Search Results (2,448)

This study developed an inertial measurement unit (IMU)-enhanced bidirectional reflectance distribution function (BRDF) imaging system to address angular errors in multi-angle polarimetric measurements. The system integrates IMU-based closed-loop feedback, motorized motion, and image calibration, achieving zenith angle error reduction of up to 1.2° and angular control precision of approximately 0.05°. With a modular and lightweight structure, it supports rapid deployment in field scenarios, while the 2000 mm rail span enables detection of large-scale targets and three-dimensional reconstruction beyond the capability of conventional tabletop devices. Experimental evaluations on six representative materials show that compared with mark-based reference angles, IMU feedback consistently improves polarimetric accuracy. Specifically, the degree of linear polarization (DoLP) mean deviations are reduced by about 5–12%, while standard deviation fluctuations are suppressed by 20–40%, enhancing measurement repeatability. For the angle of polarization (AoP), IMU feedback decreases mean errors by 10–45% and lowers standard deviations by 10–37%, ensuring greater spatial phase continuity even under high-reflection conditions. These results confirm that the proposed system not only eliminates systematic angular errors but also achieves robust stability in global measurements, providing a reliable technical foundation for material characterization, machine vision, and volumetric reconstruction. Full article

With the rapid development of smart manufacturing, the optimization of real-time monitoring in operating procedures has become a crucial issue in modern industry. Traditional disassembly and assembly (D/A) work, relying on human experience and visual inspection, lacks immediacy and a quantitative basis, further affecting operating quality and efficiency. This study aims to develop a thin-film force sensor and an inertial measurement unit (IMU)-integrated wearable device for monitoring and analyzing operators’ behavioral characteristics during D/A tasks. First, by having operators wear self-made smart gloves and 17 IMU sensors, the work tables with three different heights are equipped with a mechatronics machine for the D/A experiment. Common D/A motions are designed into the experiment. Several subjects are invited to execute the standardized operating procedure, with upper limbs used to collect data on operators’ hand gestures and movements. Then, the measured data are applied to verify the performance measure functional best path of machine D/A. The results reveal that the system could effectively identify various D/A motions as well as observe operators’ force difference and motion mode, which, through the theory of performance indicator optimization and the verification of data analysis, could provide a reference for the best path planning, D/A sequence, and work table height design in the machine D/A process. The optimal workbench height for a standing operator is 5 to 10 cm above their elbow height. Performing assembly and disassembly tasks at this optimal height can help the operator save between 14.3933% and 35.2579% of physical effort. Such outcomes could aid in D/A behavior monitoring in industry, worker training, and operational optimization, as well as expand the application to instant feedback design for automation and smartization in a smart factory. Full article

The assessment of upper limb (UL) movement patterns plays a critical role in the rehabilitation of individuals with motor impairments resulting from neuromotor disorders, which significantly affect essential activities of daily living (ADLs) such as drinking and eating. However, conventional clinical evaluation methods often lack objective and quantitative insights into the biomechanics of movement. To enable accurate identification of pathological patterns, it is first necessary to establish normative biomechanical and electrophysiological benchmarks in healthy individuals. In this study, a previously developed, low-cost, wearable, and portable prototype device was employed to objectively assess UL movement. The system, specifically designed for clinical applicability, integrates surface electromyography (EMG) sensors and an inertial measurement unit (IMU) to capture muscle activity and kinematic data, respectively. Thirty healthy participants were recruited to perform standardized drinking and eating tasks. The analysis focused on characterizing muscle activation patterns and joint range of motion during different task phases. Results revealed consistent variations in muscle contraction and joint kinematics, allowing the identification of distinct activation profiles for key shoulder muscles. The findings contribute to the establishment of a normative dataset that can serve as a reference for the assessment of clinical populations. Such data are essential for informing rehabilitation strategies and developing predictive models of UL function during ADLs in individuals with neuromotor disorders. Full article

This work focuses on time and frequency domain analyses of IMU-based orientation estimation algorithms, including indirect Kalman (IKF), Madgwick (MF), and complementary (CF) filters. Euler angles and quaternions are used for orientation representation. A 6-DoF IMU is attached to a 6-joint UR5e robotic arm, with the robot’s orientation serving as the reference. Robotic arm data is obtained via an RTDE interface and IMU data via a CAN bus. Test signals include pose sequences, which are big-amplitude, slowly changing signals used to evaluate stationary and low-dynamics responses in the time domain, and small-amplitude, fast-changing generalized binary noise (GBN) signals used to evaluate dynamic responses in the frequency domain. To prevent poor filters’ performance, their parameters are tuned. In the time domain, RMSE and MaxAE are calculated for roll and pitch. In the frequency domain, composite frequency response and coherence are calculated using the Ockier method. RMSEs are computed for response magnitude and coherence, and averaged equivalent time delay (AETD) is derived from the response phase. In the time domain, MF and CF show the best overall performance. In the frequency domain, they again perform similarly well. IKF consistently performs the worst in both domains but achieves the lowest AETD. Full article

This paper addresses the problem of recognising and classifying hand movements to control an upper-limb exoskeleton. To solve this problem, a multisensory system based on the fusion of data from electromyography (EMG) sensors, inertial measurement units (IMUs), and virtual reality (VR) trackers is proposed, which provides highly accurate detection of users’ movements. Signal preprocessing (noise filtering, segmentation, normalisation) and feature extraction were performed to generate input data for regression and classification models. Various machine learning algorithms are used to recognise motor activity, ranging from classical algorithms (logistic regression, k-nearest neighbors, decision trees) and ensemble methods (random forest, AdaBoost, eXtreme Gradient Boosting, stacking, voting) to deep neural networks, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformers. The algorithm for integrating machine learning models into the exoskeleton control system is considered. In experiments aimed at abandoning proprietary tracking systems (VR trackers), absolute position regression was performed using data from IMU sensors with 14 regression algorithms: The random forest ensemble provided the best accuracy (mean absolute error = 0.0022 metres). The task of classifying activity categories out of nine types is considered below. Ablation analysis showed that IMU and VR trackers produce a sufficient informative minimum, while adding EMG also introduces noise, which degrades the performance of simpler models but is successfully compensated for by deep networks. In the classification task using all signals, the maximum result (99.2%) was obtained on Transformer; the fully connected neural network generated slightly worse results (98.4%). When using only IMU data, fully connected neural network, Transformer, and CNN–GRU networks provide 100% accuracy. Experimental results confirm the effectiveness of the proposed architectures for motor activity classification, as well as the use of a multi-sensor approach that allows one to compensate for the limitations of individual types of sensors. The obtained results make it possible to continue research in this direction towards the creation of control systems for upper exoskeletons, including those used in rehabilitation and virtual simulation systems. Full article

Purpose: To quantify real-time hand–foot coupling in tennis and test whether the coupling pattern differs by playing standard. Methods: Fifteen nationally certified second-level male athletes and fifteen recreational beginners performed multi-directional swings, alternating forehand–backhand groundstrokes and serve-and-volley sequences while tri-axial ankle acceleration and racket-grip force were synchronously recorded in wearable inertial measurement units (IMUs). Grip metrics (mean force, peak force, force duration) and acceleration magnitudes were analysed with MANOVA and Hedges’ g effect sizes, followed by the Benjamini–Hochberg correction (α = 0.025). Results: Across tasks, athletes showed higher mean ankle acceleration (standardised mean difference, Hedges’ g) but 45% lower mean grip force (Hedges’ g = −1.28; both p < 0.01). The association between acceleration and grip metrics was moderate-to-strong and negative in athletes (r = −0.62 with mean grip force; r = −0.69 with force duration), whereas beginners exhibited moderate-to-strong positive correlations (r = 0.48–0.73). Conclusion: We quantified hand–foot coordination in tennis by synchronising tri-axial ankle acceleration with calibrated racket-grip force across three match-realistic tasks. Relative to beginners, athletes demonstrated an inverse coupling between ankle acceleration and grip-force metrics, whereas beginners showed a direct coupling, consistent with our purpose of quantifying coordination via synchronised wearable sensors. Full article

This paper presents a bionic extended Kalman filter (EKF) state estimation algorithm for agricultural planters, inspired by the bionic mechanism of migratory birds navigating in complex environments, where migratory birds achieve precise localization behaviors by fusing multi-sensory information (e.g., geomagnetic field, visual landmarks, and somatosensory balance). The algorithm mimics the migratory bird’s ability to integrate multimodal information by fusing laser SLAM, inertial measurement unit (IMU), and GPS data to estimate the position, velocity, and attitude of the planter in real time. Adopting a nonlinear processing approach, the EKF effectively handles nonlinear dynamic characteristics in complex terrain, similar to the adaptive response of a biological nervous system to environmental perturbations. The algorithm demonstrates bio-inspired robustness through the derivation of the nonlinear dynamic teaching model and measurement model and is able to provide high-precision state estimation in complex environments such as mountainous or hilly terrain. Simulation results show that the algorithm significantly improves the navigation accuracy of the planter in unstructured environments. A new method of bio-inspired adaptive state estimation is provided. Full article

Human gait analysis is essential for clinical evaluation and rehabilitation monitoring, particularly in post-stroke individuals, where joint kinematics provide valuable insights into motor recovery. While optical motion capture (OMC) is the gold standard, its high cost and restricted use in laboratory settings limit its accessibility. This study aimed to evaluate the accuracy of REEV SENSE, a novel magnetometer-free inertial measurement unit (IMU), in capturing knee and ankle joint angles during overground walking in post-stroke individuals using assistive devices. Twenty participants with chronic stroke walked along a 10-m walkway with their usual assistive device (cane or walker), while joint kinematics were simultaneously recorded using OMC and IMUs. Agreement between the systems was assessed using the mean absolute error, root mean square error, 95% confidence intervals, and Pearson’s correlation coefficient. Knee angles measured with the IMUs showed a strong correlation with the OMC (r > 0.9) and low errors (MAE < 5°), consistent with clinical acceptability. Ankle angle accuracy was lower for participants using walkers, while knee measurements remained stable regardless of the assistive device. These findings demonstrate that REEV SENSE IMUs provide clinically relevant kinematic data and support their use as a practical wearable tool for gait analysis in real-world or remote clinical settings. Full article

The real-time detection and localization of dynamic targets in cattle farms are crucial for the effective operation of intelligent equipment. To overcome the limitations of wearable devices, including high costs and operational stress, this paper proposes a lightweight, non-contact solution. The goal is to improve the accuracy and efficiency of target localization while reducing the complexity of the system. A novel approach is introduced based on YOLOv8s, incorporating a C2f_DW_StarBlock module. The system fuses binocular images from a ZED2i camera with GPS and IMU data to form a multimodal ranging and localization module. Experimental results demonstrate a 36.03% reduction in model parameters, a 33.45% decrease in computational complexity, and a 38.67% reduction in model size. The maximum ranging error is 4.41%, with localization standard deviations of 1.02 m (longitude) and 1.10 m (latitude). The model is successfully integrated into an ROS system, achieving stable real-time performance. This solution offers the advantages of being lightweight, non-contact, and low-maintenance, providing strong support for intelligent farm management and multi-target monitoring. Full article

This study introduces GaitX, a real-time pedestrian behavior recognition system that leverages only the built-in sensors of a smartphone eliminating the need for external hardware. The system is capable of detecting abnormal walking behavior, such as using a smartphone while walking, regardless of whether the device is handheld or pocketed. GaitX applies multivariate time-series features derived from accelerometer data, using ensemble machine learning models like XGBoost and Random Forest for classification. Experimental validation across 21 subjects demonstrated an average classification accuracy of 92.3%, with notably high precision (97.1%) in identifying distracted walking. In addition to real-time detection, the system explores the link between gait variability and psychological traits by integrating MBTI personality profiling, revealing the potential for emotion-aware mobility analytics. Our findings offer a scalable, cost-effective solution for mobile safety applications and personalized health monitoring. Full article

To address the decline in the localization accuracy of magnetic adhesion wall-climbing robots operating on large steel structures, caused by visual occlusion, sensor drift, and environmental interference, this study proposes a simulation-based multi-sensor fusion localization method that integrates an Inertial Measurement Unit (IMU), Wheel Odometry (Odom), and Ultra-Wideband (UWB). An Extended Kalman Filter (EKF) is employed to integrate IMU and Odom measurements through a complementary filtering model, while a geometric residual-based weighting mechanism is introduced to optimize raw UWB ranging data. This enhances the accuracy and robustness of both the prediction and observation stages. All evaluations were conducted in a simulated environment, including scenarios on flat plates and spherical tank-shaped steel surfaces. The proposed method maintained a maximum localization error within 5 cm in both linear and closed-loop trajectories and achieved over 30% improvement in horizontal accuracy compared to baseline EKF-based approaches. The system exhibited consistent localization performance across varying surface geometries, providing technical support for robotic operations on large steel infrastructures. Full article

Parkinson’s disease (PD) is a disorder that causes a decrease in motor skills. Among the symptoms that have been observed, the most significant is the occurrence of Freezing of Gait (FoG), which manifests as an abrupt cessation of walking. This study investigates the impact of spatiotemporal gait parameters using wearable inertial measurement units (IMUs). Notably, 30 PD patients (15 with FoG, 15 without) and 20 healthy controls were enrolled. Gait data were acquired using two foot-mounted IMUs and key parameters such as stride time, gait phase distribution, cadence, stride length, speed, and foot clearance were extracted. Results indicated a tangible decline in motor abilities in PD patients, especially in those with FoG. Differences were observed in the segmentation of gait phases, with diminished swing phase duration observed in patients, and in the diminished spatial parameters of stride length, velocity, and foot clearance. Additionally, to validate the results, the accuracy of IMU-derived clearance measurements was validated against an optoelectronic system. While the IMUs accurately detected maximum points, the minimum clearance showed a higher measurement error. These findings support the use of wearable IMUs as a reliable and low-cost alternative to laboratory systems for the assessment of gait abnormalities in PD. Moreover, they highlight the potential for early detection and monitoring of FoG in both clinical and home settings. Full article

Human Activity Recognition (HAR) using wearable sensors is an increasingly relevant area for applications in healthcare, rehabilitation, and human–computer interaction. However, publicly available datasets that provide multi-sensor, synchronized data combining inertial and orientation measurements are still limited. This work introduces a publicly available dataset for Human Activity Recognition, captured using wearable sensors placed on the chest, hands, and knees. Each device recorded inertial and orientation data during controlled activity sessions involving participants aged 20 to 70. A standardized acquisition protocol ensured consistent temporal alignment across all signals. The dataset was preprocessed and segmented using a sliding window approach. An initial baseline classification experiment, employing a Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) model, demonstrated an average accuracy of 93.5% in classifying activities. The dataset is publicly available in CSV format and includes raw sensor signals, activity labels, and metadata. This dataset offers a valuable resource for evaluating machine learning models, studying distributed HAR approaches, and developing robust activity recognition pipelines utilizing wearable technologies. Full article

The growing proportion of older adults in the population necessitates improved methods for assessing functional recovery. Objective, continuous monitoring using wearable sensors offers a promising alternative to traditional, often subjective assessments. This study aimed to investigate the utility of inertial measurement unit (IMU)-based data, combined with deep learning, to predict postoperative mobility, activities of daily living, and discharge destination in older adults following surgery. Data from the SURGE-Ahead project was analyzed, involving 39 patients (mean age 79.05 years) wearing lumbar IMU sensors for up to five postoperative days. Deep learning models (TabPFN) were applied and validated using leave-one-out cross-validation to predict the Charité Mobility Index (CHARMI), the Barthel Index, and discharge destination. The TabPFN model achieved R² values of 0.65 and 0.70 for predicting CHARMI and Barthel Index scores, respectively, with moderate to strong agreement with human assessments (weighted kappa ≥ 0.80). Discharge destination was predicted with an accuracy of 82%. The z-channel IMU data and parameters related to walking bouts were most predictive of outcomes. IMU-based data, combined with deep learning, demonstrates potential for automated functional assessment and discharge decision support in older adults following surgery. Full article

Posture, physical activity, and sleep have been shown to be linked to many health issues but are difficult to assess in laboratories, especially in terms of long-term patterns. Worn on the body, inertial measurement units (IMUs) measure motion and have shown promise for longitudinal measurements of these phenomena, but the repeatability of their measurements in daily life has not been extensively characterized. This study assessed the repeatability of measures of spine posture and movement in a set of standardized tasks in the lab versus those performed at home using IMUs. We also evaluated issues that impact data quality for real-world measurements. The results showed moderate repeatability in the range of spinal motion assessed during the tasks (ICC = 0.67). In contrast, the absolute angles of the spine (such as the starting posture) were more variable and more difficult to estimate. The estimation of the reference posture was identified as a key factor. Five methods to estimate the reference posture were compared, and the use of a composite set of standardized tasks performed best (ICC = 0.72 ± 0.17). Additional studies and cross-validation with other sensors are needed to draw stronger conclusions about the optimal methodology. For measurements of daily life over 2 days, magnetic interference had a major impact on the data quality, affecting 43% of all data analyzed. Metrics were developed to assess data quality and strategies are proposed to improve repeatability in future work. Full article