Search Results (3,395)

Educational environments, particularly those with limited resources, require affordable mobile robots capable of combining human–robot interaction, autonomous assistance, and academic support without continuous dependence on cloud services. This work presents a low-cost ROS2-based mobile robot implemented on a Raspberry Pi 4B to provide educational assistance in Spanish within controlled classroom environments. The system integrates voice interaction, text-to-speech synthesis, YOLOv8n-based object perception, a specialized door detection model, ultrasonic and inertial sensing, differential-drive control, and a hybrid natural language processing architecture based on semantic caching, local inference, and optional cloud connectivity. Two task-dependent operating modes, education and navigation, selectively activate ROS2 nodes to reduce computational load and energy consumption. Experimental tests conducted in a university classroom evaluated speech recognition, vision models, natural language processing alternatives, sensor behavior, and battery life. The speech recognition module achieved 98% accuracy under both quiet and noisy conditions. YOLOv8n achieved an F1-score of 0.975 for common classroom objects, while the specialized door detector achieved 100% recall with 58.7% precision. The semantic cache correctly resolved recurrent academic queries in the exact-match evaluation, with an average latency of 3.8 s, reducing the need for external language models in known-question scenarios. The robot operated for 96 min in education mode and 75.6 min in navigation mode. These results demonstrate that Spanish voice interaction, reactive navigation, academic question answering, and resource-aware operation can be integrated into a single low-cost edge robotic platform for educational environments. Full article

Indoor companion, rehabilitation, logistics, laboratory transport, and service robot scenarios require mobile platforms that can follow a human operator safely and flexibly under lighting changes, occlusion, texture-poor corridors, and dynamic pedestrian environments. Vision-, LiDAR-, and UWB-based following systems can provide high perception capability, but their deployment cost, environmental dependence, and sensing complexity remain limiting factors for low-perception-dependence applications. This paper presents a passive following system for a Mecanum-wheel mobile platform based on gimbal posture perception and orthogonal odometry fusion. A rope-tensioned two-axis gimbal is mounted above a 300 mm × 300 mm × 150 mm omnidirectional chassis, and a six-axis inertial sensor installed at the top of the gimbal detects pitch and roll changes induced by user traction. A piecewise posture-to-velocity mapping model with a dead zone, saturation, low-pass filtering, and acceleration limiting converts the user’s traction intention into planar velocity commands in the vehicle coordinate frame. To reduce pose errors caused by Mecanum-wheel slip and discontinuous roller-ground contact, two orthogonal passive odometry wheels and inertial attitude estimation are fused to provide planar position feedback for closed-loop following. A prototype was implemented using an Infineon TRAVEO CYT4BB77 controller, TI DRV8701E motor drivers, six-axis IMUs, magnetic encoders, and an embedded display interface. Experiments evaluated attitude estimation accuracy, planar localization accuracy, passive following performance, gyroscope compensation, and open-loop/closed-loop following. The compensated attitude module achieved a static yaw drift of 0.45 deg/h and a dynamic attitude RMSE below 0.56 deg. Orthogonal odometry fusion produced an average positioning error of 3.8 mm over a 3000 mm linear displacement, reducing error by approximately 84.6% compared with pure Mecanum-wheel drive odometry. In a 5000 mm forward traction task, closed-loop following reduced the average distance error from 38.6 mm to 11.5 mm compared with open-loop attitude mapping. The results indicate that the proposed gimbal-orthogonal odometry architecture provides a compact, intuitive, and environment-robust solution for passive following on omnidirectional mobile platforms. Full article

Structural Health Monitoring (SHM) of large-span bridges requires dense sensor networks to accurately capture dynamic and kinematic behaviors. Traditional commercial systems rely on complex wiring or wireless protocols that frequently suffer from data loss, high power consumption, and synchronization phase errors, which are detrimental to Operational Modal Analysis (OMA). To address these limitations, this study presents the design, development, and field validation of a custom, offline-synchronized multi-sensor Data Acquisition (DAQ) system. Two specialized sensor nodes were developed: (i) an inertial node integrating a low-noise triaxial MEMS accelerometer (ADXL355); and (ii) a displacement node featuring a 24-bit Analog-to-Digital Converter (ADS1220) for displacement sensors. Both nodes share an ultra-low-power microcontroller (STM32L431) and utilize a local microSD storage strategy with an intermediate pseudo-SRAM buffer. To ensure precise temporal alignment without wireless communication overhead, each node incorporates a temperature-compensated Real-Time Clock (DS3231) for offline timestamp synchronization. The system was validated during a field campaign on the Spyckstraße bridge (Germany), deploying a hardware pool of 53 physical DAQ nodes to monitor 118 distinct geometric measurement points (106 inertial, 12 displacement) through a hybrid strategy of fixed and roving setups. The proposed system achieved reliable, low-noise measurements and enabled accurate extraction of operational mode shapes, demonstrating its viability as a robust, cost-effective solution for large-scale infrastructure monitoring. Full article

The strapdown inertial navigation system (SINS) suffers from cumulative error growth, where velocity and acceleration drifts cause position errors to grow quadratically. While multi-sensor fusion using Doppler Velocity Sensors (DVS) can correct these drifts, traditional methods often assume a uniform radial velocity across the solar disk, thereby ignoring the differential surface velocities caused by solar rotation. This mismatch introduces a significant unmodeled system bias with a mean value of approximately 1.45 km/s and an upper bound of up to 2.07 km/s, which is orders of magnitude larger than the standard measurement error (~1 m/s). Such a dominant deterministic error leads to severe filter divergence, undermining the reliability of the entire integrated navigation system. This mismatch introduces unmodeled system bias, degrading filter performance. To address this issue, this paper proposes a solar disk differential velocity method within a multi-source fusion framework. By constructing a velocity measurement model that reflects the Sun’s actual geometric and rotational characteristics, this method effectively eliminates the deterministic system bias. Combined with dual-star Doppler measurements, the method enhances the observability of 3D velocity errors. Simulation results show that the proposed method significantly improves navigation accuracy compared to the traditional method. Position errors are reduced from kilometer-level magnitudes to the range of tens to hundreds of meters, with velocity errors remaining within 10⁻²–10⁻¹ m/s. Full article

Background/Objectives: Force platforms and optoelectronic motion capture systems are considered gold standards for postural sway assessment, although their use is confined to dedicated laboratory settings. Wearable inertial systems represent a practical alternative; however, their validity compared with reference systems within a shared physical domain (i.e., displacement domain) remains insufficiently investigated. This methodological requirement, frequently overlooked in the existing literature, is here adopted as an explicit inclusion criterion for the first time to ensure an appropriate metrological comparison. This review critically examines the validity of inertial systems for postural sway assessment, only including studies in which sway parameters derived from inertial measurement units (IMUs) were expressed in the same physical domain as the corresponding reference measurements. Methods: A systematic search of the Scopus database was conducted to identify English-language studies published up to January 2026 that compared IMU-derived sway parameters with those obtained from gold-standard systems, using parameters expressed in consistent measurement units. Sensor placement, postural tasks, signal processing techniques, extracted sway parameters, and statistical validation methods were analyzed as key methodological aspects. Results: Eight studies published between 2015 and 2022 met the inclusion criteria. The predominant configuration consisted of a single lumbar-mounted IMU, and quiet bipedal standing was the most frequently investigated postural task. Velocity-based parameters, particularly mean sway velocity, demonstrated moderate to high agreement with reference systems. In contrast, spatial dispersion measures, including the 95% confidence ellipse area and root mean square displacement, showed greater variability and, in some cases, systematic bias in Bland–Altman analyses. Conclusions: Wearable inertial systems demonstrated strong potential for estimating global and velocity-related sway parameters during quiet standing, supporting their clinical applicability. However, spatial metrics and dynamic postural tasks remain more challenging for IMU-based assessment. Methodological standardization of validation protocols and signal processing pipelines is essential to improve comparability and reproducibility across studies. Full article

Space gravitational wave detection missions demand ultra-stable calibration of inertial sensor capacitive sensing. Conventional dynamic methods suffer from mechanical vibration noise and bias separation difficulties, while large-displacement operation introduces pronounced nonlinearity. This work proposes a static calibration method using an image-based displacement measurement system to establish a vibration-free benchmark. A subpixel edge detection algorithm locates the Test Mass and Electrode Housing edges with a repeatability of approximately 0.05 pixels, and the Test Mass geometry is independently calibrated by a Coordinate Measuring Machine (CMM, ±2 µm, $k=2$) to provide SI traceability. A nonlinear calibration model incorporating higher-order Taylor terms is developed, combined with a forward/reverse connection technique for composite bias modeling. Experimental validation at $x_0=665$ µm ($x_0/d_0\approx 0.665$) demonstrated a gain coefficient repeatability of 0.01658% RMSPER and a combined expanded uncertainty of $U\approx 2.18\times {10}^{-5}$$1/\text{µ}\mathrm{m}$ ($k=2$). Intended as a complementary ground-based technique to dynamic calibration, this method avoids dynamic excitation-induced noise while establishing complete SI traceability, offering a reliable solution for ground validation and long-term monitoring of space inertial sensors. Full article

Background/Objectives: Ankle dorsiflexion plays a fundamental role in gait stability, impact absorption, and the stance-to-swing transition, and its impairment is a major limitation in transtibial prostheses. This study proposes and evaluates a lightweight two-stage pipeline for generating ankle-dorsiflexion references using a single shank-mounted inertial measurement unit (IMU). Methods: In the first stage, a deep neural network (DNN) estimates the shank pitch waveform from raw three-axis accelerations and angular velocities. In the second stage, the estimated shank pitch is transformed into an ankle-dorsiflexion waveform using a temporal mapping model. The approach was evaluated on a multisubject subset of the NONAN GaitPrint database comprising 35 healthy young adults, 598 walking trials, and approximately 122,468 gait cycles, using a strict subject-held-out protocol. Results: A feature-based Random Forest baseline showed limited performance, whereas the waveform-based DNN achieved high accuracy for shank pitch estimation, with test R² values up to 0.97. A conventional polynomial mapping between shank pitch and dorsiflexion yielded weak performance, whereas a temporal mapping model substantially improved the estimation of ankle dorsiflexion, with test R² values up to 0.85. The resulting ankle reference was integrated into a Gazebo/Robot Operating System 2 (ROS 2) simulation of a transtibial prosthesis, where the generated trajectories were executed in a software integration test under open-loop position control, confirming stable and consistent trajectory execution. Conclusions: These results indicate that combining accurate shank pitch estimation with temporal mapping enables feasible ankle-dorsiflexion reference generation from a single sensor in able-bodied gait, offering a preliminary, simulation-based pathway for single-sensor artificial intelligence (AI) pipelines in prosthetic development. The framework supports waveform-level feasibility, not clinical readiness or functional prosthetic control. Full article

Front crawl swimming stroke phase segmentation has historically relied on video analysis, but the development of wearable sensor technology has created new opportunities for automated phase segmentation. This scoping review mapped the available evidence on wearable sensor-based stroke phase segmentation methods in front crawl swimming, following PRISMA-ScR guidelines. A systematic search of SPORTDiscus, Web of Science, and IEEE Xplore conducted from January to June 2026, identified 15 eligible peer-reviewed studies published between 2000 and 2024. The review revealed an emerging field of research that has converged methodologically around inertial measurement units (IMUs) and the Chollet phase segmentation framework while remaining heterogeneous in algorithmic approach and validation practice. Most notably, no included study reported force or pressure outcomes of any kind, representing a critical gap between current sensor-based segmentation capabilities and the biomechanical information most relevant to applied coaching. Continued work in algorithm validation and intra-phase force measurement is needed to advance the field toward a more complete and practically applicable understanding of front crawl swimming mechanics. Full article

The passive nature of gravity matching navigation, along with its concealment and freedom from error accumulation over time, is essential for reducing inertial navigation system (INS) errors and enabling high-precision autonomous underwater positioning. The current paper provides a systematic review of major technologies in the field, including the development of underwater gravimeters, construction of gravity reference maps, suitable area selection, optimization of matching algorithms, gravity–inertial integrated navigation, and path planning. We discuss hardware developments, including classical sensors, gradiometers, and quantum sensors, as well as methodological concepts such as multi-source sensor data fusion, intelligent area selection, algorithm optimizations, connections between multiple filters, and intelligent trajectory design. Despite a relatively well-developed technical infrastructure, several bottlenecks remain, including the low engineering maturity of high-end hardware, poor algorithmic performance under extreme conditions, over-reliance on simulation, and weak module integration. Future research should focus on hardware miniaturization, cross-domain intelligent adaptive algorithms, multi-condition real-world validation, and the transition from loosely coupled to tightly coupled architectures to achieve improved accuracy and robustness. Full article

Human gait analysis is a key component of rehabilitation medicine, enabling objective assessment of patient recovery. In crutch-assisted locomotion, however, conventional forearm crutches operate as passive devices, providing no quantitative information on load distribution or patient adherence to partial weight-bearing (PWB) prescriptions. This work presents the design and dynamic validation of a sensorized forearm crutch system for biomechanical monitoring during assisted gait. The proposed device combines a force-sensing module based on a full Wheatstone bridge strain-gauge configuration with a 6-axis inertial measurement unit (IMU) to capture both axial load and crutch orientation. Sensor fusion was implemented through a complementary filter to estimate pitch and roll angles under dynamic conditions. The system was calibrated through static loading procedures and validated against reference instrumentation, including an optoelectronic motion capture system and an instrumented dual-belt treadmill with force platforms. Unlike previous studies relying on stationary force platforms that capture discrete steps and may alter natural gait, this validation approach enabled continuous, stride-by-stride force and orientation measurements without restricting foot placement. Experimental trials were conducted with unimpaired participants performing assisted gait using 2-point and 3-point patterns at two partial weight-bearing levels (20% and 40% body weight) and two walking speeds (0.80 m/s and 1.20 m/s). Dynamic validation showed good agreement with the treadmill reference, with force RMSE values of $9.33\pm 1.70$ N for the left crutch and $12.90\pm 2.85$ N for the right crutch, and with coefficients of determination of $R^2=0.9956$ and $R^2=0.9927$, respectively. Orientation RMSE values were $1.08\pm 0.44$° (roll, right), $2.06\pm 0.56$° (roll, left), $1.79\pm 0.55$° (pitch, right), and $1.66\pm 0.37$° (pitch, left). Beyond validation accuracy, the system enabled extraction of a set of quantitative biomechanical descriptors directly from crutch signals, axial load, cadence, crutch contact variability, load asymmetry, pitch asymmetry, and crutch stance/swing asymmetries, characterizing walking stability, bilateral coordination, and gait regularity during continuous assisted locomotion. These results demonstrate the feasibility of integrating force and inertial sensors into forearm crutches to enable quantitative monitoring of assisted gait, with potential applications in rehabilitation assessment and real-time feedback. Full article

To address the lack of standardized safety assessment tools for upper-limb rehabilitation robots, this study developed an integrated testing platform and a quantitative safety assessment framework, demonstrated with FlexoArm1 as a proof-of-concept. A 6-degree-of-freedom bionic arm equipped with multiple sensors was constructed, and a fuzzy PID control algorithm was employed to improve motion trajectory tracking accuracy. A fuzzy multi-criteria safety assessment model was established by combining the Analytic Hierarchy Process (AHP) with the entropy weight method. Experiments were conducted on the rehabilitation robot FlexoArm1. The platform reliably replaced human subjects in range-of-motion testing, interactive torque measurement (peak torque approximately 6.2 N·m in fully active mode), and spasticity simulation, with angular data showing close agreement with Inertial Measurement Unit (IMU) measurements. The assessment model assigned a comprehensive safety score of 70.23 to the tested device, successfully identifying weaknesses in fault detection capability and structural safety design. The proposed bionic-arm-based testing platform and the accompanying safety assessment methodology provide practical tools and a quantitative basis for standardizing safety evaluation and guiding design optimization of upper-limb rehabilitation robots. Full article

Background: Clinicians working with children with neuromotor impairments require sensitive measures to assess postural control and evaluate interventions. This study explored the sensitivity of a single Inertial Measurement Unit (IMU) to changes in postural control during reaching in sitting with clinician support at different segmental levels: upper thoracic, lower thoracic, lower lumbar, and no support. The effect of pelvic-stabilizing straps was examined. Methods: A single Delsys Trigno IMU sensor attached over the mid-thoracic spine recorded acceleration and angular velocity data during a reaching task in sitting in ten typically developing children. Results: Comparisons of the support levels showed a significantly lower range of accelerations in the medial–lateral and anterior–posterior directions when support was provided at the upper thoracic level compared to support at the lower lumbar level. The range of angular velocity in the sagittal and coronal planes showed progressively lower values as the level of support moved cranially. Pelvic stability straps allowed for a significantly greater range of acceleration values in all directions and a greater range of angular velocities in the sagittal and transverse planes. Conclusions: These exploratory findings suggest that IMUs may have clinical utility in postural control assessment and evaluating the effects of intervention in children with neuromotor impairment. Full article

Musculoskeletal disorders caused by poor posture are a major global health concern, contributing to spinal deformities and chronic pain. This study presents a mobile health (mHealth) enabled smart orthosis for real-time monitoring and correction of spinal posture. The wearable system integrates inertial measurement units along the spine to capture curvature data, processed through computational models to detect postural deviations. A connected mobile application enables real-time feedback, continuous monitoring, and remote assessment. Laboratory validation demonstrated reliable sensor performance. The proposed mHealth solution supports early diagnosis, long-term monitoring, and prevention of posture-related disorders, promoting personalized spine care and patient engagement in daily life. Full article

Four-wheel-independent-drive electric vehicles are gaining increasing research attention due to their comprehensive dynamic performance. Real-time tire–road friction coefficient information contributes to the development of adaptive control algorithms and active safety control systems for such vehicles. However, traditional tire models widely adopted in existing estimation methods may fail to match practical tire characteristics accurately. Furthermore, lateral velocity serves as a critical state variable for tire–road friction coefficient estimation, whereas existing lateral velocity observers using low-cost inertial measurement unit sensors suffer from degraded estimation performance under complex driving maneuvers. To address the above challenges, this paper proposes a three-stage friction coefficient estimation framework. Firstly, vehicle lateral velocities are estimated via a piecewise gain-scheduled observer using inertial measurement unit measurements. Secondly, tire slip ratios are calculated based on the observed lateral velocities; meanwhile, the longitudinal, lateral and vertical forces of each tire are reconstructed. Lastly, tire force and slip information under combined slip conditions are acquired, and a multilayer perceptron neural network is established to achieve individual tire–road friction coefficient estimation. The simulation results verify the numerical feasibility and preliminary effectiveness of the proposed estimation method under ideal simulation conditions. Full article

Accurate estrus detection is essential for optimizing artificial insemination timing, but visual detection is limited by labor demands, intermittent observation, short estrus duration, and variable behavioral expression. Although inertial measurement unit (IMU) systems capture dynamic acceleration and rotational movement, phase-specific IMU-derived activity and posture-related changes during natural estrus remain insufficiently characterized. Therefore, this study evaluated these variables across an ovulation-anchored six-phase framework using video-derived behavioral observations and ultrasound-confirmed ovulation as biological reference standards. In this observational study, five dairy cows contributing eleven natural estrus cycles were monitored, yielding 285,337 time-aligned 10 s sensor observations that were summarized for phase-level analysis. Cow movement was recorded at 10 s intervals using neck-mounted tri-axial accelerometers and gyroscopes, while posture states, estrus-related behaviors, and ovulation timing were determined from continuous video recordings and 6 h transrectal ultrasonography. Extracted variables included signal vector magnitude, VeDBA, Gyro_mag, baseline-adjusted activity features, exploratory Combined Activity Index, posture proportions, and lying bout characteristics. VeDBA was highest during standing estrus, whereas Gyro_mag and the Combined Activity Index increased during pre-estrus and standing estrus. Standing estrus involved less lying, more standing and walking, and shorter lying bout duration. These findings identify candidate IMU-derived and posture-related variables for future standing-estrus differentiation models and potential insemination-timing support, pending validation in larger independent populations. Full article