Search Results (92)

Accurate positioning is essential for inspection robots in caged chicken houses, where long straight corridors, sparse textures, and repetitive structures challenge conventional methods. This paper proposes CVIWM (Coupled Visual-Inertial-Wheel Odometry with Markers), a tightly coupled state estimation method that fuses visual, inertial measurement unit (IMU), wheel odometry (WO), and fiducial marker observations within a factor graph optimization framework. Wheel odometry preintegration suppresses IMU horizontal drift and provides absolute scale, while sparse AprilTag markers (10 m spacing) periodically reset accumulated errors. Experiments in an 80 m corridor of a commercial caged chicken house at 0.116 m/s and 0.232 m/s showed that CVIWM achieves average positioning errors of 2.402 cm and 3.253 cm. This high precision ensured reliable image acquisition (image shift $<83$ pixels), enabling 95.7% dead hen detection and 98.9% egg detection accuracy. CVIWM offers a low-cost, easy-to-deploy, high-accuracy solution for automated poultry house inspection, supporting smart livestock farming. Full article

Research on human motion representation commonly investigates portable, wearable, and ergonomic sensing systems. Cameras, infrared sensors, and inertial measurement units (IMUs) are widely used to reproduce and validate human movement. Known limitations persist, including increased error during slow movements, the gimbal lock effect in Euler space, and the requirement for one sensor per joint. The objective of this work is twofold: first, to characterize the measurement accuracy of a commercial IMU sensor (BWT901BLE) under controlled conditions using a fixed-arm pendulum model that replicates the single-degree-of-freedom planar kinematics of elbow flexion–extension, comparing angular position, angular velocity, and angular acceleration outputs against a video-based reference system; and second, to describe and publish a complete data processing pipeline—from raw sensor readings to real-time biomechanical motion visualization within OpenSim—demonstrated through upper limb motion recordings from 6 participants, whose data were used to generate motion files and estimate muscle fiber lengths and activation patterns within OpenSim. Regarding sensor characterization, experiments compared sensor data against the video-based reference. The inter-sensor angular position mean error was 0.765° (100 Hz) and 0.445° (200 Hz); angular velocity mean error was 0.124°/s (100 Hz) and 0.277°/s (200 Hz). Direct Euler angle measurements outperformed quaternion-to-Euler conversion (mean RMSE 5.69° vs. 53.1° at 100 Hz; 5.08° vs. 41.8° at 200 Hz). Angular velocity showed the highest agreement with the video-based reference (mean RMSE 0.60 rad/s at 100 Hz and 0.43 rad/s at 200 Hz; mean R = 0.982 and 0.991). Raw accelerometer output showed negligible correlation with the video-based angular acceleration reference (mean R ≈ 0.00–0.05); however, acceleration derived from angular velocity differentiation achieved high accuracy (mean RMSE 4.43 rad/s² at 100 Hz and 3.06 rad/s² at 200 Hz; mean R = 0.976 and 0.989). Regarding the OpenSim integration, the real-time visualization pipeline achieved an effective frame rate of 40–50 fps with an estimated end-to-end latency of 35–50 ms, and the recorded motion data were used to estimate muscle fiber lengths and activation patterns through OpenSim’s analysis tools. These findings confirm that angular velocity is the most reliable output of this sensor class. Full article

This study provides a comparative performance evaluation of commercial Precise Point Positioning Real-Time Kinematic (PPP-RTK) and public Network RTK (NRTK) services for vehicle-based positioning in urban and suburban environments. Using low-cost u-blox ZED-F9 receivers, the research assesses the accuracy, availability, and robustness of the u-blox PointPerfect service against a regional NRTK network across diverse real-world scenarios, including high-speed highway conditions and signal-challenging urban corridors. The experimental framework utilizes a rigid-bar setup for high-precision ground-truth validation and incorporates an independent vertical accuracy assessment against a LiDAR-derived digital elevation model (DEM). The results demonstrate that all tested configurations achieve decimeter-level accuracy. Notably, the integration of PPP-RTK with an inertial measurement unit (IMU) delivers performance nearly equivalent to NRTK, effectively mitigating vertical biases and ensuring positioning continuity in GNSS-denied areas such as tunnels. These results confirm that low-cost GNSS solutions, when paired with modern augmentation services and IMU integration, can meet the stringent demands of mass-market applications like Cooperative Intelligent Transport Systems (C-ITS) and autonomous mobility. Full article

Precise 3D positioning in GPS-denied environments is a critical enabler of autonomous robotics, industrial automation, and smart logistics within the emerging cyber-physical landscape. This paper presents a distributed and reconfigurable architecture designed to benchmark and provide unified multimodal indoor localization for mobile edge nodes. Unlike rigid commercial solutions, our architecture employs a distributed, reconfigurable framework that allows the rapid interchange of Absolute Localization Methods (UWB, External RGB-D Vision) and Relative Localization Methods (Inertial Odometry, Visual Odometry). We evaluate these modalities individually and in hybrid configurations using a custom low-cost mobile edge node. Experimental results in a controlled environment demonstrate that while all-optical systems offer high precision, a cost-effective fusion of Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) data provides a robust balance of accuracy and reliability. Conversely, we identify significant limitations in monocular visual odometry within feature-poor indoor spaces. The developed platform serves as a reproducible foundation for researchers to prototype hybrid localization algorithms and assess the trade-offs between hardware cost and operational accuracy within complex cyber-physical ecosystems. Full article

Background: The use of inertial measurement units (IMUs) in the Timed Up and Go (TUG) test enables the quantitative assessment of functional performance and mobility. It allows for the determination not only of the total test completion time, but also of the durations of individual phases, as well as the derivation of spatiotemporal gait parameters and turning velocity. The aim of this review article was to compile parameters of the instrumented Timed Up and Go (iTUG) test and to identify the parameters most commonly analyzed in populations of healthy adults, older adults, and patients with neurological disorders. Methods: A systematic literature search was conducted in the PubMed, Scopus, and ScienceDirect databases. The authors included studies in which commercial IMUs were used during the TUG test and quantitative parameters were analyzed. Methodological quality was assessed using the JBI Critical Appraisal Checklist for cross-sectional studies. Results: A total of 36 studies were included in the review. Only those disease entities represented by at least four studies were included in the tabular analysis. The study presents results for a total of 1268 individuals, including 192 healthy adults, 514 older adults, 230 patients with multiple sclerosis (MS), and 332 patients with Parkinson’s disease (PD). The analysis showed that temporal parameters, particularly the total test duration and the durations of individual phases, were the most commonly reported across all populations. Conclusions: Turning-related parameters were analyzed frequently, whereas spatiotemporal parameters were assessed less often. The results indicate a lack of standardization both in the selection of iTUG parameters as well as in the measurement methods and systems used. Full article

Inertial measurement units (IMUs) present promising avenues for performance diagnostics in skateboarding, yet systematic validation of their accuracy and applicability remains limited. This study validates the commercially available Spinnax Freak IMU system in the context of skateboarding, with a focus on selected trick detection and classification, distance measurement, maximal horizontal speed, maximal vertical height of the skateboard and airtime during a jump trick. A total of 23 skateboarders (4 females, 19 males; 27.4 ± 10.9 years) participated in this study. Validation methods included comparisons with established reference systems such as laser ranging for maximal horizontal speed (LAVEG), 2D video analysis for maximal vertical height of the skateboard (Kinovea), light barrier measurements for airtime detection (OptoJump Next), and a fixed metric reference (10 m) for rolling distance measurements. The evaluation was supported by statistical analyses including mean absolute error (MAE), root mean-square error (RMSE), mean absolute percentage error (MAPE), t-tests, Bland–Altman plots, linear regression, and ICC(3,1). The Spinnax Freak system demonstrated high validity in detecting trick events and in providing distance measurements that were statistically equivalent to the reference. Trick classification, maximal horizontal speed, maximal vertical height of the skateboard and airtime showed substantial errors, indicating that these outputs are not reliable for biomechanical interpretation at this point. These findings highlight both the potential and the current constraints of single-sensor setups for field-based motion capture in skateboarding. Future developments should prioritize algorithmic refinement, improved temporal resolution, and optimized event classification to enhance measurement accuracy and expand applicability in biomechanical analysis and automated training documentation in skateboarding. Full article

Wearable systems based on inertial measurement units (IMUs) have attracted considerable interest in recent years in the field of gait analysis. However, most gait studies using such devices have been conducted in laboratory rather than clinical settings. This study evaluated a commercially available IMU-based insole system in two cohorts: a clinical group (59 ± 18, years) recruited from podiatry clinics and a non-clinical group (28 ± 7, years) recruited from a university with no reported complaints. Participants wore the IMU-based device and performed treadmill walking (clinical group) and overground walking (non-clinical group). Spatiotemporal parameters were compared between groups using statistical analyses included the Shapiro–Wilk test, Mann–Whitney test, and Welch’s t-tests for non-bilateral data, and a two-factor linear mixed-effects model estimated by restricted maximum likelihood (REML) for bilateral spatiotemporal parameters to evaluate group, foot-side, and interaction effects. Ten of the twenty-two spatiotemporal parameters showed significant group differences, with statistical significance observed in at least one foot for parameters measured bilaterally. The observed differences may reflect a combination of clinical characteristics, age-related effects, and walking environment influences. Findings are discussed in relation to potential biomechanical mechanisms, factors influencing results and the clinical utility of IMU systems. Future research should investigate specific foot conditions under standardized walking conditions with age-matched cohorts. Full article

The vehicular energy consumption, primarily determined by the vehicle’s characteristics, exhibits significant variations influenced by driving behavior, traffic, and road attributes, with repercussions for emissions. This paper presents experimental results from real-traffic runs to characterize the relationship between fuel consumption and these factors. Data on consumption, performance, and kinematics of a light-duty vehicle were obtained using low-cost devices, including an On-Board Diagnostics (OBD) scanner, a unit integrating an Inertial Measurement Unit (IMU) and a Global Positioning System (GPS) receiver. The data allowed distinguishing consumption patterns between two distinct scenarios: a collector road stretch with deteriorated pavement and an express road stretch with lower surface roughness. Relevant association was identified between fuel consumption and factors such as discrete pavement anomalies and variables related to driving and traffic. Moderate correlations were observed with slope, and weaker ones with pavement roughness. Regarding the regression analysis, results identified acceleration and engine speed as the primary operational determinants of fuel consumption, with road grade emerging as the dominant geometric constraint across all scenarios. The results reveal relevant associations between fuel consumption and road, driving, and traffic-related factors while simultaneously demonstrating a robust and replicable experimental methodology based on commercially available sensing devices for real-traffic energy and emission assessments. Full article

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

The development of control strategies for autonomous vehicles requires a reliable and cost-effective validation approach. In this context, testbeds enabling repeatable experiments under controlled conditions are gaining relevance. Scaled vehicles have proven to be a valuable alternative to full-scale or simulation-based testing, enabling experimental validation while reducing costs and risks. This work presents a 1:5 scale modular vehicle platform, derived from a commercial Radio-Controlled (RC) vehicle and adapted as experimental testbed for control strategy validation and vehicle dynamics studies. The vehicle features an electric powertrain, operated through a Speedgoat Baseline Real-Time Target Machine (SBRTM). The hardware architecture includes a high-performance Inertial Measurement Unit (IMU) with embedded Global Navigation Satellite System (GNSS). An Extended Kalman Filter (EKF) is implemented to enhance positioning accuracy by fusing inertial and GNSS data, providing reliable estimates of the vehicle position, velocity, and orientation. Two path-following algorithms, i.e., Stanley Controller (SC) and the Linear Quadratic Regulator (LQR), are designed and integrated. Outdoor experimental tests enable the evaluation of tracking accuracy and robustness. The results demonstrate that the proposed scaled testbed constitutes a reliable and flexible platform for benchmarking autonomous vehicle controllers and enabling experimental testing. Full article

Virtual reality (VR) welding simulators provide safe and cost-effective training environments, but precise torch tracking remains a key challenge. Current commercial systems are limited in accurate bead simulation and posture feedback due to tracking errors of 3–10 mm, while external motion capture systems offer high precision but suffer from high cost and installation complexity issues. Therefore, a new approach is needed that achieves high precision while maintaining cost efficiency. This paper proposes an IMU-SLAM fusion-based tracking algorithm. The method combines Inertial Measurement Unit (IMU) data with visual–inertial SLAM (Simultaneous Localization and Mapping) for sensor fusion and applies a drift correction technique utilizing the periodic weaving patterns of the welding torch. This achieves precision below 5 mm without requiring external equipment. Experimental results demonstrate an average 3.8 mm RMSE (Root Mean Square Error) across 15 datasets spanning three welding scenarios, showing a 1.8× accuracy improvement over commercial baselines. Results were validated against OptiTrack ground truth data. Latency was maintained below 100 ms to meet real-time haptic feedback requirements, ensuring responsive interaction during training sessions. The proposed approach is a software solution using only standard VR hardware, eliminating the need for expensive external tracking equipment installation. User studies confirmed significant improvements in tracking quality perception from 6.8 to 8.4/10 and bead simulation realism from 7.1 to 8.7/10, demonstrating the practical effectiveness of the proposed method. Full article

Plastic greenhouses, which account for the majority of protected horticulture facilities in East Asia, are highly susceptible to wind-induced uplift failures that can lead to severe structural and economic damage. To address this issue, this study developed a low-power and low-cost wireless monitoring system applying the concept of structural health monitoring (SHM) to greenhouse foundations. Each sensor node integrates a MEMS-based inertial measurement unit (IMU) for attitude estimation, a LoRa module for long-range alert transmission, and a microSD module for data logging, while a gateway relays anomaly alerts to users through an IP network. Uplift tests were conducted on standard steel-pipe foundations commonly used in plastic greenhouses, and the proposed sensor nodes were evaluated alongside a commercial IMU to validate attitude estimation accuracy and anomaly detection performance. Despite the approximately 30-fold cost difference, comparable attitude estimation results were achieved. The system demonstrated low power consumption, confirming its feasibility for long-term operation using batteries or small solar cells. These results demonstrate the applicability of low-cost IMUs for real-time structural monitoring of lightweight greenhouse foundations. Full article

Monitoring electrocardiogram (ECG) and respiration continuously and non-invasively is essential for managing cardiopulmonary health. An effective wearable device can be used to regularly monitor key vitals, reducing the need for clinical visits. In this work, we propose a custom device for real-time continuous ECG by inkjet printed (IJP) dry electrodes and respiration monitoring by using a novel single 6-axis inertial measurement unit (IMU). The proposed system can extract the heart rate (HR) and respiration rate (RR) during static and dynamic postures. The respiration process implements a quaternion-based update and multiple filtering stages to estimate the signal. The custom device uses Bluetooth protocol to send the raw and processed data to a mobile application. The RR is investigated in stationary, i.e., sitting and standing, and dynamic, i.e., walking, running, and cycling, postures. The proposed device is evaluated with commercial Go Direct^® respiration belt from Vernier^® for RR and offers an overall accuracy of 99.3% and 98.6% for static and dynamic conditions, respectively. The wearable also offers 98.9% and 97.9% accuracy for HR measurements, respectively, in static and active postures when compared with the Kardia^® device. Furthermore, the device is assessed in an ambulatory monitoring setup in both indoor and outdoor environments. The low-power wearable consumes an average of only 7.4 mA of current during data processing. The device performs effectively and efficiently in both stationary and active states, offering a low complexity, portable solution for real-time monitoring. The proposed system can benefit from the continuous monitoring and early detection of pulmonary and cardio-respiratory health issues. Full article

Inertial Measurement Units (IMUs), which embed several sensors (accelerometers, gyroscopes, magnetometers) are employed by musicians and performers to control sound, music, or lighting on stage. In particular, wireless IMU systems in the performing arts require particular attention due to strict requirements regarding streaming sample rate, latency, power consumption, and programmability. This article presents a review of systems developed in this context at IRCAM as well as in other laboratories and companies, highlighting specificities in terms of sensing, communication, performance, digital processing, and usage. Although basic IMUs are now widely integrated into IoT systems and smartphones, the availability of complete commercial wireless systems that meet the constraints of the performing arts remains limited. For this reason, a review of systems used in performing Arts provides exemplary use cases that may also be relevant to other applications. Full article

Background: A single lumbar-mounted inertial sensor offers a practical alternative to optoelectronic systems for gait analysis, simplifying measurements and improving usability in the clinical field. However, its validity can be influenced by sensor placement and signal choice. This study aimed to develop and validate a novel algorithm for estimating spatiotemporal parameters using anteroposterior linear acceleration and angular velocity around the sagittal axis using a single inertial measurement unit (IMU) placed on the lumbar region. The proposed algorithm was validated comparing the parameters computed by the algorithm with the ones computed using a commercial wearable system based on a two-foot-mounted IMU configuration. Thirty healthy subjects underwent a 2 min walk test, and five spatiotemporal parameters were computed using the two methodologies. Study results showed that cadence and gait cycle time exhibited very high agreement, with only a small, statistically significant bias in cadence negligible for practical purposes. In contrast, swing, stance, and double-support parameters showed disagreement due to the presence of systematic proportional errors. This work introduces a novel algorithm for gait event detection and spatiotemporal parameter estimation, addressing uncertainties related to sensor placement, metric models, processing techniques, and signal selection, while avoiding synchronization issues associated with using multiple sensors. Full article