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The cold response of the digital arteries in a cohort of vibration-exposed workers was related to measures of daily vibration exposure expressed in terms of r.m.s. acceleration magnitude normalised to an 8-hour day, and frequency was weighted according to either the frequency weighting Wh defined in ISO 5349-1:2001 (Ah(8) in ms-2 r.m.s) or the hand--arm vascular frequency weighting Wp proposed in the ISO Technical Report 18570:2017 (Ap(8) in ms-2 r.m.s.). The metric Ap(8), which assigns more weight to intermediate- and high-frequency vibrations (31.5-250 Hz), performed better for the prediction of cold-induced digital arterial hyperresponsiveness in the vibration-exposed workers than the measure Ah(8) derived from the conventional ISO frequency weighting, which gives more importance to lower-frequency vibrations (≤16 Hz). [ABSTRACT FROM AUTHOR]

Cyclists are exposed to hand--arm vibration (HAV) for prolonged periods of time during training sessions and competitions. The vibration can reduce perceived comfort, thus limiting the ability of the cyclist to control the bike in endurance sessions. The study of HAV in cyclists in a controlled environment allows for comparisons between the effects of different postures, materials and technical solutions on perceived discomfort and on the vibration transmitted to specific body segments. This paper describes the experimental setup, the measurement chain and the data processing for the evaluation of bike comfort in the laboratory. The setup is based on single-axis or multiaxial shakers; the time history of the input vibration can be derived from on-field measurements for comparative analyses or can be selected from among classical stimuli for frequency response function evaluation (sine sweep or white noise). Comfort can be quantified via questionnaires; objective measurements can be derived from vibrations measured at different body locations using wearable accelerometers or laser doppler vibrometers. A case study is presented and discussed. [ABSTRACT FROM AUTHOR]

Models of hand--arm systems (HAS) are purely mechanical. These models do not include the biological active behaviour of the system, even though it has been known since 1997 that there is a tonic vibration reflex. Since then, several authors have investigated this reflex and related it to grip force, posture and some others features of mechanical vibration power absorption. Other scholars proposed models of HAS that do not include the tonic vibration reflex and its consequences. These models, even partial models, are nonetheless effective in describing many aspects of vibration exposure. This is probably due to the complexity of the HAS, so that the confounding factors overwhelm measurements. [ABSTRACT FROM AUTHOR]

A promising but still scarcely explored strategy for the estimation of gait parameters based on inertial sensors involves the adoption of machine learning techniques. However, existing approaches are reliable only for specific conditions, inertial measurements unit (IMU) placement on the body, protocols, or when combined with additional devices. In this paper, we tested an alternative gait-events estimation approach which is fully data-driven and does not rely on a priori models or assumptions. High-frequency (512 Hz) data from a commercial inertial unit were recorded during 500 steps performed by 40 healthy participants. Sensors' readings were synchronized with a reference ground reaction force system to determine initial/terminal contacts. Then, we extracted a set of features from windowed data labeled according to the reference. Two gray-box approaches were evaluated: (1) classifiers (decision trees) returning the presence of a gait event in each time window and (2) a classifier discriminating between stance and swing phases. Both outputs were submitted to a deterministic algorithm correcting spurious clusters of predictions. The stance vs. swing approach estimated the stride time duration with an average error lower than 20 ms and confidence bounds between ±50 ms. These figures are suitable to detect clinically meaningful differences across different populations. [ABSTRACT FROM AUTHOR]

During the secondary production of aluminum, upon melting the scrap in a furnace, there is the possibility of developing an aluminothermic reaction, which produces oxides in the molten metal bath. Aluminum oxides must be identified and removed from the bath, as they modify the chemical composition and reduce the purity of the product. Furthermore, accurate measurement of molten aluminum level in a casting furnace is crucial to obtain an optimal liquid metal flow rate which influences the final product quality and process efficiency. This paper proposes methods for the identification of aluminothermic reactions and molten aluminum levels in aluminum furnaces. An RGB Camera was used to acquire video from the furnace interior, and computer vision algorithms were developed to identify the aluminothermic reaction and melt level. The algorithms were developed to process the image frames of video acquired from the furnace. Results showed that the proposed system allowed the online identification of the aluminothermic reaction and the molten aluminum level present inside the furnace at a computation time of 0.7 s and 0.4 s per frame, respectively. The advantages and limitations of the different algorithms are presented and discussed. [ABSTRACT FROM AUTHOR]

Workers can be exposed daily to foot-transmitted vibration (FTV) from standing on mobile equipment or vibrating platforms and surfaces. This results in a consistent risk of developing neurological, vascular, and musculoskeletal problems. To date, there are no international standards describing procedures with which to evaluate the health risks deriving from long-term exposure to FTV. To study the applicability of hand--arm vibration (HAV) standards to the foot, the biomechanical responses of the hand and foot in terms of the frequency response function upon varying contact conditions were compared. Results evidenced similarities between the responses of the wrist and ankle, with differences in resonance for the fingers and toes. The study confirms that HAV standards are more suitable than whole-body vibration standards for evaluating higher frequency exposure to FTV. [ABSTRACT FROM AUTHOR]

Quality inspection in the industrial production field is experiencing a strong technological development that benefits from the combination of vision-based techniques with artificial intelligence algorithms. This paper initially addresses the problem of defect identification for circularly symmetric mechanical components, characterized by the presence of periodic elements. In the specific case of knurled washers, we compare the performances of a standard algorithm for the analysis of grey-scale image with a Deep Learning (DL) approach. The standard algorithm is based on the extraction of pseudo-signals derived from the conversion of the grey scale image of concentric annuli. In the DL approach, the component inspection is shifted from the entire sample to specific areas repeated along the object profile where the defect may occur. The standard algorithm provides better results in terms of accuracy and computational time with respect to the DL approach. Nevertheless, DL reaches accuracy higher than 99% when performance is evaluated targeting the identification of damaged teeth. The possibility of extending the methods and the results to other circularly symmetrical components is analyzed and discussed. [ABSTRACT FROM AUTHOR]

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes' performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method. [ABSTRACT FROM AUTHOR]

Changes of directions and cutting maneuvers, including 180-degree turns, are common locomotor actions in team sports, implying high mechanical load. While the mechanics and neurophysiology of turns have been extensively studied in laboratory conditions, modern inertial measurement units allow us to monitor athletes directly on the field. In this study, we applied four supervised machine learning techniques (linear regression, support vector regression/machine, boosted decision trees and artificial neural networks) to predict turn direction, speed (before/after turn) and the related positive/negative mechanical work. Reference values were computed using an optical motion capture system. We collected data from 13 elite female soccer players performing a shuttle run test, wearing a six-axes inertial sensor at the pelvis level. A set of 18 features (predictors) were obtained from accelerometers, gyroscopes and barometer readings. Turn direction classification returned good results (accuracy > 98.4%) with all methods. Support vector regression and neural networks obtained the best performance in the estimation of positive/negative mechanical work (coefficient of determination R2 = 0.42–0.43, mean absolute error = 1.14–1.41 J) and running speed before/after the turns (R2 = 0.66–0.69, mean absolute error = 0.15–018 m/s). Although models can be extended to different angles, we showed that meaningful information on turn kinematics and energetics can be obtained from inertial units with a data-driven approach. [ABSTRACT FROM AUTHOR]