Search Results (4,200)

Background: Physical activity is a well-established modifiable lifestyle factor associated with reduced cancer risk; however, the optimal weekly volume of moderate-to-vigorous physical activity (MVPA) for cancer prevention, particularly when assessed using objective measures, remains unclear. Most existing evidence relies on self-reported physical activity, which may introduce measurement bias and obscure accurate dose–response relationships. Methods: We analyzed data from UK Biobank participants with valid accelerometer measurements to quantify habitual MVPA. Observational associations between MVPA and incident cancer were evaluated using multivariable Cox proportional hazards regression and restricted cubic splines. One-sample Mendelian randomization (MR) analyses, including both linear and nonlinear approaches, were conducted to evaluate potential causal associations and explore possible dose–response patterns. Results: Higher MVPA was associated with lower total cancer risk (HR 0.971, 95% CI 0.954–0.988, p = 0.001). Consistent associations were observed for several site-specific cancers, particularly lung, colorectal, breast, kidney, and bladder cancer. MR analyses supported a directionally consistent association between genetically predicted MVPA and lower total cancer risk (HR 0.977, 95% CI 0.962–0.992, p = 0.002). Nonlinear MR analyses suggested a potential nonlinear association, with lower cancer risk observed at a model-derived exploratory point of approximately 5 h of weekly MVPA. Conclusions: These findings provide supportive evidence that higher accelerometer-measured MVPA is associated with lower total cancer risk and contribute to a better understanding of the dose–response relationship between MVPA and cancer incidence. Full article

Precise monitoring of vibration signals is crucial for early fault warning and localization in industrial applications. Traditional electromagnetic accelerometers are often unsuitable for harsh environments characterized by high temperatures, high pressures, and strong electromagnetic fields. Fiber Bragg grating (FBG) accelerometers have become a major research topic in this field due to their unique advantages, including resistance to high temperature and pressure, immunity to electromagnetic interference, and ease of wavelength division multiplexing. This paper provides a systematic review of FBG accelerometers, covering their fundamental principles, classification, performance enhancement strategies, and applications. We focus on reviewing the research progress of FBG accelerometers from two main aspects, single-axis and multi-dimensional vector types, and offer an outlook on future development to provide a reference for the research and application of FBG accelerometers. Full article

(1) Background: Physical fatigue, either in short anaerobic exercises or in aerobic ones, affects locomotion patterns. Those effects, if consistently observed, may function as fatigue proxies. The present study focuses on the regularity of the pseudo-periodic acceleration patterns measured by a wearable sensor. Studies during laboratory anaerobic tasks on healthy subjects and on persons with multiple sclerosis during 6 min walking tests demonstrated that regularity decreases with fatigue. This study’s objective is to verify if the gait regularity during an unsupervised endurance aerobic walking task progressively decreases in healthy subjects. (2) Methods: Ten healthy male adults, not competitive recreational athletes, equipped with an accelerometer, participated in a non-competitive 100 km walk in about 24 h. (3) Results: Eight participants took from about 22 to 25 h to complete the task. Two did not finish. The trend of locomotion regularity (on average −6.3%, p < 0.001, effect size 1.41) was negative for all the participants. The gait speed decrease, in all the participants, explained less than 20% of the regularity decrease. Other outcome indices, such as that related to cadence, did not provide unique trends. (4) Conclusions: Regularity decrease is associated with fatigue in submaximal locomotor efforts; due to the experimental group limitations in size and composition, further studies should extend regularity assessments to women, and to persons with neuromuscular disabilities or attending walking rehabilitation. Full article

Accurate assessment of winding condition for power transformers is critical for ensuring the stable operation of modern power systems. Vibration signal has been regarded as an effective and promising evaluator for winding diagnosis. While on-line vibration monitoring offers the continuous, non-invasive and in-service assessment for winding condition, establishing precise correlations between the variable vibration patterns and specific winding condition remains challenging. To this end, an off-line vibration response analysis (VRA) technique was presented in the paper. Specifically, vibration frequency response (VFR) curves, indicating the winding response, were first obtained when the transformer was excited by the developed vibration response testing system, consisting of constant current variable-frequency power supply, intermediate transformer, accelerometers, data acquisition, control and analysis system. The VFR curves were then quantitatively and comprehensively described through four kinds of correlation indices. Finally, hierarchical integration strategy was proposed to aggregate those indices into quantitative criterion for condition assessment. The proposed method was validated on a real transformer under both normal and fault conditions, demonstrating superior performance. Notably, a 10% decrease in the evaluation criterion indicates an incipient winding looseness, while a reduction of 25% or more suggests severe looseness, prompting timely maintenance recommendations. Full article

Orthosis compliance monitoring provides insights into effective orthosis design and user wear time. Frequently, patient reports of orthosis use are subjective and often result in overestimation of compliance. Therefore, a tool to objectively observe whether patients wear their orthoses as instructed is vital. This study assessed the real-world practicality of using an objective compliance monitoring device with a hand orthosis. A device consisting of a pressure sensor and accelerometer was tested by ten healthy volunteers who wore a hand orthosis daily and completed a diary of their wear time and activities for a week. Sensor data obtained from the compliance monitoring device were analysed to discern each user’s orthosis wear time. Differences between estimated wear time and actual wear time were insignificant. Pressure-based wear time estimations had a specificity of 99.3 ± 0.7% and a sensitivity of 80.3 ± 19.2%, whilst acceleration-derived estimations had a specificity of 94.5 ± 6.4% and a sensitivity of 73.2 ± 15.8%. This study demonstrated that orthosis compliance can be monitored outside the laboratory, and, furthermore, this device offers insights into the intensity and frequency of a user’s activities and has the future potential to monitor orthosis fit and forces applied to affected joints using pressure. Full article

Wearable technologies offer practical opportunities for assistive communication and educational support in introductory sign language learning. This paper presents an IoT-enabled smart glove for deterministic static sign language recognition over a bounded vocabulary of 15 isolated static gestures, comprising digits (0–9) and five vowel handshapes (A, E, I, O, U). The system is intended for foundational static gesture and posture practice and is not designed or validated for dynamic gestures, coarticulated signing, continuous sign language recognition, or sentence-level translation. The prototype integrates five 2.2-inch (55.9 mm) resistive flex sensors and an MPU6050 3-axis accelerometer, performs acquisition, exponential moving average filtering, user-specific calibration, normalization, and deterministic classification on a NodeMCU ESP32 board, and transmits selected processed variables to Arduino Cloud through MQTT for remote monitoring. A 10 s calibration routine maps user-specific open-hand and closed-fist responses into normalized flex-sensor ranges, allowing the same deterministic rule structure to operate across participants without model retraining. Experimental evaluation with 10 healthy adult participants aged 20–41 years (mean age: 27 years), all familiar with sign language and all providing written informed consent, produced a balanced dataset of 1500 labeled steady-state sensor vectors. The class-averaged recognition rate was 92.8%, and leave-one-subject-out validation produced a subject-wise accuracy of $92.80\pm 2.03\%$, with individual participant accuracies ranging from 90.00% to 96.00%. The local embedded processing pipeline required less than 2 ms per cycle, the complete path including MQTT visualization produced approximately 150 ms end-to-end latency, and the device operated for up to 14 h using a 3.7 V, 1000 mAh Li-Po battery. The results indicate that calibrated deterministic sensor fusion can provide a low-cost, low-latency, edge-executed solution for bounded static sign-language gesture learning tasks while maintaining stable short-term subject-wise performance under controlled experimental conditions. Full article

This article introduces the current status of the 0.18-micron CMOS MEMS foundry service platform provided by the Taiwan Semiconductor Research Institute (TSRI), extensively covering the CMOS MEMS components that it has supported in development and fabrication. It also attempts to expand the foundry service scope to the broader categories of capacitive sensors and electrostatic actuators. On the one hand, for fabless MEMS component designers, TSRI currently directly allows the design of two types of components: flow sensors with uniformly perforated membranes and actuators with comb-shaped interdigital electrodes. This service also includes tape-out and wire bonding packaging procedures, following procedures similar to those used by general IC designers. On the other hand, this article specifically presents a clear and feasible approach for MEMS designers equipped with simple wet-etching facilities and a clear and feasible approach to develop further CMOS MEMS components such as capacitive pressure sensors, accelerometers, micro mirrors, and scratch drive actuators with minimal post-processing and chip packaging steps. This work provides a practical CMOS-MEMS design and post-processing guideline for extending the current TSRI foundry platform toward capacitive sensing and electrostatic actuation applications with minimal additional fabrication complexity. Full article

Accelerometer sensors and artificial intelligence (AI) are reshaping automated behavior monitoring in precision livestock management, yet their joint deployment on extensive rangelands is constrained by energy and bandwidth budgets. Low-Power Long-Range Wide-Area Network (LoRaWAN) collars address these constraints by compressing the raw tri-axial signal on the device into a single scalar per reporting interval, the Motion Index (MI). This onboard compression preserves enough signal to separate active behaviors but discards the per-axis and frequency content that fine-grained classification typically relies on. On a dataset of 9222 labeled observations from 24 cows across four breeds, MI distinguishes walking from grazing reliably but fails to separate ruminating from resting; both correspond to a stationary animal and yield near-zero, statistically indistinguishable distributions. Earlier MI-only models reached only about 65% four-class accuracy, and ruminating was commonly merged into resting. We show that much of this loss can be recovered by treating the MI stream as a time series. Session-aware lag features, rolling statistics, and an autoregressive previous-behavior feature lift four-class macro-F1 from 0.647 to 0.94, with per-class F1 of 0.95 for ruminating and 0.92 for resting (and at least 0.92 for every behavior). In autonomous deployment the previous behavior must be predicted rather than observed; for this setting we add a Viterbi sequence-decoding step that combines the classifier’s per-step outputs with a learned behavior-transition model, recovering a substantial part of the ruminating signal from the activity stream alone while keeping walking and grazing reliable. The gain is consistent across seven classifiers and four genetically distinct breeds, indicating that it is driven by the features rather than by a specific model. Full article

Controlling macro-geometrical errors in the dry drilling of ductile cast iron remains a critical challenge for sustainable and cost-efficient automotive component manufacturing. This paper investigates the influence of cutting speed (v_c) and feed per revolution (f_n) on the dimensional and shape accuracy of holes drilled in EN-GJS-500-7 ductile cast iron using an HSS DIN 338 helical drill (Ø 11.8 mm, Ceratizit) on an AVIA VMC800 CNC milling centre. A one-factor-at-a-time (OFAT) experimental design was applied: the feed effect was evaluated at v_c = 10 m/min with f_n ∈ {0.10, 0.15, 0.20} mm/rev, while the speed effect was evaluated at f_n = 0.20 mm/rev with v_c ∈ {10, 25, 30} m/min. Cutting forces, torques, and vibration accelerations were recorded using an HBM MSC 10 transducer and a PCB 356A01 tri-axial accelerometer. Hole geometry was assessed on a Zeiss Contura G2 coordinate-measuring machine (CMM), and surface texture was evaluated with a TOPO 01P contact profilometer. The expanded measurement uncertainty (k = 2) was estimated based on duplicate test specimens. All drilled holes fell within the IT12 dimensional tolerance (PN-EN 22768-1:1999 grade c), with diameter oversizes ranging from +0.26 mm to +0.46 mm relative to the nominal bore. Cutting speed was identified as the dominant factor affecting both diameter oversize and cylindricity, which increased by 60% (from 0.10 to 0.16 mm) as v_c rose from 10 to 30 m/min. Vibration accelerations increased nonlinearly between v_c = 25 and 30 m/min (by a factor of 2.5×), indicating an approach to a structural resonance condition. The lowest surface roughness (R_a = 6.6 µm) was obtained at v_c = 25 m/min. These findings establish clear physical baselines for tool deflection limits, demonstrating that managing dynamic process stability is vital for optimising macro-geometrical accuracy in the dry machining of cast iron alloys. Full article

The integration of sensor technology and artificial intelligence is revolutionizing athletic training. This paper presents a novel cost-effective smart table tennis racket embedded with a nine-axis inertial measurement unit (IMU) for real-time stroke classification directly on the device. Unlike systems that are reliant on external computation, our approach leverages Tiny Machine Learning (TinyML) to deploy a customized Convolutional Neural Network (CNN) model onto a microcontroller unit (STM32F7), enabling real-time inference at the edge. The system captures accelerometer and gyroscope data, which is automatically segmented via a recursive algorithm and classified into six fundamental strokes (e.g., forehand/backhand stroke, pull, and chop) or a non-swing state. The classified results are wirelessly transmitted to a computer application for real-time feedback. Experimental results with actual players demonstrate that the optimized CNN model achieves an average classification accuracy of 98.3% in controlled tests and over 94% in mixed-stroke scenarios, validating the system’s high accuracy and robustness. This work exemplifies the practical implementation of an end-to-end intelligent sensor system, highlighting the potential of TinyML to enable advanced, low-power motion analysis in sports. Full article

Introduction: Biological invasions are a major driver of biodiversity loss, particularly in freshwater ecosystems. The Iberian Peninsula, a hotspot of endemic diversity, is increasingly threatened by invasive predatory fish, which may exert higher predatory rates under warmer environmental conditions, disrupting/endangering native fish communities. One such species is the European catfish (Silurus glanis), a large and voracious apex predator. Despite growing research, most telemetry studies have focused on lentic systems, limiting our understanding of its behaviour in lotic environments. Moreover, high-resolution biologging approaches remain largely unexplored. Objective: This study aims to characterize the habitat use and activity patterns of European catfish in a non-native lotic section of the lower Tagus River, and to identify key environmental drivers shaping its predatory behaviour. Methodology: Adult individuals were tagged with radio telemetry transmitters equipped with temperature, pressure (depth), and 3D-accelerometer archival sensors. A preliminary controlled experiment established activity thresholds to classify behaviours. Ten adult fish were then actively tracked over one year, combining spatial data with high-resolution biologging. Habitat use and activity patterns were analyzed across seasonal and circadian scales. Generalized Additive Models (GAMs) were used to assess the effects of environmental variables on activity levels and depth use, while Hurdle models were applied to identify the environmental drivers influencing the occurrence and frequency of burst activity events (predatory behaviour proxies). Results: Fish displayed strong site fidelity, frequently using structured habitats near riverbanks. European catfish also showed clear seasonal and circadian patterns in habitat use and activity, occupying deeper habitats in winter and shallower areas in warmer seasons. Activity occurred year-round, increasing in spring and summer and peaking at dusk, being influenced by temperature, river flow, season, and time of day. Burst activity occurred more often in spring and at dusk. Conclusions: This study unveils insights on European catfish behaviour in invaded lotic systems, highlighting consistent patterns linked to environmental conditions. These findings can support more targeted and effective management strategies for controlling this invasive species. Full article

Earthquake-induced landslides represent a critical threat to transportation infrastructure in tectonically active mountainous regions, particularly in tropical volcanic settings where weak, highly weathered geomaterials dominate. This study develops an integrated framework that directly links physically based seismic threshold modelling with real-time landslide monitoring and operational early warning. The approach is demonstrated in the Cugenang area of Cianjur Regency, West Java, Indonesia, which was severely impacted by the moment magnitude (Mw) 5.6 earthquake in 2022. Slopes composed of highly weathered pyroclastic deposits [Plasticity Index (PI) = 54–68%; porosity > 60%] exhibit low shear strength and high sensitivity to seismic loading. Limit equilibrium analysis using the Morgenstern–Price method that combines the influence of seismic loading and groundwater conditions suggests that a horizontal seismic coefficient (kh) of approximately 0.06, corresponding to a Peak Ground Acceleration (PGA) of about 0.12 gravitational acceleration (g), is a critical threshold for initial landsliding. This comparatively low threshold challenges commonly reported values and demonstrates that slope failure in tropical volcanic terrains can occur under moderate ground shaking, reinforcing the need for site-specific hazard characterisation. The derived thresholds are operationalised within a multi-sensor early warning system integrating Micro-Electro-Mechanical Systems (MEMS) accelerometers and inclinometer measurements. Three hazard levels—Normal (<0.06 g), Alert (0.06–0.12 g), and Emergency (≥0.12 g)are combined with deformation thresholds [<10 milimeter (mm), 10–30 mm, >30 mm] to capture progressive failure processes and minimise false alarms. By coupling geotechnical modelling and real-time monitoring, this study provides a transferable and scalable framework for enhancing infrastructure resilience in landslide-prone regions. Full article

Accelerometers embedded in consumer-grade smartwatches hold significant potential for health-related research applications, but their measurement reliability is often compromised. This limitation necessitates proper metrological characterization to ensure precision and consistency, particularly in health-related research contexts where reliable movement data are required. This study proposes a methodology for the simultaneous metrological characterization of multiple smartwatch accelerometers, enabling efficient and consistent bench-based measurement evaluation. The proposed methodology employs a seismic table to generate controlled vibrations within a frequency range of 1–8 Hz and acceleration amplitudes between 1 and 4 m/s². Five commercial smartwatch units were tested, collecting acceleration data at sampling rate of 50 Hz. A reference accelerometer was used to assess the accuracy of smartwatch measurements, with errors and uncertainties quantified following ISO standards. Results demonstrate that simultaneous bench-based evaluation allows consistent comparison of measurement performance across devices while reducing the time required for the process. The analysis highlights variations in frequency response and amplitude accuracy across different smartwatch units, emphasizing the need for systematic metrological characterization when considering the future use of smartwatches in health-related research studies involving wearable movement monitoring. Full article

Wearable inertial sensing has considerable potential for process-level analysis of upper-limb function, but further evidence is needed to understand how it can be applied within ecologically structured immersive virtual reality (VR) tasks. Most VR-based functional assessments rely primarily on outcome-level indicators, such as task completion time, success rate, or error count, which may not fully capture how a task is executed. This exploratory study investigated whether wearable IMU signals collected during an immersive VR sushi-making task could support binary detection of a core upper-limb manipulation phase and provide additional information about task execution beyond global performance outcomes. A total of 45 participants contributed usable motion recordings for this study, with five Xsens DOT sensors placed on the hands, forearms, and waist. Three signal modalities were analysed, including acceleration (ACC), gyroscope angular velocity (GYR), and Euler angles. The downstream recognition problem was formulated as a binary classification task (Placing vs. Non-Placing), and a self-supervised learning (SSL) pretrain–fine-tune strategy was evaluated against conventional machine learning and from-scratch deep learning baselines using five subject-wise validation splits. The strongest overall performance was achieved with hand-mounted accelerometer signals, with LeftHand–ACC achieving a Macro-F1 of $0.712\pm 0.128$ and RightHand–ACC achieving $0.679\pm 0.118$. Under both hand-ACC settings, SSL fine-tuning showed higher mean Macro-F1 than the Balanced Random Forest baseline and the same deep architecture trained from scratch. Recognition performance varied substantially across sensor locations, signal modalities, and task segments, with distal upper-limb sensors generally outperforming waist-based configurations. Cross-age analyses further showed that within-cohort and cross-cohort performance did not fully align, indicating sensitivity to age-related distribution shift. Beyond classification, Log Dimensionless Jerk (LDLJ) derived from the Placing action showed a significant positive association with Cognitron motor control time cost ($r=0.636$, $p<0.001$). These findings suggest that wearable IMU sensing can provide preliminary process-level information during immersive VR functional tasks, including task-phase detection, sensing-configuration comparison, cross-cohort generalisation assessment, and exploratory motion-quality analysis. The results should be interpreted as evidence of feasibility rather than as a mature biomechanical or clinical assessment model. Full article

Inertial Measurement Units (IMUs) enable portable, multibody motion capture in diverse environments beyond the laboratory, making them a desirable choice for diagnosing mobility disorders and supporting rehabilitation in clinical or home settings. However, challenges associated with IMU measurements, including magnetic distortions and errors due to integration drift, complicate their broader use for motion capture. In this work, we propose a tightly coupled motion-capture approach that directly integrates IMU measurements with multibody dynamic models via an iterated extended Kalman filter to simultaneously estimate the system’s kinematics and kinetics. By enforcing the complete multibody system dynamics and utilizing only accelerometer and gyroscope data, our method accurately estimates joint kinematics and kinetics. Our algorithm is designed to fuse different sensor data, such as optical motion-capture measurements and joint torque readings, to further enhance estimation accuracy. We validated our approach using highly accurate ground-truth data from a 3-degree-of-freedom pendulum and a 6-degree-of-freedom collaborative robot. We demonstrate a maximum root-mean-square difference of 3.75° in the pendulum’s computed joint angles with respect to the marker motion-capture inverse kinematics. For the robot, we observed a maximum joint angle root-mean-square difference of 3.24° with respect to the joint encoders, while the maximum joint angle root-mean-square difference of the optical motion-capture inverse kinematics with respect to the encoders was 1.16°. With regard to kinetic estimates, we report a maximum joint torque root-mean-square difference of 3.02 Nm in the pendulum with respect to the marker motion-capture inverse dynamics and 4.27 Nm in the robot relative to its joint torque sensors. Full article