Search Results (189)

Stroke survivors with upper-limb impairments often have difficulty using conventional computer interfaces, which limits their ability to perform daily computer-related activities independently. This study developed a wearable multimodal assistive interface that enables computer interaction through a virtual cursor. A lightweight headband equipped with electrooculography (EOG), electroencephalography (EEG), and an inertial measurement unit (IMU) was used to acquire multimodal signals for interaction control. EOG signals were processed to detect voluntary blinks to generate clicks, head movements were mapped to cursor movements through IMU-based control, and frontal EEG signals were used to estimate attention as an auxiliary mechanism for command verification. A rapid user-specific calibration procedure was introduced to adapt blink-detection thresholds to individual EOG characteristics without requiring extensive training. Thirty stroke patients with upper-limb impairments participated in experiments involving common computer tasks, including news reading, video playback, and character spelling. The system achieved an average operation accuracy of 87.53 ± 4.92%, an average operation time of 3.49 ± 0.49 s, and an information transfer rate of 62.04 ± 15.93 bits/min in the spelling task. The mean NASA-TLX score was 32.1 ± 5.4, indicating a moderate subjective workload during system use. These results demonstrate the feasibility of the proposed wearable multimodal assistive interface for supporting computer interaction in stroke survivors with upper-limb impairments. Full article

Cervical range of motion (CROM) has been used in research and clinically for assessing cervical health. Gold-standard goniometers tend to be cumbersome. However, Inertial Measurement Units (IMUs) or vision-based alternatives demand frequent calibration and/or costly hardware; moreover, the subject is aware of being measured and there is a risk of breaching privacy. In response, we have developed a non-contact HPNet system for head pose estimation (HPE) that can use a rear-facing camera to quantify CROM accurately. A Re-parameterized Visual Geometry Group (RepVGG)-D2se model is employed as the backbone of the network, and a Spatial Feature Enhancement (SCFE) module is incorporated to improve feature extraction. HPNet was evaluated on the large-scale Carnegie Mellon University (CMU) Panoptic dataset, achieving a mean absolute error (MAE) of 3.48°, 3.22°and 3.34° for yaw, pitch and roll respectively. Inter-instrument reliability was excellent for all six cervical movements when compared with the research/clinical-grade CROM device, with intraclass correlation coefficients (ICCs) averaging 0.939. Bland–Altman plots confirmed close agreement between the two methods. Cervical movement trajectory curves further confirmed the concordance between the clinical device and our method. The system is fully automatic, requires only a rear-facing camera, effectively preserves patient privacy, and provides accurate cervical posture estimation. This technology may provide a basis for future applications in neck-disorder screening, remote health monitoring, and personalized musculoskeletal wellness management, although further task-specific clinical validation will be required. To date, HPNet has been validated primarily on a computer-based platform and has not yet been deployed on smartphones. Future work will focus on model lightweighting, mobile deployment, and cross-device adaptation to facilitate its practical implementation on mobile devices. Full article

In Global Navigation Satellite System (GNSS)-denied indoor environments, ultra-wideband (UWB) localization of unmanned ground vehicles (UGVs) is challenged by position-dependent anchor visibility and mixed line-of-sight (LOS)/non-line-of-sight (NLOS) ranging. This study proposes a soft continuous confidence weighting method within an adaptive Kalman filter (AKF)-based UWB/inertial measurement unit (IMU) localization framework. The vehicle model uses motor pulse increments and IMU yaw-rate measurements as inputs and outputs vehicle position and heading estimates. Virtual forward–backward iteration converts inconsistencies between the current UWB ranges and tag–anchor geometry into terminal virtual-anchor displacements. A half-Gaussian function then maps each displacement to a continuous confidence coefficient. The resulting coefficients are incorporated into weighted least-squares (WLS) and AKF localization, while the UWB measurement-noise covariance is adaptively updated using the range innovations. The proposed method was evaluated through static calibration and dynamic localization experiments. These experiments compared soft and hard weighting schemes and assessed the contribution of AKF fusion. These results indicate that the method proposed in this study improves localization accuracy, robustness, and temporal continuity under position-dependent anchor visibility and mixed LOS/NLOS conditions. Full article

To address the degradation in attitude accuracy caused by mismatched rotation-axis tilt parameters after inertial measurement unit (IMU) reinstallation in rotational inertial navigation systems (RINSs) on large marine platforms, an interleaved rotation-dwell attitude-and-heading calibration method is proposed. First, a relative attitude-and-heading model incorporating the combined effects of the rotation-axis tilt errors of the two systems is established, with the horizontal error mapping induced by the relative heading between their base frames explicitly considered. Second, a four-state interleaved rotation-dwell sequence is designed, and the rotation-axis tilt parameters of the two RINSs are separated in closed form through Hadamard orthogonal projection. Simulations verify the parameter-decoupling capability of the proposed method. Experimental results show that, compared to a filtering-based self-calibration method for a single RINS, the proposed method reduces the roll and pitch root-mean-square errors (RMSE) by 90.29% and 41.80%, respectively. After compensation for the rotation-axis tilt errors, relative heading alignment between the two systems is achieved by estimating the residual heading bias. The proposed method provides a system-level solution for attitude-and-heading calibration after IMU reinstallation under moving-base field conditions. Full article

High-precision angular-rate measurements in extreme spin environments require systems capable of handling very high rotation rates, rapid startup, and reliable operation under vibration and shock. However, most commercially available gyro-based Inertial Measurement Units (IMUs) provide measurement ranges limited to approximately ±2000°/s, which may be insufficient for high-speed spinning platforms such as spin-stabilized satellites and drilling systems. This work presents the design, implementation, calibration, and validation of a complete Data Acquisition System (DAS) based on the ADXRS649 analog gyroscope, supporting angular rates up to ±20,000°/s. The system integrates a 12-bit ADC within a dsPIC33 microcontroller, high-speed nvSRAM for continuous logging, and firmware enabling sensor self-testing, memory checks, synchronized sampling, and onboard processing. Operating at a configurable sampling frequency of 50 Hz, the proposed system provides approximately 20 min of continuous data recording. Custom hardware, including multilayer PCB design, signal conditioning, power management, a rugged metallic enclosure, and polyurethane potting, enhances mechanical robustness for operation under vibration and shock. Laboratory calibration over the angular-rate range of ±980°/s quantified the gyroscope bias and scale factor, while experimental validation using a high-speed rotary machine demonstrated stable rolling measurements and reliable data integrity at angular rates exceeding 2000°/s. The results demonstrate that the proposed analog gyro-based DAS provides a robust and cost-effective solution for ultra-high-spin applications and future multi-sensor integration. Full article

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

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

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

This paper investigates the precision and stochastic error propagation of navigation solutions obtained from a low-cost microelectromechanical system inertial measurement unit (MEMS-IMU) under static conditions. A modern smartphone equipped with an MEMS-IMU was rigidly mounted at a calibrated fixed location to establish a zero-reference scenario, and inertial measurements were collected while the device remained stationary. The dataset was divided into 75 non-overlapping segments, each comprising 30 s of data sampled at 10 Hz, to enable statistically robust analysis. For each segment, velocity and position, which are theoretically zero under static conditions, were computed using strapdown inertial mechanization. A comprehensive statistical framework was then applied to characterize the stochastic behavior of both the raw inertial measurements and the derived navigation states. The methodology first assessed data normality, stationarity using the Augmented Dickey–Fuller (ADF) test, and variance homogeneity using Bartlett’s test. Subsequently, ARIMA models were identified and validated using the Ljung–Box (LB) test, while power spectral density (PSD) analysis provided complementary frequency-domain characterization. In addition, a multivariate, non-negative least squares variance component estimation (NNLS-VCE) method was employed to jointly estimate the variance components of multiple navigation state variables. The results demonstrate that the accelerometer and gyroscope measurements along all three axes are well characterized as stationary white-noise processes, with standard deviations in the order of ${10}^{-2} \mathrm{m}/{\mathrm{s}}^2$ and ${10}^{-4} \mathrm{rad}/\mathrm{s}$, respectively. The estimated velocity random walk (VRW) coefficients are $\left[0.197,0.201,0.160\right] \mathrm{m}/\mathrm{s}/\sqrt{\mathrm{h}}$, while the corresponding angular random walk (ARW) coefficients are $\left[0.009,0.012,0.008\right] \mathrm{rad}/\sqrt{\mathrm{h}}$. In contrast, the derived velocity and position estimates exhibit random walk behavior caused by error accumulation in the inertial mechanization process and are best represented by ARIMA$(0,1,0)$ and ARIMA$(0,2,0)$ models, respectively, consistent with the corresponding Allan variance analysis. After 30 s of static navigation, the average standard deviations of the ENU velocity estimates are ${\sigma }_v=[0.77,0.44,0.29] \mathrm{m}/\mathrm{s}$, while the corresponding position standard deviations are ${\sigma }_p=[1.30,0.69,0.46] \mathrm{m}$. The proposed framework provides a comprehensive approach for the stochastic modeling, precision assessment, and error characterization of low-cost MEMS-IMU navigation systems. Full article

Ultra-wideband (UWB) and inertial measurement unit (IMU) fusion is an effective scheme for train and rail-guided target localization because UWB provides absolute ranging results and the IMU provides high-rate motion prediction. In practical rail transportation systems, UWB anchors are usually deployed along the track with large longitudinal intervals and limited lateral separation to reduce installation and maintenance costs. This anchor deployment leads to a large condition number of the observation matrix, so slight ranging errors caused by non-line-of-sight (NLoS) propagation may be amplified into large localization errors. To mitigate LoS/NLoS interference, this article proposes a propagation reliability estimation method for track-constrained UWB/IMU fusion localization. First, rail transportation localization along a narrow path is formulated as a one-dimensional track-constrained problem, and each UWB ranging result is converted into a candidate longitudinal coordinate on the known track centerline. Second, the reliability of each anchor–target propagation is estimated in a sliding window by comparing the motion increments solved by UWB observations with the motion prediction by the IMU. Third, the estimated reliability is incorporated into a reliability-weighted track-domain update before a closed-loop position–velocity Kalman correction. The simulation results show that, under the mixed LoS/NLoS scenario, the proposed method achieves an NLoS-interval RMSE of 0.0090 m. Compared with Track-EKF, Track-Gauss-AUKF, Track-Adaptive KF, and Track-SW-FGO, the proposed method reduces the NLoS-interval RMSE by 92.9%, 62.1%, 92.8%, and 92.6%. A supplementary real-data stress test on the public STAR-loc dataset demonstrates an average longitudinal RMSE of 0.0555 m under a strict online calibrated-range protocol, supporting the algorithm’s practical applicability against real-world lateral sway and asynchronous sensor noise. Full article

Inertial motion sensing plays an important role in real-time human–machine interaction applications, including interactive systems, virtual environments, and motion-controlled interfaces, where low latency and accurate motion tracking are important. This paper presents the design and implementation of an embedded inertial sensing system that acquires, processes, and transmits motion data from consumer-grade inertial measurement units (IMUs) under real-time constraints. Rather than introducing a new sensor fusion algorithm, the contribution lies in a systems-oriented methodology comprising a predictive clock-advancement mechanism that prevents cumulative timing drift, an automated matrix-based calibration procedure for hardware-agnostic deployment, and a benchmarking framework for end-to-end real-time system evaluation. Implemented on a resource-constrained embedded platform, the framework integrates sensor acquisition, lightweight filtering, sensor fusion, and real-time orientation estimation within a single processing pipeline. Motion data are transmitted using the CemuHook UDP (User Datagram Protocol) motion protocol (DSU) to demonstrate interoperability with existing motion-control software while maintaining low end-to-end latency and stable throughput. Experimental results show stable sampling frequency, low communication latency, accurate orientation estimation, and low computational overhead. The presented system provides an embedded inertial sensing framework that can be adapted to a range of real-time motion-sensing applications beyond the communication protocol used for demonstration. Full article

Calibration of body-worn inertial measurement units (IMUs) is essential for accurate motion estimation, yet sensor orientations may drift over time due to slippage or long-term integration errors. This causes an IMU to gradually lose alignment with the body segment on which it is mounted. In this paper, we develop a novel technique for Automatic Calibration—recovering this alignment during normal motion, without a dedicated calibration step. We implement automatic calibration by formulating it as the alignment of motion-induced orientation distributions on $SO\left(3\right)$. When the activity being performed is known (e.g., walking), the distribution of orientations for a correctly calibrated IMU exhibits a characteristic and repeatable pattern on $SO\left(3\right)$. Although sensor drift may rotate this distribution into a different orientation, the underlying pattern remains similar. Thus, we calibrate a drifted IMU by aligning its observed orientation distribution to a prior reference distribution obtained from the same activity, either from the same subject at an earlier time or from other subjects. To perform this alignment, we employ a correspondence-free spherical pattern matching method on $SO\left(3\right)$ (SO3_SPMC), based on transformed basis vector distributions and spherical cross-correlation. We evaluate the proposed automatic calibration framework with N = 5 people wearing body-worn IMUs during activities of daily living, demonstrating calibration accuracy comparable to supervised single-frame calibration without requiring explicit calibration poses or user intervention. The activities of walking, typing, and using using a computer mouse were best for automatically calibrating a sensor on the wrist. 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

This study proposes a digital twin-based CPR compression measurement system (DTCMS) architecture for real-time monitoring of CPR compression. The system combines a load cell, an inertial measurement unit (IMU), a LabVIEW acquisition platform, and a CNN module to capture multi-modal motion characteristics during CPR repetitive compression training. A calibration-aware sensor fusion framework synchronizes heterogeneous signals, reduces drift, and enhances robustness under high-frequency operation. Real-time data acquisition, latency-controlled transmission, and digital twin visualization enable synchronized physical–virtual interaction. Experimental results demonstrate high accuracy (R² > 0.99), stable repeatability (coefficient of variation: CV < 3.5%), and reliable dynamic tracking. The compression depth error was maintained within ±1.5 mm, and synchronization latency remained below 0.2 s. Results confirm the proposed DTCMS architecture as a robust solution for real-time biomechanical monitoring and digital twin-based interactive systems. Compared with conventional single-sensor CPR monitoring systems, the proposed framework improves synchronization stability and sensing robustness through calibration-aware multi-sensor fusion. Full article

Indoor localization for unmanned aerial vehicles (UAVs) remains challenging in GNSS-denied environments due to the difficulty of position calibration of multiple ultra-wideband (UWB) anchors and the asynchronous fusion of heterogeneous sensors. This paper proposes a multi-sensor fusion localization framework that integrates multi-anchor one-shot calibration with factor graph optimization (FGO). First, Landmark Multidimensional Scaling (LMDS) is used to reconstruct the relative geometry of the anchors and the onboard tag from ranging measurements. Then, rigid Procrustes alignment is performed using a small number of anchors with known coordinates in the East–North–Up (ENU) frame to recover the transformation to the ENU frame, thereby enabling efficient position calibration of multiple UWB anchors and UAV pose initialization. Subsequently, a tightly coupled factor graph is constructed by incorporating inertial measurement unit (IMU) pre-integration, UWB ranging, laser rangefinder height measurements, and visual–inertial odometry (VIO) pose constraints. The resulting nonlinear optimization problem is solved using incremental smoothing, which improves robustness against non-line-of-sight (NLOS) errors and long-term drift. Experimental results on anchor calibration, public datasets, and real-world indoor UAV flights demonstrate that the proposed method improves the accuracy and robustness of indoor UAV localization. In particular, on the real-world rectangle trajectory, FGO-TC reduces the RMSE by approximately 38.8% compared with FGO-LC. Full article