Search Results (3,619)

Monitoring animal welfare on farms and in research settings is attracting increasing interest, both for ethical reasons and for improving productivity through the early detection of stress or diseases. In contrast to video-based monitoring, which requires good light conditions and has difficulty tracking specific animals, recent advances in the miniaturization of wearable devices allow for the collection of acceleration and location data to track individual animal behavior. However, for broilers, there are several challenges to address when using wearables, such as coping with (i) the large numbers of chickens in commercial farms,(ii)the impact of their rapid growth, and (iii) the small weights that the devices must have to be carried by the chickens without any impact on their health or behavior. To this end, this paper describes a pilot study in which chickens were fitted with devices containing an Inertial Measurement Unit (IMU) and an Ultra-Wideband (UWB) sensor. To establish guidelines for practitioners who want to monitor broiler welfare and activity at different scales, we first compare the attachment methods of the wearables to the broiler chickens, taking into account their effectiveness (in terms of retention time) and their impact on the broiler’s welfare. Then, we establish the technical requirements to carry out such a study, and the challenges that may arise. This analysis involves aspects such as noise estimation, synergy between UWB and IMU, and the measurement of activity levels based on the monitoring of chicken activity. We show that IMU data can be used for detecting activity level differences between individual animals and environmental conditions. UWB data can be used to monitor the positions and movement patterns of up to 200 animals simultaneously with an accuracy of less than 20 cm. We also show that the accuracy depends on installation aspects and that errors are larger at the borders of the monitored area. Attachment with sutures had the longest mean retention of 19.5 days, whereas eyelash glue had the shortest mean retention of 3 days. To conclude the paper, we identify current challenges and future research lines in the field. Full article

Padel is a racket sport that has grown internationally, both in the number of players and in the number of competitions. Inertial measurement devices enable a comprehensive analysis of competitive load in padel by providing kinematic variables that enhance players’ performance in this discipline. This study aimed to analyse the external load variables recorded with an inertial device in elite padel players, comparing metrics based on the players’ positions (left and right sides of the court). A total of 83 players were monitored during 23 matches of the professional circuit. The results revealed specific load metrics, including distance covered, frequency of accelerations and decelerations per hour, maximum speeds reached, and acceleration profiles relative to distance covered, which were all measured using the Wimu Pro™ device. Left-side players showed more frequent accelerations and decelerations per hour compared to right-side players. The results of this study will, on one hand, enable the adjustment of new specific parameters for professional padel training, such as acceleration and deceleration profiles, player load, and distances covered at explosive speeds. On the other hand, the results will provide a more objective evaluation of padel players’ performance based on their positions. Full article

Structural health monitoring is vital for the safety and longevity of infrastructure, particularly in seismic zones. This study focuses on identifying the dynamic properties of a reinforced concrete building in Chile’s Valparaíso region. Using an experimental approach, the study compares ambient vibration records, seismic events (moment magnitude > 4), and data collected during adjacent construction activities. Force-balanced accelerometers were used for vibration measurements. The analysis employs the Stochastic Subspace Identification with Covariances (SSI-COV) method within an operational modal analysis framework to extract the building’s modal parameters without requiring artificial excitations. This technique effectively identifies modal characteristics under different vibration sources, making it suitable for evaluating the structural condition under diverse loading conditions. The findings reveal the building’s modes and frequencies, offering critical insights for maintenance and management of infrastructure. Little to no variations were observed in the identified frequencies of the building when working with different types of input data. These data support the integration of real-time IoT systems for continuous monitoring, providing a foundation for future digital twin applications. These advancements facilitate early deterioration detection, enhancing resilience in seismic environments. Full article

The agricultural industries have embraced the use of technologies as they improve efficiency and food security. The pork industry is no exception to this, as monitoring techniques and artificial intelligence allow for unprecedented capacity to track the physiological and behavioural condition of individual animals. This article reviews a range of those technologies in reference to the detection of oestrus in sows, a time when the ability to precisely ascertain physiological and behavioural changes associated with fluctuating hormone levels can have an immense impact on the economic profitability of the farm. The strengths and weaknesses of each technique from a practical application perspective are discussed, followed by considerations for further research and refinement. Full article

This study introduces a mechatronic biomedical device engineered for concurrent acquisition and analysis of four cardiac non-invasive signals: Electrocardiogram (ECG), Phonocardiogram (PCG), Impedance Cardiogram (ICG), and Photoplethysmogram (PPG). The system enables assessment of individual and simultaneous waveforms, allowing for detailed scrutiny of cardiac electrical and mechanical dynamics, encompassing heart rate variability, systolic time intervals, pre-ejection period (PEP), and aortic valve opening and closing timings (ET) through an application programmed with MATLAB App Designer, which applies derivative filters, smoothing, and FIR digital filters and evaluates the delay of each one, allowing the synchronization of all signals. These metrics are indispensable for deriving critical hemodynamic indices such as Stroke Volume (SV) and Cardiac Output (CO), paramount in the diagnostic armamentarium against cardiovascular pathologies. The device integrates an assembly of components including five electrodes, operational and instrumental amplifiers, infrared opto-couplers, accelerometers, and advanced filtering subsystems, synergistically tailored for precision and fidelity in signal processing. Rigorous validation utilizing a cohort of healthy subjects and benchmarking against established commercial instrumentation substantiates an accuracy threshold below 4.3% and an Interclass Correlation Coefficient (ICC) surpassing 0.9, attesting to the instrument’s exceptional reliability and robustness in quantification. These findings underscore the clinical potency and technical prowess of the developed device, empowering healthcare practitioners with an advanced toolset for refined diagnosis and management of cardiovascular disorders. Full article

Cognitive rumination, a transdiagnostic symptom across mental health disorders, has traditionally been assessed through self-report measures. However, these measures are limited by their temporal nature and subjective bias. The rise in wearable technologies offers the potential for continuous, real-time monitoring of physiological indicators associated with rumination. This scoping review investigates the current state of research on using wearable technology to detect cognitive rumination. Specifically, we examine the sensors and wearable devices used, physiological biomarkers measured, standard measures of rumination used, and the comparative validity of specific biomarkers in identifying cognitive rumination. The review was performed according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines on IEEE, Scopus, PubMed, and PsycInfo databases. Studies that used wearable devices to measure rumination-related physiological responses and biomarkers were included (n = 9); seven studies assessed one biomarker, and two studies assessed two biomarkers. Electrodermal Activity (EDA) sensors capturing skin conductance activity emerged as both the most prevalent sensor (n = 5) and the most comparatively valid biomarker for detecting cognitive rumination via wearable devices. Other commonly investigated biomarkers included electrical brain activity measured through Electroencephalogram (EEG) sensors (n = 2), Heart Rate Variability (HRV) measured using Electrocardiogram (ECG) sensors and heart rate fitness monitors (n = 2), muscle response measured through Electromyography (EMG) sensors (n = 1) and movement measured through an accelerometer (n = 1). The Empatica E4 and Empatica Embrace 2 wrist-worn devices were the most frequently used wearable (n = 3). The Rumination Response Scale (RRS), was the most widely used standard scale for assessing rumination. Experimental induction protocols, often adapted from Nolen-Hoeksema and Morrow’s 1993 rumination induction paradigm, were also widely used. In conclusion, the findings suggest that wearable technology offers promise in capturing real-time physiological responses associated with rumination. However, the field is still developing, and further research is needed to validate these findings and explore the impact of individual traits and contextual factors on the accuracy of rumination detection. Full article

Background: Mechanomyography (MMG) is a neurodiagnostic technique with a documented ability to evaluate the compression of nerve roots. Its utility in degenerative spine surgery is unknown. Objective: To assess the utility of intraoperative MMG during cervical posterior foraminotomy, minimally invasive transforaminal interbody fusion (MIS-TLIF), and tubular lumbar far lateral discectomy. Methods: A prospective feasibility study was conducted during which MMG was applied during three procedures. Adhesive accelerometers were placed on two muscle groups per procedure. Stimulus threshold in mA was recorded before and after the decompression of the nerve root. Differences in stimulation thresholds were correlated with operative findings. Results: In total, 22 patients were included in this study; 5 patients underwent cervical foraminotomies, 3 underwent MIS-TLIFs, and 14 underwent tubular far lateral discectomies. For the foraminotomies, all cases showed a reduction in stimulation threshold (mean of 3.4 mA) after decompression. For MIS-TLIF cases, there was a limited reduction in the stimulation threshold after decompression (mean 1.7 mA). For far lateral discectomy, there was a mean reduction of 4.3 mA in the stimulation threshold following decompression. Conclusions: MMG is a method that may provide intraoperative feedback on the decompression of nerve roots. In the context of MIS-TLIF, MMG showed a limited decrease in stimulus threshold. This may be due to the identification of the nerve occurring after decompression is already underway. For cervical foraminotomies and far lateral discectomies, MMG showed promising results in determining adequate decompression of the nerve root. Full article

The quartz flexible accelerometer (QFA) is a critical component in navigation-grade strapdown inertial navigation systems (SINS) due to its bias error, which significantly impacts the overall navigation accuracy of SINS. Temperature variations induce dynamic changes in the bias and scale factor of QFA, leading to a degradation of the navigation accuracy of SINS. To address this issue, this paper proposes a temperature error compensation method based on a non-uniform mutation strategy genetic algorithm (NUMGA) and a polynomial curve model (PCF). Firstly, the temperature bias mechanism of QFA output is analyzed, and a polynomial temperature error model is established. Then, the NUMGA is utilized to identify the model parameters using the −20–40 °C test data, seeking the optimal parameters for the polynomial. Finally, the compensation parameters are used for cold start static test verification. The results demonstrate that the temperature compensation model based on NUMGA-PCF can automatically select the optimal parameters, which enable the model to exhibit a stable decreasing trend on the adaptation curve without multiple fluctuations. Compared to the traditional GA temperature compensation model, the compensation errors in the three axes of QFA in SINS are reduced by 612.24 μg, 60.82 μg, and 875.82 μg, respectively. Before the 20th generation, there are no decrease in convergence speed observed with the in-crease of population diversity. Within the −20–40 °C temperature range, the average values and standard deviations of QFA for the three optimized axes can be maintained below 0.1 μg by using this compensation model. Full article

Aims: The aim was to assess the agreement between microphone-derived and neck accelerometer-derived voice acoustic parameters and their associations with recording moments and speech types. Methods: Using simultaneous recordings, a 7-week study on a single individual was conducted to reduce intersubject variability. Agreement was assessed using Bland–Altman plots, and associations were examined with generalized estimating equations. Results: Bland–Altman plots showed no significant bias between microphone (MIC) and accelerometer (ACC) measurements for alpha ratio, CPP, PPE, SPL SD, fundamental frequency (fo) mean, and SD. Speech type and measurement timing were significantly associated with alpha ratio, while the instrument was not. Microphone measurements resulted in slightly lower CPP compared to the accelerometer, while reading samples yielded higher CPP compared to vowel productions. PPE, SPL SD, and fo mean showed significant associations with speech type, based on univariate analysis. Microphone measurements yielded a statistically smaller fo SD compared to the accelerometer, while reading productions had a larger fo SD than vowel productions. Conclusions: Fundamental frequency, alpha ratio, PPE, and SPL SD values were robust, regardless of the instrument used, suggesting the potential use of accelerometers in less-controlled environments. These findings are crucial for enhancing confidence in voice metrics and exploring efficient clinical assessment protocols. Full article

Physical activity (PA) decreases during summer months, potentially leading to accelerated weight gain and increased depressive symptoms in adolescents. Summer care programs offer opportunities for PA promotion but understanding how different groups (based on initial perceived and objectively measured PA) respond to these programs is crucial for developing focused interventions. Adolescents (n = 47; mean age = 11.0 years; 51.1% female) who participated in an 8-week summer program wore ActiGraph GT9X accelerometers to measure moderate-to-vigorous physical activity (MVPA) at the beginning and end of the program. Self-reported PA was assessed using the Health Behavior in School-Aged Children survey. Both measures were then transformed into respective z-scores. K-means cluster analysis was performed to identify distinct groups based on device-measured and perceived PA at the beginning of summer. Changes in MVPA were compared across clusters using one-way ANOVA and post hoc Tukey’s HSD tests. Three clusters were identified: “High Accuracy Actives” (n = 17), “Underestimators” (n = 22), and “Overestimators” (n = 8). “Overestimators” showed the largest mean increase in MVPA (30.63 min/day), followed by “Underestimators” (17.76 min/day). “High Accuracy Actives” experienced a mean decrease in MVPA (−7.69 min/day). ANOVA revealed significant differences in MVPA change between clusters (F(2,44) = 4.93, p = 0.01). Summer care programs can positively impact adolescent PA, particularly for those who initially underestimate or overestimate their activity levels. However, strategies are needed to prevent declines among initially highly active participants. For example, adolescents who underestimate their activity levels may benefit from interventions focused on building self-efficacy and providing positive feedback, while those who overestimate might require educational components about PA guidelines and self-monitoring techniques. Full article

Standing long jump (SLJ) power is recognized as informative of the ability of lower limbs to exert power. The study aims to provide athletes/coaches with a simple and low-cost estimate of selected SLJ power features. A group of 150 trained young participants was recruited and performed a SLJ task while holding a smartphone, whose inertial sensors were used to collect data. Considering the state-of-the-art in SLJ biomechanics, a set of features was extracted and then selected by Lasso regression and used as inputs to several different optimized machine learning architectures to estimate the SLJ power variables. A Multi-Layer Perceptron Regressor was selected as the best-performing model to estimate total and concentric antero-posterior mean power, with an RMSE of 0.37 W/kg, R² > 0.70, and test phase homoscedasticity (Kendall’s τ < 0.1) in both cases. Model performance was dependent on the dataset size rather than the participants’ sex. A Multi-Layer Perceptron Regressor was able to also estimate the antero-posterior peak power (RMSE = 2.34 W/kg; R² = 0.67), although affected by heteroscedasticity. This study proved the feasibility of combining low-cost smartphone sensors and machine learning to automatically and objectively estimate SLJ power variables in ecological settings. Full article

In this study, anomalies in a fan system were classified using a real measurement setup to simulate mechanical anomalies such as blade detachment or debris accumulation. Data were collected under normal operating conditions and with an added unbalancing mass. Additionally, sensor anomalies were introduced by manipulating accelerometer readings and examining three types: spike, stuck, and dropout. To classify the anomalies, four neural network models—variations in Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) were tested. These models incorporated a Continuous Wavelet Transform (CWT) layer. A novel approach for implementing the CWT layer in both LSTM and CNN architectures was proposed, along with a dual-branch input structure featuring two CWT layers using different mother wavelets. The dual-branch configuration with different mother wavelets yielded better accuracy for the simpler LSTM network. Accuracy comparisons were conducted for the 10 best-performing models based on validation set predictions, revealing improved classification performance. The study concluded with a summary of prediction accuracy for both the validation and test sets of data, along with the calculation of average accuracy, demonstrating the effectiveness of the proposed dual-branch neural network structure in classifying anomalies in fan systems. Full article

Introduction: Gait analysis is a vital tool in the assessment of human movement and has been widely used in clinical settings to identify potential abnormalities in individuals. However, there is a lack of consensus on the normative values for gait metrics in large populations. The primary objective of this study is to establish a normative database of spatiotemporal gait metrics across various age groups, contributing to a broader understanding of human gait dynamics. By doing so, we aim to enhance the clinical utility of gait analysis in diagnosing and managing health conditions. Methods: We conducted an observational case–control study involving 313 healthy participants. The MetaMotionC IMU by Mbientlab Inc., equipped with a triaxial accelerometer, gyroscope, and magnetometer, was used to capture gait data. The IMU was placed at the sternal angle of each participant to ensure optimal data capture during a 50 m walk along a flat, unobstructed pathway. Data were collected through a Bluetooth connection to a smartphone running a custom-developed application and subsequently analysed using IMUGaitPY, a specialised version of the GaitPY Python package. Results: The data showed that gait speeds decrease with ageing for males and females. The fastest gait speed is observed in the 41–50 age group at $1.35\pm 0.23\mathrm{m}/\mathrm{s}$. Males consistently exhibit faster gait speeds than females across all age groups. Step length and cadence do not have clear trends with ageing. Gait speed and step length increase consistently with height, with the tallest group (191–200 cm) walking at an average speed of $1.49\pm 0.12\mathrm{m}/\mathrm{s}$, with an average step length of $0.91\pm 0.05\mathrm{m}$. Cadence, however, decreases with increasing height, with the tallest group taking $103.52\pm 5.04\mathrm{s}\mathrm{t}\mathrm{e}\mathrm{p}\mathrm{s}/\mathrm{m}\mathrm{i}\mathrm{n}$ on average. Conclusions: This study has established a comprehensive normative database for the spatiotemporal gait metrics of gait speed, step length, and cadence, highlighting the complexities of gait dynamics across age and sex groups and the influence of height. Our findings offer valuable reference points for clinicians to distinguish between healthy and pathological gait patterns, facilitating early detection and intervention for gait-related disorders. Moreover, this database enhances the clinical utility of gait analysis, supporting more objective diagnoses and assessments of therapeutic interventions. The normative database provides a valuable reference future research and clinical practice. It enables a more nuanced understanding of how gait evolves with age, gender, and physical stature, thus informing the development of targeted interventions to maintain mobility and prevent falls in older adults. Despite potential selection bias and the cross-sectional nature of the study, the insights gained provide a solid foundation for further longitudinal studies and diverse sampling to validate and expand upon these findings. Full article

This paper studies the relevance of CWT (continuous wavelet transform) processing of vibration signals for improving the performance of CNN-based models that detect certain types of helical gearbox faults. Gear tooth damages, such as incipient and localized pitting and localized wear on helical pinion tooth flanks, combined with improper lubrication, are the faults under observation. Vibrations at the housing level for three rotating velocities of the AC motor and three load levels (for each velocity) are acquired with a triaxial accelerometer. Through CWT, the vibration signal is decomposed into 2D time-frequency grayscale images, with a filter bank of ten voices per octave in the frequency band of interest. Three 2D-CNN-based models trained on the CWT-based representation of the vibration signals measured on individual accelerometer axes (X, Y, and Z) are proposed to detect the four health states (one normal and three faulty) of the helical gearbox, regardless of the selected load level or speed on the test rig. These models achieve an accuracy higher than 99%. By fusing the CWT-based representations of the signals on individual axes for use as input to a 2D-CNN, the best-performing model for the proposed defect detection task is generated, reaching an accuracy of 99.91%. Full article

Background/Objectives: Adults with hearing loss demonstrate poorer overall health outcomes (e.g., physical health, cognitive functioning and wellbeing) and lower levels of physical activity/function compared to those without hearing loss. Hearing aids have the potential to improve cognitive and wellbeing factors, but there is a dearth of evidence on their impact on physical health outcomes. Evidence on the association between hearing aid provision and physical activity is mostly limited to cross-sectional studies. This research aimed to assess whether a study can be performed to identify whether the provision of hearing aids can improve physical activity. Methods: This study employed a preregistered observational (prospective cohort) study design of ten older adults (51–75 years) completed assessments at baseline and again at a six-week follow-up. The participants wore an accelerometer (ActiGraph GT9X) without feedback for the full duration of the study. Feasibility was determined using pre-defined criteria, including study drop-out, adherence to accelerometer use and willingness. A battery of health outcomes was also assessed at baseline and follow-up. Conclusions: Overall, this study was perceived favourably, with all participants reporting that they enjoyed taking part. Participant retention was 100%, and adherence to the wrist-worn accelerometers was “good” (70%). However, recruitment was challenging, and some participants found the accelerometers to be burdensome. Descriptive statistics for all outcome measures showed non-significant changes in the expected direction (e.g., improved physical activity, cognition and wellbeing). Although the study was well received by participants, modifications to the recruitment strategy and activity tracking procedures are necessary before future large-scale trials assessing the effectiveness of hearing aids on physical activity can be undertaken. Full article