Search Results (624)

The demographic shift towards an aging population necessitates innovative solutions for care and mobility support. While commercial quadruped robots like the Unitree Go1 offer dynamic stability, their native following modes often lack the safety margins and predictability required, and they do not consistently follow the user, at times deviating and navigating independently. This paper presents a robust, vision-based, person-following algorithm designed to address these limitations. Utilizing a ZED 2 stereo camera and Robot Operating System (ROS), the system employs a finite state machine to ensure deterministic target tracking. A velocity control strategy partitions the robot’s motion into distinct stability, proportional, and braking zones based on depth data to ensure fluid interaction. The framework was validated on a Unitree Go1 quadruped platform in an outdoor environment involving 90-degree turns to evaluate tracking robustness. By operating in a headless mode, the system achieved a mean processing latency of $66.5\pm 4.3$ ms. Experimental results demonstrated consistent operational stability, 0.0% intrusion into the intimate safety zone, and effective velocity synchronization between $0.47$ and $0.54$ m/s. While this study establishes a robust technical baseline using healthy subjects, it serves as a preliminary development platform; further iterative testing with elderly users in clinical settings is required to move toward deployment. Beyond the evaluated trials, the framework maintained reliable functional performance across various care facility workshops, successfully following the target in all deployment scenarios. These findings establish a stable technical foundation for the future development of robotic walking partners. Full article

Hexapod robots are attractive for operation in cluttered and uneven environments, but their walking stability is strongly affected by the coupled effects of leg morphology and foot-end trajectory planning. In many existing designs, leg-segment proportions, reachable workspace, and swing-phase trajectory smoothness are considered separately, which makes it difficult to clarify how structural parameters and motion planning jointly influence locomotion stability. To address this issue, this study presents a spider-leg-inspired hexapod robot with a simplified three-degree-of-freedom leg configuration. Selected functional characteristics of spider legs, including segmented limb structure and compliant distal contact, were abstracted into an engineering-feasible hexapod platform rather than directly reproducing spider anatomy. A parametric workspace analysis was conducted under a fixed total leg length to compare six candidate femur-to-tibia ratios. Based on forward reach, vertical foot-lifting capability, stride potential, and structural compactness, a 4:6 femur-to-tibia ratio was selected. In addition, an eleventh-order Bézier curve was developed for swing-phase foot trajectory planning and compared with a conventional composite cycloid trajectory under identical tripod-gait conditions. Simulation and straight-line walking experiments showed that the Bézier-based trajectory reduced body-attitude fluctuation and produced smoother angular-velocity variation than the composite cycloid trajectory. The results indicate that the proposed structural design and Bézier-based trajectory can improve flat-ground walking stability of the hexapod robot. This work provides a practical reference for biomimetic structural design and gait-trajectory optimization of multi-legged robots, while further validation on more complex terrain remains necessary. Full article

The attribution of systemic financial stress to specific market sectors requires metrics that are faithful to the model’s computations, statistically consistent, and connected to a physically meaningful measure of directed information flow. This paper addresses all three requirements through information geometry, contributing to SDGs 7, 8, 9, and 17 through an entropic causal chain linking energy infrastructure failure to financial market stress. We conjecture and empirically verify the Entropy–Saliency Equivalence: Metabolic Saliency is an asymptotically unbiased estimator of the local Kullback–Leibler divergence between stressed and resting sector return distributions, with bias decaying at a parametric rate under Gaussian regularity conditions. The finite-sample bias–variance decomposition of the Kraskov–Stögbauer–Grassberger transfer entropy estimator is derived, establishing a minimax-optimal convergence rate. A novel metric, the Spatio-Temporal Information Flux (STIF), quantifies directed inter-sector stress transmission in bits per trading day, providing a bootstrap-calibrated audit trail aligned with the South African Financial Sector Regulation Act and MiFID II. Empirical validation on the JSE canonical panel (87 securities, 2857 trading days, 2015–2026) with Eskom load-shedding stages as exogenous stress injectors confirms the equivalence ($R^2=0.810$, $\widehat{\rho }=0.90$), with walk-forward $R^2=0.789$ and placebo $R^2=0.081$ ruling out estimation artefacts. The energy sector is identified as the primary stress transmitter during Stage 4+ Eskom events (STIF rising from 0.14 to 0.43 bits/day, directional asymmetry ratio 4.7). Robustness checks confirm stability across non-Gaussian securities and rolling transfer entropy windows. Full article

Esophageal squamous cell carcinoma (ESCC) progression involves dynamic cellular state transitions and tumor microenvironment remodeling, accompanied by extensive transcriptional regulation reprogramming. Here, we systematically mapped the TF-mediated regulatory landscape underlying ESCC progression at single-cell resolution by integrating stage-specific ESCC single-cell transcriptomic datasets comprising over 200,000 cells with TF–target interaction networks. Using a random walk algorithm combined with hypergeometric testing, we identified malignant progression-associated TFs (mpTFs) across multiple cell types and disease stages. Our analysis revealed extensive stage-dependent regulatory remodeling during ESCC progression. TCF4 was identified as an early-stage regulator associated with epithelial–mesenchymal transition activation and malignant invasive phenotypes. In immune lineages, BATF and IRF4 exhibited trajectory-associated activation during CD4⁺ T-cell differentiation and CD8⁺ T-cell exhaustion, suggesting critical roles in immunosuppressive T-cell state transitions. Additionally, mpTF-mediated remodeling of M2 macrophage subpopulations contributed to immunosuppressive tumor microenvironment formation during advanced ESCC progression. We further identified prognosis-associated cell-type-specific and shared mpTFs, including TFAP2C, which was associated with stabilized fibroblast and monocyte functional states and a less aggressive tumor microenvironment phenotype. Collectively, this study provides a comprehensive single-cell atlas of TF-mediated regulatory programs during ESCC progression and offers potential therapeutic targets for precision oncology. Full article

Video synthetic aperture radar (ViSAR) provides continuous multiframe images while maintaining high resolution and has become an important tool for complex scene surveillance and moving target tracking. ViSAR imaging is susceptible to interframe drift caused by motion errors, which severely degrades video stability. When registering long time series of real airborne video SAR images, conventional image registration based on Normalized Cross-Correlation (NCC) is affected by several factors, including platform residual motion errors, approximations in the imaging geometry, interpolation resampling, and SAR speckle noise. As a result, noticeable interframe jitter persists in the registered sequence, and the stabilization accuracy is insufficient to meet high-precision image stabilization requirements. To address these issues, this paper proposes an image stabilization method for airborne video SAR based on a joint Singer-random walk model. Firstly, with the first frame selected as the reference, subpixel drift measurements in the azimuth and range directions are extracted from continuous frames via NCC-based registration. Subsequently, the true drift is modeled as a two-dimensional Singer process and the systematic bias as a random walk process, yielding a joint state space model that comprises displacement, velocity, acceleration, and bias components. On this basis, a Kalman filter and a Rauch–Tung–Striebel (RTS) fixed-interval smoother are applied to perform temporal filtering and trajectory smoothing on the drift measurements, thereby producing smooth two-dimensional drift estimates that closely approximate the actual drift trajectory. Finally, the smoothed drift trajectory is used to perform frame-by-frame subpixel drift correction on the original image sequence, achieving high-precision interframe stabilization of the ViSAR imagery. The results of real data processing demonstrate that the proposed method can effectively improve the consistency and scene stability of ViSAR multi-frame imaging. Full article

Background: Performance-based tests (PBTs) objectively assess functional capacity and are increasingly applied in fibromyalgia (FM) to complement patient-reported outcomes (PROMs). However, evidence regarding their reliability, especially considering patients’ socio-occupational status, is limited. This study aimed to determine test–retest reliability of a standardized PBT battery in women with FM and to examine the influence of employment status on measurement stability. Methods: A total of 119 women were assessed (89 with FM). The battery included the 6 min walk test (6MWT), handgrip strength test (HST), and 8 feet up and go test (8FUGT). Test–retest reliability was examined using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), and smallest real difference (SRD). Analyses were conducted for the total FM group and socio-occupational subgroups (actively working, claiming disability, and permanent disability). Results: All PBTs demonstrated excellent test–retest reliability. Measurement stability was consistently higher in controls. Absolute reliability indices confirmed acceptable measurement stability. However, the claiming disability group showed markedly higher SEM% and SRD% for HST, suggesting reduced reproducibility. The 6MWT and 8FUGT maintained excellent reliability and stability across all groups. PROMs showed good-to-excellent reliability. Conclusions: PBTs showed excellent reliability in women with FM. However, reliability varied across socio-occupational groups, particularly for HST in patients claiming disability. PROMSs showed lower reliability than PBTs. Full article

This study proposes a safe, seated-use training device designed to strengthen the ankle dorsiflexor muscles, which are essential for maintaining walking stability and preventing falls. In addition to being safe and easy to use, the device enables ‘multitasking exercise’, allowing users to train while sitting and performing daily activities such as watching television. Furthermore, a muscle strength measurement system was developed to quantitatively evaluate the dorsiflexor force. To evaluate its effectiveness, electromyographic analysis examined the proposed device alongside commercially available steppers, demonstrating that only the proposed device provided stable and periodic activation of the ankle dorsiflexor muscle group. A three-week training test with healthy adults revealed increased dorsiflexor strength, confirming the device’s effectiveness following seated training. Exercise using the proposed device is expected to improve muscle strength around the ankle and toes, thereby enhancing ankle stability and helping to prevent falls caused by tripping. Safe and stable walking contributes to improved activities of daily living and extends the range of life activities for the elderly, preventing bedridden conditions and maintaining or improving quality of life. Full article

This study aims to enhance the precision of humanoid robots in imitating complex human “walking–grasping” coordinated movements. Addressing limitations in sample efficiency and reward function design in Generative Adversarial Imitation Learning (GAIL), we propose the Similarity Reward-Augmented Generative Adversarial Imitation Learning (SRA-GAIL) framework. The method integrates plantar thin-film resistive pressure sensors to measure the real-time pressure distribution at four key points on both feet, combined with roll/pitch angle data acquired from JY901S inertial measurement units (IMUs). A Lagrangian constraint optimization strategy is employed to achieve gait stability control based on the zero moment point (ZMP). Simultaneously, a visual similarity evaluation module is established using human demonstration trajectories captured by a Logitech C920E camera, augmented by grip force feedback from flexible thin-film pressure sensors on the hands. This enables the design of a multimodal sensor-fused similarity reward function. By incorporating Lagrangian constraint optimization and a maximum entropy reinforcement learning framework, Similarity Reward-Augmented Generative Adversarial Imitation Learning synchronously optimizes gait stability control—guided by zero moment point (ZMP) and roll/pitch data—and vision-based trajectory similarity evaluation. These components address motion stability constraints and trajectory similarity metrics, respectively, generating biomechanically plausible gait strategies. A spatiotemporal attention mechanism parses human motion trajectory features to drive the end-effector for high-precision trajectory tracking. To validate the proposed method, an imitation learning experimental system was constructed on a physical XIAOLI humanoid robot platform, integrating inertial measurement units (IMUs), plantar pressure sensors, and a vision system. Quantitative evaluations were conducted across multiple dimensions, including robot platform analysis, walking stability, object grasping success rates, and end-effector trajectory similarity. The results demonstrate that, compared to Generative Adversarial Imitation Learning (GAIL) and behavioral cloning, Similarity Reward-Augmented Generative Adversarial Imitation Learning achieves a stable object grasping success rate of 93.7% in complex environments, with a 23.8% improvement in sample efficiency. The method maintains a 96.5% compliance rate for zero moment point (ZMP) trajectories within the support polygon, significantly outperforming baseline approaches. This effectively addresses the bottleneck in robot policies adapting to dynamic changes in real-world environments. Full article

Prolonged standing and repetitive lifting are routine occupational stressors that elevate plantar pressures across workers. In those with diabetes, these demands represent additional risk factors for diabetic foot pathology, highlighting the need for ergonomic interventions beyond standard safety footwear. This study evaluated the perceived ergonomic performance of the MoonWalking^® insole, a novel adaptive pneumatic system designed for real-time pressure stabilization and offloading when integrated into safety footwear. A comparative experimental protocol tested two conditions: safety footwear with the manufacturer’s original insole and the same footwear with the MoonWalking prototype. Twenty participants assessed perceived comfort using a VAS and binary ergonomic questionnaires. The results showed statistically significant improvements in perceived cushioning, foot fit, and overall comfort when using the MoonWalking insole. Participants consistently identified pressure-stabilizing and offloading functions across all plantar regions, indicating that adaptive pressure control was clearly perceptible. No pain or movement restrictions were reported. Although perceived fatigue did not reach statistical significance, a decreasing trend was observed. A slight reduction in intention to reuse the footwear occurred with the prototype, possibly due to its increased weight. These findings provide evidence that integrating an adaptive pneumatic insole into safety footwear may improve plantar pressure redistribution and user comfort. Full article

Background: Special Forces Operators often carry out missions in conditions where the use of motor vehicles is impossible. Additional external load across areas with variable inclination may reduce walking efficiency and consequently limit the combat capability of soldiers. The aim of the study was to determine how ground inclination affects the spatiotemporal structure of gait in Special Forces Operators (SFO) with different military loads. Methods: The study included 50 operators from Polish special forces units. Measurements of walking were performed using the h/p/cosmos Gaitway 1D + 3D treadmill. Tests were conducted at four uphill inclination levels: 0%, 5%, 10%, and 15%. Each participant completed trials both without external load and with a 27 kg load (helmet, tactical vest, and backpack). Statistical analyses were performed using the Friedman test, the Durbin–Conover post hoc test, and linear mixed models (LMM) to assess interaction effects. The Robinson Symmetry Index (SI) was calculated to assess asymmetry between the dominant and non-dominant limbs. Results: Increasing inclination caused statistically significant changes in the spatiotemporal structure of gait. The greatest modifications were observed at 10–15% inclinations, particularly under the maximum load of 27 kg. A significant shortening of step length and gait cycle time was noted, while cadence showed a slight upward trend, especially at a 15% inclination with the highest load. Step width remained stable. Conclusions: Ground inclination, especially when combined with the additional mass of military equipment, significantly affects the locomotion of Special Forces Operators. The stable SI values and consistent step width indicate a high level of gait stability and effective adaptive mechanisms. However, the extent of spatiotemporal modifications observed at inclinations of 10–15% with a 27 kg load may increase the risk of overuse injuries among operators. Full article

Unlike reactive systems, mechanical ventilation controlled by CO₂ concentration operates at a target efficiency that dynamically increases whenever the target CO₂ level is exceeded. This approach eliminates the typical ‘dead-time’ and prevents air quality degradation by ensuring the system adjusts its performance immediately in response to concentration changes. In this work, the study focuses on the development and evaluation of data-driven predictive models for near-term indoor CO₂ forecasting that can be integrated into pre-occupancy ventilation strategies, rather than designing a complete control scheme. Experimental data were collected over four months in a 48 m² smart laboratory configured as an open-plan office, where a heterogeneous IoT sensing architecture logged synchronized time-series measurements of CO₂ and microclimate variables (temperature, relative humidity, PM_2.5, TVOCs), together with acoustic noise levels and appliance-level energy consumption used as indirect occupancy-related signals. Raw telemetry was transformed into a 22-feature state vector using a structured feature engineering method incorporating z-score standardization, cyclic time encodings, multi-horizon CO₂ lags, rolling statistics, momentum features, and non-linear interactions to represent temporal autocorrelation and daily periodicity. The study benchmarks multiple regression paradigms, including simple baselines and ensemble methods, and found that an automated multi-level stacked ensemble achieved the highest predictive fidelity for short-term forecasting, with an Mean Absolute Error (MAE) of 32.97 ppm across an observed CO₂ range of 403–2305 ppm, representing improvements of approximately 24% and 43% over Linear Regression and K-Nearest Neighbors (KNN), respectively. Temporal diagnostics showed strong phase alignment with observed CO₂ rises during occupancy transitions and statistically reliable prediction intervals. Five-fold walk-forward cross-validation confirmed the temporal stability of these results, with top models achieving consistent R² values of 0.93–0.95 across Folds 2–5. These results demonstrate that, within a single-room university laboratory setting, historical sensor data from low-cost IoT devices can support accurate short-term CO₂ forecasting, providing a predictive layer that could support future proactive ventilation scheduling aimed at reducing CO₂ lag at the start of occupancy while avoiding unnecessary ventilation runtime. Generalization to other building types and occupancy profiles requires further validation. Full article

We propose a dimensionally consistent fractional spatio-temporal PDE framework for modelling immune-mediated demyelination in multiple sclerosis (MS). The system couples effector and regulatory T cells, M1/M2 macrophage polarisation, pro- and anti-inflammatory cytokines, oligodendrocyte dynamics, and time-dependent therapeutic controls within a unified distributed-parameter structure. In contrast to ad hoc replacements of integerorder derivatives by Caputo fractional derivatives, the fractional extension proposed here is derived from an underlying continuous-time random walk (CTRW) process with Mittag–Leffler-distributed residence times. This stochastic derivation yields a governing system in which a single commensurate fractional order $\alpha \in (0,1]$, together with a characteristic memory timescale ${\tau }_0$, ensures dimensional consistency and mass balance across all coupled components. The model is formulated as a system of nonlinear reaction–diffusion equations with cross-regulatory and multiplicative interaction terms governing immune amplification, cytokine feedback, and the demyelination–remyelination balance. Analytical interpretation shows how non-Markovian residence times induce Mittag–Leffler-type relaxation and thereby modify effective growth, decay, and stability properties. Numerical simulations compare classical and fractional dynamics, revealing that memory-driven kinetics prolong effector T-cell and M1-macrophage activity, attenuate reparative M2 and oligodendrocyte responses, and extend the effective action of bang–bang therapy inputs representing IFN-$\beta$ and glatiramer acetate beyond their dosing windows. The results indicate that integer-order models may underestimate chronic inflammatory persistence and demyelination severity, while providing a mathematically and physically well-posed platform for memory-aware immune modelling and therapy evaluation in MS. Full article

Cattle behavior constitutes important phenotypic information reflecting animals’ health status, activity level, and welfare condition, and is therefore of considerable significance for automated monitoring and precision management in smart livestock farming. However, under complex barn conditions, cattle behavior recognition is easily affected by factors such as illumination variation, partial occlusion, background interference, and individual differences, thereby reducing recognition stability and generalization capability. To address these challenges, this study proposes a pose-driven method for cattle behavior recognition in complex barn environments. First, a 16-keypoint annotation scheme suitable for describing bovine posture, termed cow16, was constructed. Based on this scheme, OpenPose was employed to extract heatmaps (HMs) and part affinity fields (PAFs), which were then used to build an intermediate HM/PAF posture representation. Subsequently, this representation was taken as the input to a lightweight convolutional neural network for classifying three behavioral categories: stand, walk, and lying. On this basis, class-imbalance correction during training and a multi-random-seed logits ensemble strategy during inference were further introduced. In addition, knowledge distillation was adopted to transfer knowledge from a high-performance teacher model to a lightweight student model. Experimental results demonstrate that training-stage class-imbalance correction and inference-stage multi-random-seed logits ensembling exhibit strong complementarity; when combined, the AB configuration improves the test-set Macro-F1 by 3.83 percentage points. Moreover, the distilled student model still achieves competitive recognition performance while maintaining 1× inference cost, indicating a favorable trade-off between accuracy and efficiency. This study provides a useful reference for deployment-oriented cattle behavior recognition in smart farming scenarios and offers a lightweight technical basis for subsequent practical applications. Full article

Conventional gel electrodes are the gold standard for surface electromyography (sEMG), yet their bulkiness, stiffness, and limited gel lifetime prevents seamless day-long integration with wearable robots. We integrated ultrathin skin-conformal temporary tattoo electrodes with a powered unilateral hip exoskeleton and compared signal quality during treadmill walking against gel. In this pilot study, five healthy participants completed three consecutive walking blocks at fixed speed: (1) using gel electrodes; (2) using tattoo electrodes to compare signal quality; and (3) using the same tattoo electrodes (not repositioned) after eight hours of wear to simulate a full day of typical device use and to evaluate potential degradation in signal quality over time. Electrodes were positioned on muscles not covered by the exoskeleton interface (tibialis anterior and gastrocnemius medialis), as well as on muscles located beneath the exoskeleton cuff, which were potentially subject to motion artifacts due to the application of external forces by the exoskeleton (rectus femoris and biceps femoris, BF). Across all muscles, for both gel and tattoo electrodes, the root mean square error (RMSE) between normalized sEMG envelopes and biological activation profile was 0.069 ± 0.048, and Pearson’s correlation coefficient (ρ) was 0.844 ± 0.091. Re-testing the same tattoo electrode pair after eight hours confirmed day-long stability without the need for recalibration. Statistical analysis revealed no significant differences in signal quality, also when applying assistive forces, between the two electrode types and across all muscles (RMSE, all p ≥ 0.3125; ρ, all p ≥ 0.1250), as well as no degradation after eight hours (RMSE and ρ: all p ≥ 0.0626, uncorrected). Finally, in a proof-of-concept session, BF activity measured with tattoo electrodes was found reliable to drive hip-extension assistance in real time. Collectively, these results show that tattoo electrodes deliver signal quality comparable to gel electrodes while offering a low-profile skin-conformal interface and day-long usability, making them a promising option for enhancing EMG-based control in wearable robots. Full article

Nonlinear analysis provides a framework for understanding the complexity and stability of human locomotion by capturing dynamic patterns beyond linear methods. This study examined the effect of data length on seven nonlinear measures: Sample Entropy (SpEn), Approximate Entropy (ApEn), Lyapunov Exponents using Wolf’s (LyE_W) and Rosenstein’s (LyE_R) algorithms, Detrended Fluctuation Analysis (DFA), Correlation Dimension (CD), and the Hurst–Kolmogorov process (HK). A 3500-frame kinematic dataset from a healthy adult performing motor-assisted elliptical training and treadmill walking was segmented from 100 to 3500 frames in 10-frame increments. Data from treadmill and elliptical conditions were analyzed and presented in a combined manner to highlight general stabilization trends across locomotor tasks. Results revealed that increasing data length significantly affected all nonlinear metrics (p ≤ 0.0005). Stabilization occurred at varying minimum lengths: SpEn at ~4.5–8.8 s (540–1060 frames), ApEn at ~5.4–7.7 s (650–920 frames), LyE_W at ~19.1–29.2 s (2290–3500 frames), LyE_R at ~1.3–1.5 s (150–180 frames), DFA at ~29.2 s (3500 frames), CD at ~1.7–15.9 s (200–1910 frames), and HK at ~9.1–9.8 s (1090–1180 frames). Notably, HK achieved stable estimates in approximately one-third of the time required for DFA and substantially less than LyE_W, supporting its suitability for time-constrained or clinical settings. These findings suggest the need to tailor data collection to each nonlinear metric and to report data length explicitly to improve accuracy, reproducibility, and methodological rigor in gait variability research. However, these findings should be interpreted within the limitations of a single-participant, exploratory design. Full article