) evaluation relies on manual assessments by experts, which can be laborious, hindering timely welfare management. Deep learning (DL) models, conversely, may serve as a cost-effective solution in evaluating GS via automated detection of broiler mobility. This study aimed to develop a vision-based YOLOv8 model to detect the locations of individual broilers, allowing for continuous tracking of birds within a pen and determining bird walking distances, speeds, idleness and movement ratios, and time at the feeder and drinker ratios. Then, Machine Learning (ML) models were developed to estimate the GS level from the mobility indicators in a lab setting. Ten broilers were color-coded and recorded via a top-view camera for 41 days. Their GS were assessed manually twice per week. The YOLOv8 model was trained, validated, and tested with 600, 150, and 50 images, respectively, and subsequently applied to the dataset yielding each broiler’s mobility indicators. The GS levels and mobility indicators were correlated through Ordinal Logistics (OL), Random Forest (RF), and Support Vector Machine (SVM) ML models. The YOLOv8 model was developed with 91% training, 89% testing, and 87% validation mean average precision (mAP) accuracies in identifying color-coded broilers. After tracking, the model estimated an average of 472.26 ± 234.18 cm hourly distance traveled and 0.13 ± 0.07 cm/s speed by a broiler. It was found that with deteriorated GS levels (i.e., worse walking ability), broilers walked shorter distances (p = 0.001), had lower speeds (p = 0.001), were increasingly idle and less mobile, and were increasingly stationed near or around the feeder. The movement ratio, average hourly walking distance, hourly average speed, and age variables were found to be the most significant variables (p < 0.005) in predicting GS levels. These variables were further reduced to one, the average hourly walking distance, because of high collinearity and were used to predict the GS with ML models. The RF model, outperforming others, was able to predict GS with a generalized R² of 0.62, root mean squared error (RMSE) of 0.54, and 65% classification accuracy. Full article

This study investigates the factors influencing urban residents’ interest in and visits to forests and explores strategies to promote forest space utilization. A survey was conducted among 5000 residents of Tokyo’s 23 wards, one of the world’s most densely populated urban areas, using an online questionnaire. The collected data were analyzed using least absolute shrinkage, selection operator (LASSO) logistic regression, and piecewise structural equation modeling (pSEM). The analysis revealed that nature experiences in current travel destinations, particularly scenic walks, had a significant positive effect on both forest interest (standardized path coefficient = 0.19) and forest visits (0.30). These experiences were also significantly influenced by childhood nature experiences and frequent local walks. Conversely, factors negatively affecting forest visits included the lack of private vehicle ownership (−0.13) and increasing age (−0.21). While previous studies suggest that older individuals tend to visit natural areas more frequently, our findings indicate the opposite trend. One possible explanation is the low car ownership rate among Tokyo residents, which may limit accessibility to forests. These findings provide valuable insights for policy design, particularly regarding strategies to enhance forest accessibility and engagement among urban populations. Full article