Search Results (4,120)

Background: Wearable sensors have gained increasing popularity as an objective method for remotely monitoring infant movement in naturalistic settings. Over the first year of life, infants generate a wide range of motions, from goal-directed to spontaneous movement. These include linear movements, such as kicks, and orientation changes, such as postural transitions. Many sensor processing pipelines emphasize capturing linear movements through movement-generated acceleration while focusing less on information about orientation embedded in the gravitational part of the data. Here, we introduce a complementary gravity-referenced approach that extracts the gravitational component of accelerometer signals to estimate limb orientation, extending the reliable quantification of rich and detailed aspects of infant movement. Infant orientation has demonstrated clinical relevance, including associations with later neuromotor outcomes, and it can be used to chart infant motor development, motivating the development of objective methods to quantify orientation from sensor data. Methods: Wearable sensors (Opal APDM) were used to longitudinally evaluate infant motor activity recorded in sessions conducted at 3, 6, 9, and 12 months of age. We extracted data from a 5 min segment that has simultaneous video recordings. From these datasets, applying the gravity-referenced method, we computed pitch, roll, and yaw, angles that collectively describe limb orientation. We then quantified orientation variability using axis-specific circular standard deviations (SDs) for pitch, roll, and yaw and a multi-axis composite measure based on generalized variance. Results: Axis-specific circular SDs for pitch, roll, and yaw, as well as the composite generalized variance, increased significantly from 3 to 12 months (p ≤ 0.01 for each metric). Composite variability was strongly associated with Mullen gross motor outcomes at 9 and 12 months of age (r = 0.55, p < 0.001). Conclusions: Overall, gravity-referenced pitch, roll, and yaw provide rich orientation features that increased as infants develop more postural transitions. Furthermore, the orientation features correlated with standardized measures of infant motor function. These orientation metrics can complement traditional linear kinematic measures and improve our ability to granularly track infant motor development in the first year of life. Full article

Dairy cows typically respond to stressors by altering their behavior, such as reducing eating time (ET) and rumination time (RT). Although declines in milk yield (MY) have been extensively studied, models to quantify perturbations in ET and RT are still lacking. This study adopts a smoothing approach to identify and characterize perturbations in MY, ET, and RT in response to the main primary stressors, heat stress (HS), lameness (L), and mastitis (M), while evaluating the influences of parity and stage of lactation. A total of 350 Italian Simmental cows were monitored in farms equipped with automatic milking systems and accelerometers. Within this population, cows with a lactation period of at least 150 days were selected. A double-curve smoothing model (λ = 100 and λ = 10,000) was applied to calculate response and recovery times and to quantify production and feeding behavior losses. The results indicate that L causes the longest (30.6 d and 28.8 d, respectively) perturbations for both MY and ET. While L caused the greatest loss in milk production (14.7 kg), HS resulted in the greatest losses regarding feeding behavior (ET: 175.2 min and RT: 210.3 min). In general, M had a lower impact, likely due to the timeliness of treatments. Primiparous cows showed faster responses to stress but slower recovery times compared to multiparous ones. However, multiparous cows exhibited greater total MY losses. The method proved effective for quantifying resilience and opens new perspectives in health monitoring, allowing for the identification of both economic loss and each animal’s capacity to cope with pathological and environmental events, improving the overall sustainability of the dairy farm. Full article

This study investigates the effectiveness of various feature extraction methods applied to vibration signals for the automatic detection of production defects in CNC (Computerised Numerical Control) milling machines. A dataset consisting of real-world data collected from CNC machines equipped with accelerometers was used. The objective of the study is to compare three main groups of techniques: time-domain analysis (TDA), frequency-domain analysis (FDA), and time–frequency-domain analysis (TFA). The findings indicate that basic TDA features lack the necessary sensitivity to accurately distinguish between Good Processing (GP) and Bad Processing (BP) states. Frequency-domain methods, such as the Fast Fourier Transform (FFT), median frequency calculation, and the Welch periodogram, provide better insights but still have limitations. The most effective results are obtained with TFA methods, particularly Empirical Mode Decomposition (EMD) and the Hilbert–Huang Transform (HHT), which reveal deeper signal characteristics. Following the feature optimisation studies, it was determined that a combination of four features—FMED, IMF2, IMF5 and WPT26—yielded the optimal performance, with an accuracy of 91.48%. The incorporation of a fifth feature resulted in information saturation within the model and did not improve performance. This study makes a novel contribution to literature by conducting an in-depth investigation into the most effective feature extraction and selection techniques for achieving robust discrimination between GP and BP productions using vibration signals in CNC milling processes. Conclusively, TFA features, supported by advanced signal processing, offer a strong basis for reliable, automated defect detection in CNC milling operations. Full article

This study presents a structured methodology for optimizing the placement and selection of accelerometer sensors for structural health monitoring in civil infrastructures. The approach integrates both ambient and forced vibration testing data, followed by a unified analysis of sensor energy distribution through singular value decomposition of the cross power spectral density. The energy associated with each sensor is normalized and decomposed into its vertical, longitudinal, and transversal components, allowing for detailed ranking and visualization across different structural elements such as the deck and supporting piers. A comparative analysis between the energy distributions obtained from ambient and forced vibrations is conducted to identify consistent sensor locations. The sensor configuration is then iteratively refined using a combination of global dynamic criteria and local spatial constraints to ensure both stability and optimal spatial distribution. The resulting framework enables the systematic design of sensor layouts that combine energy-based robustness with optimal spatial distribution across all three spatial components, while significantly reducing the number of required sensors, ensuring the preservation of damage detection capability and long-term structural health monitoring. Full article

Cats are among the most popular pets globa lly, yet little is known about how the home environment influences their behaviour. Most studies have focused on cats in shelters or research facilities, potentially limiting applicability to pet cats. This study combined behavioural data from cats in three housing conditions: indoor pet (n = 10), free-roaming pet (n = 18), and research (n = 8), collected in summer and winter. Eight behaviours were classified from collar-mounted accelerometer data using a validated machine learning model and analysed using generalised linear mixed models. Free-roaming pet cats were more active in summer than winter (3.9 ± 0.39% vs. 2.7 ± 0.33%; p < 0.001) and more active than both research (2.0 ± 0.36%; p = 0.004) and indoor pet cats (2.0 ± 0.36%; p < 0.001) in summer. Research cats spent more time lying (52.9 ± 2.03% vs. 36.9 ± 2.89%; p = 0.009) and eating (7.8 ± 0.41% vs. 2.4 ± 0.39%; p = 0.003) in winter than summer, whereas no seasonal differences in these behaviours were observed for pet cats. A bimodal daily activity pattern, with peaks around sunrise and sunset, was observed across housing conditions and seasons. These findings demonstrate that both housing and seasonal conditions influence domestic cat behaviour and should be considered when interpreting behavioural studies. Full article

This research presents a machine learning-based vibration signal acquired from aluminum grinding experiment for potential application in smart and intelligent manufacturing. The study addresses the challenges of traditional surface finishing quality inspection by integrating vibration sensing and support vector machine (SVM). A robot manipulator lab grinding experiment consist of a four-axis DOBOT Magician with a handheld cylindrical grinding tool attached on the end-effector of the DOBOT Magician. This customized lab grinding experiment was designed to perform consistent surface finishing experiment for different aluminum work coupon and time duration. Triaxial accelerometer was used to collect the vibration signal and to investigate the most relevant vibration signal direction (x, y, and z) to the surface quality prediction of the aluminum work coupon. The vibration signal was acquired via LabVIEW and NI data acquisition (DAQ) system. The vibration features were extracted and analyzed using Python programming in Google Colab. The SVM algorithm in Python (3.11 and 3.12) is used to classify surface roughness quality into coarse, medium, and fine categories based on the extracted vibration features. Vibration feature parameters such as root mean square (RMS), Peak to RMS, Skewness, and Kurtosis were also investigated to determined which feature pairs are most critical for effective surface roughness monitoring and prediction using SVM classification. The classification model achieved high accuracy across all three vibration axes (x, y, and z), with the z-axis yielding the most consistent results. The proposed system has potential applications in real-time surface quality prediction within smart manufacturing practices aligned with Industry 4.0 principles. Full article

We investigated the effects of manipulating visual information on motor control indicators during the Waiter’s Bow Test. The results suggested that visual information occlusion reduced the maximum flexion angles of the lumbar spine and upper lumbar region. Furthermore, subjects who tested negative under the open-eye condition tested positive under the closed-eye condition. Regarding muscle activity in the rectus abdominis and erector spinae muscles, it was suggested that this activity was not affected by visual information. These findings indicate that visual sensory feedback is one factor influencing lumbar motor control. The integration of electromyography and accelerometer systems in this study highlights the role of wearable sensor technologies in quantifying neuromuscular function in Bioengineering. By restricting visual information, a model for sensory reweighting can be established for the design of biofeedback systems, rehabilitation robotics, and assistive devices. The results of this study demonstrate how sensor-based evaluation and sensory manipulation can inform the engineering of diagnostic and therapeutic technologies for motor control assessment. Full article

This work presents the development of a wrist-worn wireless sensor system for high-accuracy indoor human zone tracking. The proposed system employs machine learning techniques to combine data from multiple sources, including a Received Signal Strength Indicator (RSSI) from wireless signals, three-axis acceleration, and three-axis angular velocity. A prototype wearable wireless sensor device was implemented using a SparkFun Thing Plus-XBee3 microcontroller supporting the Zigbee/IEEE 802.15.4 standard at 2.4 GHz, integrated with a six-degree-of-freedom IMU sensor (MPU-6050). Experiments using one wrist-worn sensor as a transmitter and one base station as a receiver were conducted in a two-story residential building environment covering three zones (i.e., staircase area, living room, and dining room) under static and dynamic test scenarios. Classification performances of 33 machine learning classifiers with different data feature groups and window sizes were evaluated. The results demonstrate the achievement of wrist-worn wireless sensor system development. The system exhibits high communication reliability with a packet delivery ratio (PDR) of 99.99% and can efficiently track data signals in real time. Results indicate that using only raw RSSI data achieves 75.0% accuracy in classifying human zones. However, when statistical RSSI features and accelerometer data fusion are applied, accuracies significantly increase to 98.7% (static scenario, wide neural network with a window size of 25) and 99.6% (dynamic scenario, Fine k-NN). These results demonstrate the system’s potential for indoor human tracking applications. Full article

Long-distance coal gangue slurry transportation pipelines are critical components of underground coal mine green backfilling systems, yet blockage failures severely threaten their safe and efficient operation. Existing distributed acoustic sensing (DAS)-based monitoring methods for such pipelines suffer from three key limitations: insufficient fixed-point quantitative accuracy, lack of verified blockage-specific characteristic indicators, and limited quantitative severity assessment capability. To address these gaps, this paper proposes a novel feature-level fusion monitoring method integrating DAS, fiber Bragg grating (FBG), and piezoelectric accelerometers for accurate blockage identification and quantitative evaluation in coal gangue slurry pipelines. A slurry pipeline circulation test platform with gradient blockage simulation (0% to 76.42%) and a synchronous multi-sensor monitoring system were developed. Through multi-domain signal analysis, three blockage-correlated characteristic frequencies were identified and cross-validated by synchronous multi-sensor data: 1.5 Hz (system background vibration), 26 Hz (blockage-induced fluid–structure resonance, verified by the Euler–Bernoulli beam theory with a theoretical value of 25.7 Hz), and 174 Hz (transient flow impact). The DAS phase change rate exhibited a unimodal nonlinear response to blockage degree, with the peak occurring at 40.94% blockage. On this basis, a sine-fitting quantitative inversion model was developed, achieving a high goodness of fit (R² = 0.985), and leave-one-out cross-validation confirmed its excellent robustness with a mean relative prediction error of 3.77%. Finally, a collaborative monitoring framework was built to fully leverage the complementary advantages of each sensor, realizing full-process blockage monitoring covering global blockage localization, precise quantitative severity calibration, and high-frequency transient risk early warning. The proposed method provides a robust experimental and technical foundation for real-time early warning, precise localization, and quantitative diagnosis of long-distance slurry pipeline blockages and holds important engineering application value for the safe and efficient operation of underground coal mine green backfilling systems. Full article

Accurately monitoring physical activity, including stationary cycling on an exercise bike, is important in managing chronic diseases and rehabilitation after lower limb surgery. This study aimed to validate a new smartphone-based sensor application (the BeSAFE+) for activity recognition and step counting across five phone placements, using the SENS Motion^® system as a reference standard, and observed activity time as ground truth. In a laboratory-based study, 20 participants performed walking, brisk walking, running, high- and low-intensity cycling, sitting, standing, and lying activities while carrying five smartphones placed in the front and back trouser pockets, a backpack, a running armband, and a fanny pack, and wearing the activity tracker. The front pocket placement had the most accurate classification during cycling activities (89–93%) versus SENS Motion^® (96–98%). For other activities, the highest overall classification accuracy was achieved with the phone in the back pocket. Overall, the SENS Motion^® activity tracker demonstrated higher classification accuracy than most smartphone placements across all activities, except for running. Nevertheless, several smartphone placements and Application Programming Interface (API) approaches achieved activity recognition and step count estimates that were not significantly different from the SENS Motion^® activity tracker, indicating that smartphone-based activity recognition can be valid under specific conditions. Full article

Wearable device-based Human Activity Recognition (HAR) is widely used in health management, rehabilitation, and personal safety. While contemporary HAR research effectively classifies a wide range of discrete activities, there remains a significant gap in organizing these heterogeneous motions into a structured intensity framework suitable for continuous risk assessment. Furthermore, many high-performing models rely on computationally intensive architectures that hinder real-time deployment on resource-constrained wearables. We propose an on-device method for estimating five-level activity intensity in real time using only accelerometer signals from a commercial smartwatch. To bridge the gap between simple identification and intensity modeling, 13 dynamic and emergency-like wrist motions were integrated with 11 daily activities from the PAMAP2 dataset, yielding 21 activities mapped onto an ordinal five-level intensity scale. A finetuned Multi-Layer Perceptron (MLP) classifier trained on this integrated dataset achieved 0.939 accuracy and a quadratic weighted kappa (QWK) of 0.971. The model was deployed on a Galaxy Watch 7, achieving $<1$ ms inference latency and a size $<0.1$ MB, confirming real-time feasibility. This approach demonstrates that organizing diverse activities into a lightweight, intensity-aware framework provides a robust foundation for safety-aware monitoring systems under real-world, on-device constraints. Full article

Background/Objectives: Evidence suggests that modifiable lifestyle interventions improve disability in relapsing multiple sclerosis (MS); however, interactions between different factors may impact outcomes. Thus, the objective of this secondary analysis was to investigate diet-induced effects on the impact of MS and effect modification by other modifiable lifestyle factors. Methods: The physical and psychological impact of MS was assessed with the MS Impact Scale-29 (MSIS) at run-in, baseline, 12 weeks, and 24 weeks. Participants were randomized at baseline to the Swank low-saturated fat or Wahls modified Paleolithic elimination diets and instructed to maintain usual physical activity, objectively measured with an accelerometer, throughout the trial. Baseline information on sleep, physical activity, alcohol, and smoking was explored as effect modifiers. Results: Among the Swank group, MSIS-Physical scores improved from 33.8 ± 3.8 at baseline to 28.7 ± 3.6 at 12 weeks (p = 0.04) and 25.3 ± 3.5 at 24 weeks (p < 0.001). MSIS-Psychological scores also improved from 35.7 ± 3.3 at baseline to 25.6 ± 2.6 at 12 weeks (p = 0.001) and 22.8 ± 2.4 at 24 weeks (p < 0.001). Among the Wahls group, MSIS-Physical scores improved from 33.8 ± 3.1 at baseline to 21.7 ± 3.0 at 12 weeks (p < 0.001) and 19.0 ± 3.1 at 24 weeks (p < 0.001). MSIS-Psychological scores also improved from 38.4 ± 3.8 at baseline to 25.5 ± 3.8 at 12 weeks (p < 0.001) and 20.6 ± 3.6 at 24 weeks (p < 0.001). Improvements in MSIS-Physical were greater among participants who were physically inactive or drank little alcohol at baseline. Conclusions: Both diets led to favorable within-group improvements in the perceived impact of MS. People with MS who are physically inactive or drink little alcohol may benefit the most from dietary interventions. Full article

Localization and navigation techniques have become fundamental for modern lives, while achieving accurate results indoors still remains a significant challenge. The widespread adoption of smart devices, and especially smartphones, has increased the need for accurate and robust pedestrian dead reckoning systems that operate in infrastructure-less environments. Pedestrian dead reckoning’s primary challenge is maintaining accuracy despite varying smartphone placements (attitudes) and the noisy, low-cost inertial measurements units. In this work, a comprehensive pedestrian dead reckoning framework is presented that integrates advanced step counting and heading estimation techniques. For step detection and counting, we propose a robust step counting algorithm that utilizes the optimum fusion of the raw IMU readings, i.e., accelerometer, linear accelerometer, gyroscope, and magnetometer readings, each broken down into three degrees of freedom for different body placements and walking speeds. Furthermore, to address the critical issue of heading estimation, we propose the heading estimation axis mapping (HEAT-MAP) algorithm, which dynamically adjusts the sensor axes in response to the smartphone’s orientation, ensuring a consistent coordinate frame and reducing heading drift. Moreover, to eliminate cumulative pedestrian dead reckoning errors, the system incorporates an adaptive weighted fusion mechanism with Wi-Fi fingerprinting. Experimental results demonstrate that this integrated system significantly improves the overall trajectory accuracy, providing a high-precision, attitude-unconstrained solution for real-time indoor pedestrian navigation. Full article

Anti-friction bearings are fundamental components of rotating machines. In bearing condition monitoring, fault detection is a primary task, as any undetected faults could result in catastrophic failures and downtime losses. To ensure effective and reliable fault detection, the use of appropriate sensors and measurement technologies is essential. This paper presents a comparative study on the applications of four sensor types in bearing condition monitoring. These four sensor types are vibration accelerometer, encoder, acoustic emission (AE) sensor and motor current probe. Their effectiveness and practicability in bearing fault detection are evaluted. Data simultaneously measured from these four sensor types on a split roller bearing within an experimental rig are used for the analysis. Different factors such as machine operating speeds, bearing fault sizes and their location are considered during the experiments to understand the effectiveness and fault detectability of different sensors on a common bearing. Both the accelerometer and the AE sensor are observed to effectively detect all bearing faults from small to extended sizes and from low to high operating speeds. However, the other two sensors, the encoder and motor current probe, have been found to be sensitive only to relatively larger fault sizes and higher operating speeds. The study presents valuable insights into their advantages and limitations through a systematic comparison of roller bearing fault detection. The study provides a basis for sensor selection in bearing condition monitoring and fault detection to enhance the reliability of industrial maintenance activities. Full article

This paper presents a low-noise analog front-end (AFE) integrated circuit (IC) circuit for capacitive micro-electromechanical system (MEMS) accelerometers that can be used for optical image stabilization (OIS) in various optical imaging systems. The AFE circuit design features a fully differential chopper stabilization technique that efficiently minimizes low-frequency 1/f noise and parasitic coupling. The AFE circuit chip is fabricated in a 0.18 $\mathsf{\mu }$m complementary metal-oxide-semiconductor (CMOS) technology and co-packaged with an x-axis capacitive MEMS accelerometer based on a silicon-on-glass (SOG) process. The SOG accelerometer has a footprint of 1000 $\mathsf{\mu }\mathrm{m}$ $\times$ 950 $\mathsf{\mu }\mathrm{m}$. The packaged system demonstrates a sensitivity of 342 mV/g and a nonlinearity of 1.1% between −1 g and +1 g, a dynamic range of 88 dB, and an equivalent noise floor of 14 $\mathsf{\mu }\mathrm{g}/\sqrt{\mathrm{H}\mathrm{z}}$. Full article