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Lower-limb rehabilitation exoskeletons offer a transformative approach to enhancing recovery in patients with movement disorders affecting the lower extremities. This comprehensive systematic review delves into the literature on sensor technologies and the control strategies integrated into these exoskeletons, evaluating their capacity to address user needs and scrutinizing their structural designs regarding sensor distribution as well as control algorithms. The review examines various sensing modalities, including electromyography (EMG), force, displacement, and other innovative sensor types, employed in these devices to facilitate accurate and responsive motion control. Furthermore, the review explores the strengths and limitations of a diverse array of lower-limb rehabilitation-exoskeleton designs, highlighting areas of improvement and potential avenues for further development. In addition, the review investigates the latest control algorithms and analysis methods that have been utilized in conjunction with these sensor systems to optimize exoskeleton performance and ensure safe and effective user interactions. By building a deeper understanding of the diverse sensor technologies and monitoring systems, this review aims to contribute to the ongoing advancement of lower-limb rehabilitation exoskeletons, ultimately improving the quality of life for patients with mobility impairments. [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]

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]

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]

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]

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]

The use of whole-body vibration (WBV) for therapeutic purposes is far from being standardized and an empirical foundation for reporting guidelines for human WBV studies has only very recently been published. Controversies about safety and therapeutic dosage still exist. The present study aimed to investigate the metabolic and mechanical effects of low-intensity WBV according to the ISO 2631 norm on subjects with obesity. Forty-one obese subjects (BMI ≥ 35 kg/m2) were recruited to participate in a 3-week multidisciplinary inpatient rehabilitation program including fitness training and WBV training. During WBV the posture was monitored with an optoelectronic system with six infrared cameras (Vicon, Vicon Motion System, Oxford, UK). The primary endpoints were: variation in body composition, factors of metabolic syndrome, functional activity (sit-to-stand and 6-min walking test), muscle strength, and quality of life. The secondary endpoints were: modification of irisin, testosterone, growth hormone, IGF1 levels. We observed significant changes in salivary irisin levels, Group 2 (p < 0.01) as compared to the control group, while muscle strength, function, and other metabolic and hormonal factors did not change after a 3-week low-intensity WBV training with respect to the control group. Future studies are needed to further investigate the potential metabolic effect of low-intensity WBV in managing weight. [ABSTRACT FROM AUTHOR]

Recent researches have proven that the underbridge geometry can be reconstructed by mounting a 3D laser scanner on a motorized cart travelling on a walkway located under the bridge. The walkway is moved by a truck and the accuracy of the bridge model depends on the accuracy of the trajectory of the scanning head with respect to a fixed reference system. In this paper, we describe a vision-based measurement system that can be used to identify the relative motion of the cart that moves the 3D laser scanner with respect to the walkway. The orientation of the walkway with respect to the bridge is determined using inclinometers and a camera that detect the position of a laser spot, while the position of the truck with respect to the bridge is measured using a conventional odometer. The accuracy of the proposed system was initially evaluated by numerical simulations and successively verified by experiments in laboratory conditions. The complete system has then been tested by comparing the geometry of buildings reconstructed using the proposed system with the geometry obtained with a static scan. Results showed that the error is less than 6 mm; given the satisfying quality of the point clouds obtained, it is also possible to detect small defects on the surface. [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]

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]

This paper describes a measurement method for the quality control of cylinders for printing machines based on roll-to-roll presses. If the surface finishing of the cylinders is not adequate, the printing is unacceptable, and the defective cylinders must be reworked. The performed quality check of the cylinder surface roughness by means of contact methods was unable to identify the cylinder defects, and acceptance of the manufactured cylinders before integration was demanded to the visual inspection performed by trained operators. In this work a contactless measurement method based on the eddy current displacement sensor was proposed and validated as a tool for quality check as an alternative to optical roughness measurements. A test bench for the characterization of printer cylinders was designed and manufactured, allowing for the validation of the proposed method on different batches of cylinders and the identification of a threshold to guide the acceptance of tested cylinders prior to mounting on the roll-to-roll press. [ABSTRACT FROM AUTHOR]

This work describes the design and validation of an electro-mechanical excitation system for characterization of the response of the human body to multiaxial vibrations. The presented system is based on the linear delta configuration and is designed to expose standing subjects to vibration along three perpendicular axes, with an excitation bandwidth of at least 30 Hz and a maximum vibration amplitude of ±30 mm along the vertical direction and ±20 mm along the horizontal directions. The shaker characteristic dimensions are the result of numerical optimization of the inverse manipulability index; the motors and transmissions have been selected using a multibody dynamic simulation. Finite element simulations were performed to ensure that the structural resonances were outside the excitation bandwidth. Once the shaker had been manufactured, experiments were performed to verify the capability of the system in real testing conditions. The mean quadratic error between the modulus of the imposed acceleration and the measured one is between 5.7 × 10−3 and 1.4 × 10−2 m/s2 in the frequency range between 1 and 50 Hz, proving the good outcome of the design process. [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]