Search Results (669)

Human activity recognition (HAR) using inertial sensors is essential for health monitoring and wellness applications, yet robust classification in real-world adult scenarios remains challenging due to subject variability and activity transitions in smartphone sensing environments. This study investigated smartphone-based physical activity recognition using accelerometer and gyroscope signals under a cross-subject evaluation protocol. To reduce label ambiguity and improve generalization, the original activity set was grouped into a reduced 6-class taxonomy. We evaluated lightweight deep learning models, including a smartphone-only convolutional neural network (CNN) and a multimodal fusion model combining smartphone and smartwatch signals. Using GroupKFold cross-subject validation, the smartphone-only CNN achieved competitive performance with Macro-F1 ≈ 0.46, while multimodal fusion did not provide consistent improvements. We also examined temporal segmentation and showed that shorter windows (2.0 s) yield better results than longer windows. Sensor ablation confirmed the importance of gyroscope information, and per-class analysis indicated that dynamic activities could be recognized reliably, whereas stairs and static categories remained difficult. Overall, the results demonstrate the practicality of smartphone-based activity recognition using built-in smartphone sensors without external wearable devices for adult activity monitoring and provide recommendations for window length and sensor selection in cross-subject HAR. Full article

Agile parkour in unstructured environments poses significant challenges for quadruped robots, requiring both dynamic motion generation and terrain adaptability. Recent advances such as Adversarial Motion Priors (AMP) have shown promise in learning dynamic behaviors through motion imitation, but the resulting policies are typically specialized and struggle to generalize across varying terrains. However, existing AMP-based approaches largely lack explicit environmental awareness, leading to limited adaptability and revealing a clear gap in achieving general agile locomotion. To address this limitation, we propose a novel terrain-conditional AMP framework that extends adversarial motion priors by conditioning the discriminator on explicit terrain features, enabling the learning of terrain-aware motion representations adaptable to diverse environments. To improve practical applicability, we further leverage a vision-based policy distillation scheme, where a teacher policy with privileged terrain height information supervises a student policy operating only on forward-looking depth images. This enables agile, perception-driven locomotion in real time. To the best of our knowledge, this is the first work to integrate environmental information into adversarial motion priors and jointly learn a vision-based policy through policy distillation for agile quadruped locomotion. Experiments on terrains such as platforms, gaps, stairs, slopes, and debris show that the proposed method achieves more efficient training convergence and higher success rates compared to pure AMP-based and RL-based methods. These results highlight the effectiveness of the proposed framework and represent a step toward perception-driven agile locomotion for quadruped robots in complex environments. Full article

Background/Objectives: Sarcopenia is a progressive decline in skeletal muscle strength and mass, leading to decreased functionality, metabolic disorders, morbidity, and mortality. There are a number of sarcopenia screening tools, such as the SARC-F questionnaire (that includes noting strength, assistance with walking, ability to raise from the chair, climb stairs, and falls), with its augmented forms that have added calf circumference (SARC-CalF), BMI and age (SARC-F + EBM), and the Ishii score, which show variable performance across populations. However, these were developed and validated mostly in Asian cohorts. To evaluate the diagnostic accuracy of these tools for the European Working Group on Sarcopenia in Older People (EWGSOP2), as well as define sarcopenia in hospitalized East European older adults, with sex and obesity stratification. Methods: Sarcopenia was diagnosed using the EWGSOP2. ROC analyses with DeLong tests assessed SARC-F, SARC-CalF, SARC-F + EBM, and the Ishii score in 278 Romanian inpatients (probable sarcopenia n = 201/278, 72.3%; confirmed n = 77/278, 27.7%). Results: Probable sarcopenia was noted as good-excellent discrimination against across all tools (AUCs 0.764–0.812); confirmed sarcopenia was noted as SARC-CalF superior (AUC = 0.743), followed by SARC-F + EBM (0.697), the Ishii score as moderate (0.667), and SARC-F was limited (0.591; p < 0.001 vs. augmented). SARC-CalF optimal cut-offs varied significantly: 4–6 (probable) vs. ≥11 (confirmed). Sex-stratified outcomes had excellent probable detection in both sexes, and this was confirmed to be superior in men. The Ishii score thresholds were 152/244 vs. Asian ≥ 105/120. Obesity required higher cut-offs with high NPVs (77–100%), confirming rule-out utility and SARC-F + EBM performing the best, both in the obesity and sarcopenic obesity subgroups (AUCs 0.742, 0.964). Conclusions: Augmented SARC-F scores outperformed the original SARC-F for confirmed sarcopenia in multimorbid Europeans, with SARC-F CalF having the best performance overall. Population-specific (sex/obesity) data-driven thresholds are essential, especially for the Ishii score, as this first Romanian validation reveals limitations of Asian norms in European cohorts, thus advocating for European recalibration. Full article

Chronic stress is among the most pervasive health challenges of contemporary urban life, yet its persistence is not simply a matter of external pressure. When adult hippocampal neurogenesis is impaired, the brain loses its capacity to regulate the hypothalamic–pituitary–adrenal (HPA) axis and distinguish new threats from familiar ones through dentate gyrus pattern separation, rendering stress self-perpetuating. Physical activity is widely recognised as a promoter of neurogenesis through brain-derived neurotrophic factor (BDNF), yet the built environments in which most people spend approximately 90% of their time simultaneously suppress BDNF through chronic stress and deny sufficient physical activity intensity to restore it, a condition known as type 2 allostatic overload sustained by architectural impoverishment. This paper proposes architectural enrichment as a theoretical framework designed to resolve this problem at its root through two independent but synergistic mechanisms: architecturally mediated voluntary stair use to elevate peripheral BDNF via metabolic pathways, and neurobiophilic design based on the Neurobiophilia Index to attenuate cortisol and passively support BDNF and neurogenesis. Twelve hypothesised neurobiological profiles are derived in a framework that advances the concept of hippocampal neurosustainability, proposing that buildings can be designed not merely to avoid harming the brain but to actively sustain its capacity for resilience amid the stressors of modern urban living. Full article

Lower-limb rehabilitation exoskeletons must balance biomechanical compatibility, structural safety, and low mass to enable practical, repeatable gait assistance. This paper proposes a planar pantograph-derived exoskeleton leg driven by a Chebyshev Lambda linkage and develops an integrated workflow from mechanism synthesis to manufacturable optimization and experimental verification. A mannequin-coupled multibody model was built in MSC ADAMS to evaluate joint kinematics, end-point (foot) trajectories, and joint reaction forces under multiple scenarios (fixed-frame, ramp, stair ascent, and inclined-plane walking). The extracted joint loads were transferred to a parametric finite element model in ANSYS Workbench 2019, where response surface surrogates and a multi-objective genetic algorithm (MOGA) were used to minimize mass under stiffness and strength constraints. For the optimized load-bearing link, the selected minimum-mass design reached a component mass of 0.542 kg while respecting the imposed structural limits, i.e., a maximum total deformation below 0.2 mm and a maximum equivalent (von Mises) stress below 50 MPa (e.g., ~0.188 mm deformation and ~39 MPa stress in the optimal candidate). A rapid prototype was manufactured by 3D printing and experimentally evaluated using CONTEMPLAS high-speed video tracking, providing measured X_M(t) and Y_M(t) trajectories and joint-angle histories for quantitative comparison with simulations via RMSE metrics. Full article

The shift toward lightweight powertrain architectures necessitates a detailed characterization of polymer gears to verify their efficiency and durability. This study investigated the effectiveness of non-contact structured-light 3D scanning for evaluating the surface topography and dimensional tolerance quality of polymer gears produced via distinct manufacturing technologies. A structured-light 3D scanner was used to capture dense point clouds (exceeding 6 million points) of gears produced by three methods: conventional hobbing (POM-C), Material Extrusion (MEX) with carbon fiber reinforcement, and Selective Laser Sintering (SLS). The manufactured parts were compared against the nominal Computer Aided Design (CAD) models to evaluate their geometrical deviations in accordance with DIN 3961 and surface roughness parameters per ISO 25178. The experimental results revealed a consistent ranking of manufacturing quality. The conventionally hobbed POM-C gear exhibited superior precision, achieving DIN quality grades of Q9–Q10 and the smoothest surface finish (S_a = 5.0 µm). Among additive manufacturing techniques, SLS-printed PA 12 showed intermediate quality (Q11, S_a = 12 µm), whereas MEX-printed PPS-CF exhibited significant deviations (exceeding Q12) and the highest surface irregularity (S_a = 25 µm) due to stair-stepping effects. These findings indicate that while additive manufacturing offers geometric flexibility, conventional hobbing retains a decisive advantage in dimensional precision. The optical scanning methodology demonstrated here constitutes an efficient metrological framework for gear quality control, with potential applications extending to the quality assurance of additively manufactured adaptive fixtures and assembly tooling, including automotive assembly operations. Full article

This study investigates the dynamic artifacts (DAs) in knitted resistive strain sensors (KRSS) subjected to various deformation types, including stair-wise, trapezoidal, and triangle-type deformations. The presence of DAs, characterized by sharp peak-wise increases in resistance followed by a gradual decline, was observed across all KRSS samples. The amplitude of DA peaks increased with higher deformation velocities within the investigated range of 2.6–40 cm/s. The study also identified the temporal offset between resistance and deformation during linear deformation, suggesting a complex mechanism underlying DAs. The results demonstrate that DAs are most prominent in stepwise and trapezoidal deformations, while continuous deformations like triangle-type loading partially mask these artifacts. The resistance signals were recorded at a sampling rate of 150 Hz, with temporal desynchronization between recorded parameters not exceeding 6.7 ms, enabling the observation of dynamic effects. Manifestation of DAs in KRSS degrades the metrological characteristics of KRSS and cannot be ignored. This paper provides insights into the relationship between KRSS structure, deformation velocity, and DA behavior, and provides an experimental basis for future compensation approaches to mitigate the impact of DAs on measurement accuracy. Full article

Urban underground infrastructures are highly vulnerable to intense rainfall events, particularly access stairs, where preferential runoff paths and the most probable evacuation routes can conflict. This study presents a pluvial flood hazard assessment for underground access stairs in the Barcelona Metropolitan Area Metro network. It integrates the EU ICARIA project modeling framework and the hazard assessment criteria based on hydraulic parameters identified by the Spanish national research project FAVOUR. Both current and future climate change rainfall scenarios are considered. The results showed that out of 415 underground access points, 27 face a high risk of floods, while 35 more have potentially high-risk conditions. These figures could rise to 38 (40% increase) and 47 (74% increase) respectively by the end of the century since climate change is projected to increase rainfall intensity and frequency. By quantifying hazard levels across the network, this study allows the identification of points of the infrastructure where hazard conditions can be more critical. Furthermore, the results presented could potentially support targeted adaptation strategies such as entrance retrofitting, improved drainage design, and emergency planning to develop resilient and sustainable cities. The proposed methodology demonstrates how ICARIA’s modeling framework can effectively evaluate and anticipate flood hazards in complex urban environments at the asset level. Full article

Precise orbital floor reconstruction requires personalised surgical templates that combine high geometric fidelity with manufacturing efficiency. This study presents and validates the TARMM procedure, developed to optimise the production of polylactide (PLA) templates. A key innovation is the integration of advanced machine learning algorithms (Random Forest) and Mitchell–Netravali interpolation to reduce medical reconstruction artefacts, as well as the implementation of Material Extrusion (MEX) technology with Variable Layer Height (VLH). This strategy minimises the stair-step effect on complex anatomical curvatures while maintaining high process throughput. The results demonstrate that the TARMM procedure ensures a geometric error within ±0.1 mm. A strong linear correlation (r = 0.99) was found between layer height and surface roughness (Sa), indicating that a 0.07 mm layer in critical areas significantly improves template morphology and facilitates the contouring of titanium meshes. The clinical validation across 21 cases confirmed a 30 min reduction in surgical preparation time. The developed method serves as a low-cost, high-precision alternative to photopolymerization technologies, contributing to modern 3D printing applications in maxillofacial surgery. Full article

In this study, the continuous strengthening behavior of CoCrNi medium-entropy alloy at 1.2–4.2 nm twin spacings was revealed by molecular dynamics simulation. It was found that the yield strength increased linearly with the decrease in twin spacing, up to 12.526 GPa, and there was no softening inflection point. The strengthening mechanism is mainly due to the effective obstruction of coherent twin boundaries (TBs) to the dislocation slip, especially the stair-rod and Lomer–Cottrell lock structures generated by ISF and ESF stacking faults when crossing the interface. These structures significantly enhance the work-hardening capacity of the alloy by inducing dislocation stacking, although the very dense twin boundary will reduce the dislocation growth rate by limiting dislocation propagation. This precise interface control provides an important atomic-scale basis for the design of novel high-strength and high-work-hardening alloys. Full article

Sarcopenia is a degenerative musculoskeletal condition recognised as the age-related decline in skeletal muscle mass, strength, and function. Traditional diagnostic methods are limited by cost, accessibility, and subjectivity. This study aimed to develop a non-invasive, AI-driven, video-based framework for early Sarcopenia detection using functional movement analysis. Participants with and without Sarcopenia were recorded performing functional movements such as level walking, stair climbing, and ramp walking. Ten representative frames were extracted from each participant, resulting in 300 images (150 Sarcopenic, 150 non-Sarcopenic) utilised for the study. The DeepSarcAE model is an integrated framework of an autoencoder and a CNN-based classifier. Its performance was benchmarked against pretrained architectures such as EfficientNet, ResNet, MobileNet, Inception, VGG16 and four custom CNN models. Evaluation metrics such as sensitivity, specificity, precision, negative predictive value (NPV), accuracy, and AUC were used to analyse the models. DeepSarcAE outperformed all other models, attaining 100% sensitivity, 83.33% specificity, 85.71% precision, 100% NPV, 91.67% accuracy, and an AUC of 0.96. VGG16 and MobileNet followed the performance of DeepSarcAE closely, while the Inception network exhibited the weakest results due to poor generalisation. TheDeepSarcAE framework offers a scalable, cost-effective, and non-invasive approach for Sarcopenia screening from the input gait image frames. Its promising preliminary performance highlights the potential of deep learning in early diagnosis and clinical decision support in preventive healthcare. Full article

Chronic obstructive pulmonary disease, interstitial lung disease, and post-lung trans-plantation are often accompanied by skeletal muscle dysfunction that worsens the quality of life. Such physiological changes are driven by physical inactivity, systemic inflammation, oxidative stress, anabolic and hormonal resistance, and medication effects. Structural changes include impaired capillarization, fiber-type shifts (slow-to-fast in limb muscle and fast-to-slow in respiratory muscles), mitochondrial dysfunction, reduced oxidative capacity, and early lactate accumulation. Electromyography and dynamometry, both isokinetic and isometric, quantify neuromuscular drive through measuring strength, power, and endurance and are associated with functional outcomes (6-min walk, sit-to-stand, stair climbing tests). Pulmonary rehabilitation (PR) improves neuromuscular efficiency, dyspnea, exercise tolerance, and quality of life by combining resistance, endurance, and eccentric training. The effects of PR generally plateau at three months, emphasizing the need for maintenance and the personalization of rehabilitation plans. While nutritional optimization is important, supplements have shown little benefit. Future priorities include defining EMG/dynamometry thresholds to allow standardized routine testing for comparable benchmarks and more precise PR protocols. Future research targeting mitochondrial remodeling, inflammatory signaling, and anabolic resistance offer potential pathways for preventing and reversing muscle wasting. Full article

Background: Microgravity exposure causes muscle atrophy and bone density loss in astronauts. Traditional motion analysis provides estimations of external kinematics and muscle activation, but cannot resolve internal load. OpenSim closes this gap by applying musculoskeletal modeling to estimate internal joint mechanics. Methods: In this study, we aimed to develop an OpenSim workflow to estimate joint angles and moments using datasets from two publicly available gait studies: the Politecnico di Milano study (Dataset 1), which includes level-floor walking, walking on heels, walking on toes, and step-down-from-stairs tasks, and Maclean et al.’s walking study in reduced gravities (Dataset 2), which includes four simulated gravity levels (1.0 G, 0.76 G, 0.54 G, and 0.31 G). Marker and ground reaction force (GRF) data, along with participants’ mass, were used to prepare the first three steps of OpenSim’s workflow, including scaling, inverse kinematics (IK), and inverse dynamics (ID). Scripts using MATLAB R2025a (The MathWorks, Inc., Natick, MA, USA) were created to store, normalize, and compare OpenSim outputs with reference data on the right leg. Pearson’s correlation coefficient (PCC) was used to quantify agreement between OpenSim-derived joint angles and moments and the reference data, and root mean square error (RMSE) was used to characterize accuracy. Results: Hip and knee angles showed excellent correlation across both datasets (PCC > 0.974). Ankle angles were more variable, particularly in Dataset 1 (PCC = 0.833; RMSE = 19.797°) compared to Dataset 2 (PCC = 0.995; RMSE = 8.73°). Joint moment correlations were strong for hip and knee (PCC > 0.85), though ankle moments in Dataset 1 exhibited lower correlation (PCC = 0.677) and higher error (0.30 Nm/kg) compared to the high accuracy observed across all joints in Dataset 2. Discussion: We speculate that the lower PCC values and higher RMSE observed for ankle dorsi/plantar flexion angle and moment in Dataset 1 are mainly attributable to differences in shank segment frame definitions between the OpenSim model and the human body model used in Dataset 1. Higher ankle angle RMSEs in Dataset 2 may be due to lower weights assigned to ankle markers in the scaling and IK setup files, resulting in different ankle joint center definitions. Conclusion: In the future, we plan to improve this OpenSim workflow by including additional participants and datasets collected in simulated reduced-gravity environments and by implementing a residual reduction algorithm (RRA) and computed muscle control (CMC) to enable muscle activation estimation. Full article

In recent years, many countries have increased their investment in the field of humanoid robots, promoting significant technological development. This study aims to enable humanoid robots to better adapt to various complex environments, enhancing the robustness of their motion systems and the generalization ability of their motion strategies. Using reinforcement learning algorithms, training on varied terrain is a critical factor for developing adaptable humanoid robots. This paper takes the humanoid robot G1 as the research platform. First, it completes the training, transfer verification, and real-machine deployment of a flat-ground walking model. Then, using fuzzy logic control and a phased training strategy, walking models for ascending/descending stairs and traversing slopes are trained. By systematically varying the stair height and slope gradient, the convergence of the reward function and the task completion success rate are analyzed. Furthermore, the dynamic stability of the robot on complex terrains is validated through qualitative kinematic analysis. The research concludes that as the single-step height and slope gradient increase, the reward value initially rises with more iterations but converges more slowly and at a lower final value. Statistical analysis shows that the success rates of phased training for stair and slope terrains are higher than 86% and 92%, respectively. Full article

Background: Cardiorespiratory fitness is frequently impaired in survivors of pediatric acute lymphoblastic leukemia (ALL), limiting their functional performance. While aerobic exercise is recommended, evidence is needed to guide the prescription of specific training protocols in this population. Objective: This study sought to compare the efficacy of constant-load (CL-AEx) and graded aerobic exercise (G-AEx) protocols on cardiorespiratory fitness and functional capability in pediatric survivors of ALL. Methods: Seventy-two pediatric ALL survivors were allocated to CL-AEx, G-AEx, or a control group. Cardiopulmonary fitness [peak oxygen consumption (peak VO₂), peak minute ventilation (VE), ventilatory equivalent for oxygen (VE/VO₂), respiratory exchange ratio (RER), peak oxygen pulse (peak O₂P), maximum heart rate (max HR), and one-minute heart rate recovery (HHR₁)] and functional performance [six-minute walk test (6MWT), 4x10-m shuttle run test (4x10-mSRT), and timed up down stairs (TUDS)] were assessed at pre- and post-intervention. Results: The G-AEx group exhibited significantly enhanced cardiorespiratory and functional outcomes compared to both the CL-AEx and control groups (all p < 0.05). The G-AEx group demonstrated more pronounced improvements, showing significant increases in peak VO₂, VE, VE/VO₂, peak O₂P, and HHR1, alongside a more efficient RER. Functionally, the G-AEx intervention led to superior improvements in 6MWT distance, and significantly faster completion times in the 4x10-mSRT and TUDS, highlighting multi-domain functional gain. Conclusions: In pediatric survivors of ALL, G-AEx demonstrated superior improvements in cardiorespiratory fitness and functional performance compared to CL-AEx over 12 weeks. These findings suggest that G-AEx is an effective modality for addressing acute physical deconditioning in this population. Incorporating G-AEx into clinical rehabilitation may enhance immediate physiological and functional recovery during the survivorship phase. Full article