Search Results (1,940)

Accurate assessment of joint kinematics is fundamental to biomechanics, rehabilitation, and sports science. Although optical motion capture (OMC) remains the laboratory reference standard for biomechanical validation, its cost, infrastructure requirements, and limited applicability outside controlled environments restrict its broader use. Wearable inertial measurement units (IMUs) and vision-based markerless systems have consequently emerged as complementary alternatives, offering portability, reduced subject preparation, and applicability in ecological settings. Their rapid development, however, has not always been accompanied by an equally rigorous metrological interpretation of performance. This narrative review provides a comparative analysis of IMU- and vision-based approaches for joint kinematics estimation, focusing on biomechanical validation metrics and measurement error. Because the primary literature reports fundamentally different quantities under widely differing experimental conditions, evidence is presented stratified by outcome class, joint, plane of motion, task, and acquisition dimensionality, and values belonging to different outcome classes are not pooled. For sagittal-plane lower-limb angles during level walking in healthy adults, with careful sensor-to-segment calibration and an optoelectronic reference, IMU-based systems show the most consistent performance, with RMSE commonly between 3° and 6°. Vision-based systems achieve comparable accuracy for selected outcomes, particularly spatiotemporal gait parameters and sagittal-plane angles in controlled views, while degrading with occlusion, motion blur, and depth ambiguity. Accuracy is therefore not an intrinsic property of the sensing modality but of the entire measurement chain, including calibration, biomechanical modeling, acquisition geometry, and reporting conventions. Rather than ranking technologies by accuracy alone, the measurement requirements should be derived from the intended application. Full article

Disaster-prone urban areas must support transient and vulnerable populations, including foreign tourists who may face language barriers and difficulty interpreting conventional 2D hazard information. This study developed a smartphone-based augmented reality (AR) system designed to support flood-risk interpretation by foreign tourists in Japan. The system integrates four live external APIs providing location, weather forecast, hazard, and designated emergency evacuation site information, and retrieves these data on demand. Its core function converts hazard map-derived flood-inundation category data into 1:1-scale AR flood visualizations using simultaneous localization and mapping (SLAM) and inertial measurement unit (IMU) data. The system also provides a multilingual interface in English, Japanese, and Thai, weather forecast display, and nearby designated emergency evacuation site identification within a 1500 m radius. This study is positioned as a technical feasibility study. The evaluation comprised a verification of the AR height-placement mechanism against a physical reference at the five display heights used by the system, including the smallest category at 0.3 m (21.5 mm mean absolute alignment error, 1.0–2.3% of the target height); a quantitative SLAM drift evaluation under three movement patterns (at most 1.6 cm mean horizontal and 0.7 cm mean vertical drift after 180 s, within a priori thresholds); and an API response time evaluation under 5G and Wi-Fi (50 measurements per API per network; summed per-API means of approximately 3.99 s and 3.89 s at the endpoint level). The results support technical feasibility under the tested conditions, while effects on comprehension, preparedness, and evacuation decision-making remain for future user studies. Full article

Adaptive locomotion across continuous terrain transitions remains difficult for hexapod robots because contact timing, body attitude, support height, and load distribution change simultaneously along a route. This paper presents a unified proprioception-driven deep reinforcement learning and central pattern generator (DRL-CPG) framework for terrain-transition locomotion without visual terrain classification, explicit terrain labels, or terrain-specific controller switching. A high-level proximal policy optimization policy maps a 46-dimensional proprioceptive observation to a three-dimensional CPG modulation action comprising oscillation amplitude, swing-phase frequency, and turn modulation. A coupled six-node Hopf oscillator network then expands these modulated parameters into phase-coordinated rhythmic commands, which are mapped to the 18 joint targets of a JetHexa hexapod and executed by a low-level proportional-derivative controller. The observation space contains body linear velocity, body angular velocity, relative joint positions, relative joint velocities, the previous three-dimensional policy action, and inertial measurement unit (IMU)yaw/heading relative to the initial track direction. A continuous route consisting of flat ground, uphill stairs, irregular terrain, downhill stairs, and a recovery segment is defined to evaluate transition-aware locomotion using route completion, velocity-tracking error, lateral deviation, and roll/pitch fluctuation. Compared with the fixed-parameter CPG and end-to-end DRL baselines, the proposed method increased the full-distance success rate at 4.7 m from 9% and 20%, respectively, to 88%, while maintaining smoother velocity, lateral deviation, and roll/pitch responses. The framework preserves the rhythmic prior of CPG control while reducing the exploration burden of reinforcement learning, providing a compact formulation for adaptive hexapod locomotion across terrain transitions. Full article

Neurological disorders drive demand for prolonged upper-limb rehabilitation, yet specialist access is uneven and assessment stays subjective. Marker-based and inertial measurement unit (IMU) systems are accurate but costly and impractical at home, while pose estimation pipelines mostly stop at keypoints, and many process video server-side, raising privacy concerns. We present a markerless pipeline that analyzes monocular RGB video entirely on-device in the browser, so it never leaves the machine. From 33 BlazePose keypoints, it derives five geometric metrics designed to limit body-size dependence—incomplete elbow extension, inter-limb asymmetry, shoulder girdle elevation, lateral trunk lean, and head tilt—compared against empirically calibrated, preliminary thresholds; a finite-state machine segments repetitions, and the flags are pooled into an unvalidated, exploratory quality score. Against an IMU reference over the 0–${62}^{\circ }$ range that the recordings cover, the image-plane angle showed a mean absolute error of ${2.18}^{\circ }$, below the pre-specified $5^{\circ }$ tolerance, a trajectory-averaged bias within $\pm 2^{\circ }$, and Lin’s concordance correlation coefficient of $0.956$; the difference is, however, proportional to the angle—about $4\%$ of the measured value—so the accuracy should not be extrapolated to larger elevations, and because that comparison was made offline, it does not include the timing error of the causal real-time path. On a single seated frontal-plane abduction task, with 18 healthy volunteers simulating the compensations and annotated by two independent clinicians blind to the instructed condition, compensation detection reached a macro-averaged $F_1$ of $0.75$ and $0.72$ against the individual raters. The five signs differ in maturity: near-expert for trunk lean and head tilt, moderate for incomplete elbow extension and inter-limb asymmetry, and weakest for shoulder elevation, which a single frontal view cannot fully disentangle from the abduction motion. Running at 22–30 frames per second on consumer laptops without relying on a discrete GPU, it offers an accessible, privacy-preserving proof-of-concept foundation for home telerehabilitation; generalization beyond this one exercise and effectiveness on genuine post-stroke compensations remain to be established. Full article

The Velocity-Aided Navigation (VAN) method for determining latitude, longitude, and altitude is proposed when global navigation satellite system (GNSS) signals are unavailable. Currently, GNSS receivers are the primary navigation systems that meet consumer demand for location accuracy. However, GNSS receivers are not autonomous. Strapdown inertial navigation systems (SINSs), unlike GNSSs, are autonomous. Their operating principle is based on double integration of accelerometer output signals. However, they have a significant drawback: SINS errors increase significantly over time. Two approaches are used to improve accuracy. The first involves using expensive, high-precision gyroscopes and accelerometers. The other involves correcting the SINS by integrating it with navigation systems built on physical principles different from those of the SINS. An alternative method, based on VAN and an inertial measurement unit (IMU), for determining navigation parameters is proposed and does not require double integration of accelerometer output signals. Analytical expressions for the errors of the new method are derived. Calculations showed that the errors of the new method are significantly smaller than those of the autonomous SINS. Experimental testing confirmed the calculation results and demonstrated that the errors of the new method are comparable to those of the SINS integrated with GNSS using a Kalman filter. The proposed alternative VAN method for determining latitude, longitude, and altitude can be used independently, as an alternative to GNSS for integration with the SINS, and can also serve as a backup navigation system. Full article

Robotic teleoperation is a core enabling technology spanning remote surgery, industrial inspection, and virtual-reality applications. Despite growing deployment, operator experience assessment still relies almost exclusively on post hoc subjective questionnaires, which preclude real-time monitoring and adaptive intervention. Here, the wearable IMU chain is considered not only as a command interface but also as an implicit sensing channel for operator state. We test whether end-effector kinematics generated by the IMU-to-robot mapping contain information about ten post-task workload and user-experience dimensions, comprising NASA-TLX-inspired workload scales together with usability, responsiveness, realism, intuitiveness, and perceived performance. A secondary analysis of a publicly available dataset (16 participants, 144 motion recordings, simulated UR10e arm) is conducted through a three-stage pipeline: bivariate correlation analysis (Pearson and Spearman), multivariate regression (10 model families, 16 feature-set combinations, Leave-One-Subject-Out validation), and binary classification (median-split). Statistical validity is assessed via 1000-permutation nested testing. Target-specific regression models reach $R^2\ge 0.50$ on seven out of 10 subjective dimensions, with a peak of $R^2=0.787$ for usability; permutation testing confirms significance for eight out of 10 targets. Binary classification achieves AUC $\ge 0.75$ on nine out of 10 targets, with three dimensions reaching perfect AUC. SHAP analysis identifies temporal irregularity and distributional shape descriptors as the dominant kinematic explanatory families. These results support the feasibility of kinematics-based inference of operator experience and provide an offline proof of concept toward future real-time adaptive teleoperation systems. Full article

Background: Quantitative assessment of dynamic balance remains challenging in clinical practice. Wearable inertial measurement unit (IMU)-based technologies offer an objective and accessible approach for monitoring postural control. This study aimed to develop and preliminarily validate a wearable IMU-based trunk sway monitoring system (SwayTracker) and investigate trunk sway characteristics in stroke patients walking with and without arm sling support. Methods: A sternum-mounted IMU system was developed to quantify dynamic trunk sway during walking. Fifteen healthy adults and fourteen stroke patients participated. The healthy participants established normative reference values, whereas the stroke patients completed walking trials with and without arm sling support. Trunk sway was quantified using anteroposterior (AP) and mediolateral (ML) deviations and a polar-coordinate-based sway model. Results: The healthy reference cohort exhibited a mean trunk sway magnitude (radius) of 8.20 ± 3.16°. The stroke patients demonstrated greater sway during unsupported (15.36 ± 5.83°) and sling-assisted walking (15.35 ± 4.59°). Although overall sway magnitude remained unchanged, the mean sway direction shifted from 69.49° to 110.20°, indicating a redistribution of trunk sway from the anterior-right toward the anterior-left quadrant. Forward sway remained the dominant AP component, whereas ML sway shifted from predominantly rightward to leftward with sling use. Conclusions: SwayTracker provides a feasible method for objective assessment of dynamic trunk sway during walking. The stroke patients exhibited increased sway magnitude and altered directional organization compared with healthy individuals. Arm sling use primarily modified ML postural compensation patterns rather than reducing overall trunk sway, highlighting the potential of wearable trunk sway monitoring for gait and balance assessment in neurological rehabilitation. Full article

Anterior cruciate ligament (ACL) injuries represent one of the most common and disruptive conditions in sport. Effective return-to-play (RTP) monitoring requires multidimensional approaches capturing physical, psychological, and sport-specific components rather than reliance on isolated benchmarks. This study aimed to longitudinally examine the RTP process of a female varsity basketball athlete following ACL reconstruction, using a framework integrating physical performance (capacity), biomechanical sport-specific (capability), and psychological (confidence) components relative to pre-injury benchmarks. Data collection included countermovement jump testing with force plates, on-court inertial measurement unit (IMU) monitoring of limb-loading, and psychological questionnaires, analyzed relative to the athlete’s pre-injury baseline, with post-surgical change interpreted against that individualized reference using minimal detectable change thresholds. Pre-injury monitoring indicated stable movement profiles with minor fluctuations. Following ACL reconstruction, jump height recovered within seven weeks of RTP initiation, but notable inter-limb asymmetries persisted in force plate and IMU measures despite high confidence scores. Symmetry improved over time, yet variability in on-court loading remained after clinical clearance. These findings highlight the value of integrated, multidimensional monitoring to detect residual deficits that may be overlooked by traditional outcome-based assessments. This study demonstrates that integrating athlete-specific biomechanical, psychological, and sport-specific assessments relative to pre-injury baselines can support RTP decision-making to enhance individualized recovery trajectories in female athletes. Full article

Reliable fault detection and isolation (FDI) for actuators and sensors in underactuated autonomous underwater vehicles (AUVs) is challenging because nonlinear hydrodynamic coupling redistributes fault signatures, and persistent ocean currents can trigger false alarms. This paper presents a reinforcement learning (RL)-augmented dual-layer FDI framework for actuator and sensor faults in underactuated AUVs. The actuator layer uses a robust adaptive propagated interval observer (RAPIO) that evaluates thruster and control-surface residuals against a calibrated dynamics-consistency tube. The sensor layer forms estimator-consistency residuals for Doppler velocity log (DVL), depth, and inertial measurement unit (IMU) measurements against a reference-separated finite-time extended state observer (FTESO). An offline-trained soft actor–critic (SAC) policy schedules bounded actuator uncertainty margins and sensor alarm thresholds according to operating confidence. The scheduled actuator error dynamics remain Metzler and Hurwitz, preserving positive interval propagation and center-error input-to-state stability (ISS) independent of policy convergence. A Schmitt-trigger alarm and signal-space disambiguation rule classify healthy, actuator-only, sensor-only, and simultaneous-fault conditions under explicit residual-separation and persistence conditions. Across 72 simultaneous-fault episodes over a $4\times 6$ uncertainty–current grid, the proposed method achieved $100\%$ detection coverage for both actuator and sensor faults with only five false-alarm events, retaining full coverage in the severe-current, high-uncertainty subset where the selected actuator and sensor baselines achieved only 88.9% and 70.4% detection, respectively, with more false alarms. These results indicate that the proposed bounded RL scheduler can deliver reliable, certifiable actuator and sensor fault diagnosis under significant operational uncertainty. Full article

Autonomous flight planners consume sensor-derived estimated states rather than simulator ground truth. Learned dynamics surrogates are queried over a planning horizon through architecture-native multi-step interfaces, so strong short-horizon accuracy can still yield unreliable long-horizon trajectory scores. We address this with a JSBSim sensor-interface evaluation protocol that assesses surrogate predictions from onboard sensing and lightweight estimation—inertial measurement unit (IMU), global navigation satellite system (GNSS)/air-data, barometric, and vertical-speed channels with delay, dropout, asynchronous refresh, and estimator filtering—and guides surrogate selection by a planning task. This paper contributes an evaluation-and-selection methodology for learned flight-dynamics surrogates used by autonomous planners; it is not a new flight-dynamics, guidance, or control method, and it does not propose a new neural architecture. The contribution is a transparent and auditable sensor-interface and estimator-conditioned protocol, instantiated on five surrogate families over a 20 s prediction horizon. We emphasize at the outset that under strict native-unit physical tolerances, all evaluated surrogates diverge on 90–100% of validation windows and none is field-ready; all results are comparative JSBSim stress-test evidence, not field-readiness evidence. Results reveal criterion-dependent ordering: long short-term memory (LSTM) networks are strongest on root-mean-square error (RMSE@1s/RMSE@20s), shared-threshold failure, and absolute fidelity, whereas the Transformer is favored under self-scaled divergence and capped-risk criteria. These aggregate scores combine heterogeneous simulator-coordinate units and are benchmark diagnostics rather than physical safety margins. Because different threshold families select different winners, our central conclusion is not that one surrogate is best, but that the choice of metric and threshold family is itself part of the benchmark assessment. In separately generated JSBSim candidate-screening episodes, Transformer has approximately 8% lower mean cost under abstract sensing corruption, whereas estimator-loop screening shifts toward LSTM; feedback-rich tracking remains exploratory at 40 episodes per scenario and has mixed cost and success endpoints. Cross-aircraft transfer degrades substantially. Surrogates should be assessed using sensing-aware, estimator-conditioned, task-specific long-horizon criteria, not short-horizon accuracy alone. Full article

A sign language interpretation system aims to translate sign gestures into spoken or written language in real time, enabling signers and non-signers to communicate in their familiar linguistic forms. However, vision-based approaches suffer from hand occlusion, lighting variability, and complex backgrounds, while Deep Neural Network (DNN)-based methods incur heavy computational costs that hinder real-time use on resource-constrained platforms. In this paper, we propose sign language gloves and a lightweight motion retrieval method for real-time sign language interpretation that runs on mobile devices and embedded systems. The sign language gloves integrate flex sensors, an inertial measurement unit (IMU), and pressure sensors to accurately capture gesture features, including finger bending angles, hand orientation, movement trajectories, and fingertip contacts with body parts, enabling recognition of touch-based gestures. For the motion retrieval method, we build a comprehensive gesture dataset with the gloves and perform feature analysis on each sign language gesture to avoid redundant information in the dataset. During interpretation, our system employs a feature-labeling mechanism to ensure gesture distinguishability and a gesture retrieval algorithm to evaluate movement continuity and similarity. This allows the system to identify corresponding feature labels and consolidate them into complete sign language vocabulary entries. The proposed motion retrieval method is characterized by low computational complexity and a well-defined data structure. This makes it suitable for integration into embedded systems, offering real-time performance and high portability for practical deployment. In our experiments, the proposed system achieved an average recognition accuracy of $92\%$ on a gesture dataset covering 300 sign language words. Full article

In accelerometer-based measurement, the inclusion of upper extremity (UE) activity during walking can overestimate real-world functional UE use in people post-stroke. This study introduces a comprehensive, accelerometer-based approach to examining real-world UE use, including the contribution of UE activity during walking, and compares UE use during walking versus ADL-based activities. People with subacute stroke wore bilateral wrist and ankle inertial measurement units (IMU) for one week (24 h/day). Agreement between UE-Total, UE-Continuous Walking and UE-ADL measures were assessed using Bland–Altman analyses. Variables were modeled using linear regression to explore step count, mobility aid use and UE motor impairment as predictors of IMU measures of real-world UE use. Agreement between UE-Total and UE-ADL measures showed minimal bias. Differences between UE-Continuous Walking and UE-ADL varied by mobility aid. UE-Continuous Walking was associated with increased UE activity and relatively greater paretic arm use compared with UE-ADL. Across UE-Continuous Walking and UE-ADL, step count was the strongest predictor of real-world UE use. UE activity during continuous walking exhibits different use patterns as compared to ADL-based activity. Accordingly, separating continuous walking-related UE activity from ADL-based UE use may improve clinical interpretation of IMU measures of real-world UE use in individuals with subacute stroke. Full article

To address inaccurate longitudinal speed acquisition, difficult road adhesion identification, and insufficient reliability of state inputs for traction control in dual-motor electric vehicles under low-adhesion, adhesion-transition, and drive-slip conditions, this paper proposes a state estimation method oriented to traction control. Four-wheel speeds, inertial measurement unit (IMU) signals, and vehicle dynamics are fused to establish a layered longitudinal speed estimation structure, including slip-confidence evaluation, inertial correction, kinematic and dynamic fusion, and multi-mode weight decision. Standard road adhesion curves, fuzzy inference, and recursive correction are further combined to estimate the peak adhesion coefficient and the optimal slip ratio online. CarSim/Simulink co-simulation results show that the root mean square errors of the proposed speed estimation method are 0.1226, 0.1728, 0.1070, and 0.0322 m/s under comprehensive driving, acceleration slip, emergency braking, and high-speed steering conditions, respectively. Under an adhesion-transition condition, the peak adhesion coefficient and optimal slip ratio can be updated rapidly with road changes. Application results suggest that the estimated states can provide useful inputs for front–rear axle traction coordination under the investigated low-adhesion conditions. Full article

In this article, machine learning (ML) is proposed as a solution to detect and classify false message injection attacks in autonomous ground navigation. First, multiple trajectories are designed and simulated to collect authentic feature samples offered by the odometry and inertial measurement unit (IMU) of an autonomous ground vehicle (UGV). Then, a dataset comprising these samples and other injected samples that simulate two cyberattacks, namely path modification (PM) and velocity drift (VD), is created to train, validate, and benchmark various ML classification models. These include decision tree (DT), k-nearest neighbors (KNN), multi-layer perceptron (MLP), random forest (RF), and support vector machine (SVM). The optimum classification model is experimentally evaluated using a UGV platform, and results suggest that the proposed solution allows the detection of authentic and attacked messages with more than 98% average accuracy and sub-millisecond prediction time. Thus, this solution is ideal for real-time classification, especially in fixed-route applications, e.g., public transportation. Full article

To address the reduction in nozzle row-following accuracy caused by sensing–execution latency during corn row-following operations, a delay compensation method based on machine vision and dynamic region of interest (ROI) adjustment was proposed. The method integrates real-time forward-velocity information from a global navigation satellite system/inertial measurement unit (GNSS/IMU), decomposes the delays associated with image processing, command transmission, and actuator motion, and calculates a visual look-ahead distance from the total response delay and robot forward velocity. Inverse-perspective mapping was used to establish the relationship between pixel and world coordinates, and the ROI position was dynamically shifted to synchronize the sensing–execution process. Indoor bench tests showed that, under variable conveyor-belt speeds ranging from 0 to 0.25 m/s, the algorithm achieved a row-following accuracy of 93.75% and a lateral mean absolute error of 0.019 m; compared with the average result of the three fixed-ROI tests, the lateral mean absolute error was reduced by 24.8%. Whole-machine tests showed that, under random platform forward speeds of 0–1.00 m/s, the row-following accuracy remained above 85.71%, with a lateral mean absolute error of 0.034 m. The results indicate that the proposed method effectively compensates for system delay under different speed conditions and reduces lateral tracking errors caused by longitudinal spatiotemporal mismatch, providing technical support for the development of precision corn row-following spraying equipment. Full article