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The design and control of artificial hands remains a challenge in engineering. Popular prostheses are bio-mechanically simple with restricted manipulation capabilities, as advanced devices are pricy or abandoned due to their difficult communication with the hand. For social robots, the interpretation of human intention is key for their integration in daily life. This can be achieved with machine learning (ML) algorithms, which are barely used for grasping posture recognition. This work proposes an ML approach to recognize nine hand postures, representing 90% of the activities of daily living in real time using an sEMG human-robot interface (HRI). Data from 20 subjects wearing a Myo armband (8 sEMG signals) were gathered from the NinaPro DS5 and from experimental tests with the YCB Object Set, and they were used jointly in the development of a simple multi-layer perceptron in MATLAB, with a global percentage success of 73% using only two features. GPU-based implementations were run to select the best architecture, with generalization capabilities, robustness-versus-electrode shift, low memory expense, and real-time performance. This architecture enables the implementation of grasping posture recognition in low-cost devices, aimed at the development of affordable functional prostheses and HRI for social robots.

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Low-cost particulate matter (PM) sensors provide new methods for monitoring occupational exposure to hazardous substances, such as flour dust. These devices have many possible benefits, but much remains unknown about their performance for different exposure monitoring strategies in the workplace. We explored the performance of PM sensors for four different monitoring strategies (time-weighted average and high time resolution, each quantitative and semi-quantitative) for assessing occupational exposure using low-cost PM sensors in a field study in the industrial bakery sector. Measurements were collected using four types of sensor (PATS+, Isensit, Airbeam2, and Munisense) and two reference devices (respirable gravimetric samplers and an established time-resolved device) at two large-scale bakeries, spread over 11 participants and 6 measurement days. Average PM2.5 concentrations of the low-cost sensors were compared with gravimetric respirable concentrations for 8-h shift periods and 1-min PM2.5 concentrations of the low-cost sensors were compared with time-resolved PM2.5 data from the reference device (quantitative monitoring strategy). Low-cost sensors were also ranked in terms of exposure for 8-h shifts and for 15-min periods with a shift (semi-quantitative monitoring strategy). Environmental factors and methodological variables, which can affect sensor performance, were investigated. Semi-quantitative monitoring strategies only showed more accurate results compared with quantitative strategies when these were based on shift-average exposures. The main factors that influenced sensor performance were the type of placement (positioning the devices stationary versus personal) and the company or workstation where measurements were collected. Together, these findings provide an overview of common strengths and drawbacks of low-cost sensors and different ways these can be applied in the workplace. This can be used as a starting point for further investigations and the development of guidance documents and data analysis methods.

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PURPOSE: The objective of this work is to evaluate the performances of a novel integrated device, based on passive head-mounted display (HMD), for the pattern reversal visual evoked potential (PR-VEP) clinical test. METHODS: Google Cardboard® is used as passive HMD to generate the checkerboard pattern stimuli through an Android® application. Electroencephalographic signals are retrieved and processed over 20 subjects, 12 females and 8 males between 20 and 26 years. Morphological PR-VEPs and frequency response were compared with previous literature results, to test the reproducibility and the efficacy of the proposed solution. RESULTS: PR-VEPs evoked by our novel prototype showed typical triphasic waveforms in moderate agreement with those obtained with other more expensive HMDs and standard commercial devices. Statistical analysis did not highlight strong differences among the systems over the features analyzed except for the P100 amplitude and peak time (**p < 0.005). CONCLUSION: The proposed solution opens the door for a new generation of non-invasive first-level diagnostic devices of optic nerve pathologies inexpensive and easy to access.

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The present scenario in the world largely demands affordable, fast, recyclable, and flexible electronic devices for bio sensing. Varieties of paper-based devices such as microfluidics paper electrodes, paper diodes, and paper-based transistors etc. have been developed and validated. Most of the fabrication techniques published so far have focused on economic, environment-friendly straightforward methods to develop paper-based field-effect transistors (PFET) sensors, additionally, explored their applications. The synthetic-free, mechanically flexible, biocompatible, and signal amplification capability render PFET based sensors for wearable device makers. Modified organic/inorganic PFETs identify target analytes based on the electrical signal and endow them as perfect transducers. In the field of PFET bio sensing technology, numerous challenges are needed to be discussed to proceed forward in biomedical and other analytical applications. Realizing biologically or chemically modified PFET having an exceptional signal to noise ratio, specificity, with rapid detection ability is challenging. This review recapitulates the fabrication techniques, performances of various PFET sensors, and summarizes the report by concluding remarks including the limitations of the existing PFET based sensors and the future holds in regards to the sustainable nature of PFET.

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The measurement of maximal inspiratory (MIP) and maximal expiratory (MEP) pressures is a widely used technique to non-invasively evaluate respiratory muscle strength in clinical practice. The commercial devices that perform this test range from whole body plethysmographs to portable spirometers, both expensive and include a wide range of other respiratory tests. Given that a portable, low-cost, and specific option for MIP and MEP measuring device is not currently available in the market. A high-performance and easy-to-build prototype has been developed and the detailed technical information to easily reproduce it is freely released. A novel device is based on an Arduino microcontroller with a digital display, an integrated pressure transducer, and three-dimensional (3D) printed enclosure (total retail cost €80). The validation of the device was performed by comparison with a laboratory reference setting, and results showed accuracy within ±1%. As the device design is available according to the open-source hardware approach, measuring MIP/MEP can greatly facilitate easily available point-of-care devices for the monitoring of patients and, most important, for making this lung function measurement tool affordable to users in low- and middle-income countries.

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Objectives: Globally, less than 1% of people who could benefit from a cochlear implant have one and the problem is particularly acute in lower-income countries. Here we give a narrative review of the economic and logistic feasibility of cochlear implant programmes in lower-income countries and discuss future developments that would enable better healthcare. We review the incidence and aetiology of hearing loss in low- and middle-income countries, screening for hearing loss, implantation criteria, issues concerning imaging and surgery, and the professional expertise required. We also review the cost of cochlear implantation and ongoing costs. Findings: The cost effectiveness of cochlear implants in lower-income countries is more limited by the cost of the device than the cost of surgery, but there are also large ongoing costs that will deter many potential users. Conclusions: We conclude that the main barriers to the future uptake of cochlear implants are likely to be logistical rather than technical and cochlear implant provision should be considered as part of a wider programme to improve the health of those with hearing loss.

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The integration of monitoring technologies in the last decades has been a key factor in the development of new ways to track air pollutants and supplementing the network of traditional monitoring systems. In this regard, the appearance of affordable and accurate sensor devices to monitor air quality has made possible to obtain relevant data about the state of the air, and moreover, eminent institutions are interested in promoting the use of novel and more affordable tools for air pollution, such as the United States Environmental Protection Agency and European institutions, within a new approach to environmental surveillance, known as Next Generation Compliance and Enforcement technologies. On other hand, in order to get more reliable measurements, the use of machine learning to support adjustment or calibration process has been used in some studies to improve the performance of monitoring devices. On this paper, led by a group of specialists of the Chilean Superintendence of Environment (henceforth, SMA from its Spanish initials), a first approach case study related to the convenience of the usage of low-cost devices in environmental enforcement will be presented. The study was made in the Metropolitan Region of Santiago and considers the spatial distribution of different particulate matter sensors in the region. Some aspects regarding communication and technical issues are presented as well as the main findings about their performance. Results illustrate that low-cost sensors, aided by machine learning algorithms, could provide a reliable enough general screening of particulate matter within a large city, constituting a valuable decision-making tool for environmental oversight, as well as a powerful preventive and deterrent approach for compliance.

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Balance recovery mechanisms are of paramount importance in situations like public transport where sudden loss of equilibrium can occur. These mechanisms can be altered by aging or pathological disorders. However it is almost impossible to investigate these phenomena in real-life conditions, and the safe environment of a laboratory is needed. This paper investigates how jerk perturbations in the transverse plane similar to those experienced on public transport can be simulated in a controlled manner. A platform capable of producing horizontal perturbations with a person standing on it was developed. Accuracy, repeatability, and load sensitivity of the system were assessed with repeated trials in all four directions of movement. Comparison between the destabilising effect experienced on public transport and the postural response to perturbations from the platform was also made by tracking acceleration of the centre of mass of four subjects in these two situations. Results show that balance perturbations representative of real-life situations, such as standing on public transport, can accurately and repeatedly be produced in a safe and controlled environment with a low-cost and low-maintenance system. Coupled to motion capture technology, the system can be used for pathology assessment and rehabilitation treatments.

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An accurate and easy-to-use Q3 system for on-chip quantitative real-time Polymerase Chain Reaction (qPCR) is hereby demonstrated, and described in detail. The qPCR reactions take place inside a single-use Lab-on-a-Chip with multiple wells, each with 5 to 15 µL capacity. The same chip hosts a printed metal heater coupled with a calibrated sensor, for rapid and accurate temperature control inside the reaction mixture. The rest of the system is non-disposable and encased in a 7 × 14 × 8.5 (height) cm plastic shell weighing 300 g. Included in the non-disposable part is a fluorescence read-out system featuring up to four channels and a self-contained control and data storage system, interfacing with an external user-friendly software suite. Hereby, we illustrate the engineering details of the Q3 system and benchmark it with seamlessly ported testing protocols, showing that Q3 equals the performance of standard commercial systems. Overall, to the best of our knowledge, this is one of the most mature general-purpose systems for on-chip qPCR currently available.

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The high rate of vehicle-crash victims has a fatal economic and social impact in today’s societies. In particular, road crashes where heavy vehicles are involved cause more severe damage because they are prone to rollover. For this reason, many researches are focused on developing RSC Roll Stability Control (RSC) systems. Concerning the design of RSC systems with an adequate performance, it is mandatory to know the dynamics of the vehicle. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill hard real-time processing constraints, achieving high level of accuracy during circulation of a vehicle in real situations. In order to address this issue, this study has two main goals: (1) Design and develop an IoT based architecture, integrating ANN in low cost kits with different hardware architectures in order to estimate under real-time constraints the vehicle roll angle. This architecture is able to work under high dynamic conditions, by following specific best practices and considerations during its design; (2) assess that the IoT architecture deployed in low-cost experimental kits achieve the hard real-time performance constraints estimating the roll angle with the required calculation accuracy. To fulfil these objectives, an experimental environment was set up, composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model Band the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations highly approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risk situations, fulfilling real-time operation restrictions stated for this problem.

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In recent years, there have been many advances in vehicle technologies based on the efficient use of real-time data provided by embedded sensors. Some of these technologies can help you avoid or reduce the severity of a crash such as the Roll Stability Control (RSC) systems for commercial vehicles. In RSC, several critical variables to consider such as sideslip or roll angle can only be directly measured using expensive equipment. These kind of devices would increase the price of commercial vehicles. Nevertheless, sideslip or roll angle or values can be estimated using MEMS sensors in combination with data fusion algorithms. The objectives stated for this research work consist of integrating roll angle estimators based on Linear and Unscented Kalman filters to evaluate the precision of the results obtained and determining the fulfillment of the hard real-time processing constraints to embed this kind of estimators in IoT architectures based on low-cost equipment able to be deployed in commercial vehicles. An experimental testbed composed of a van with two sets of low-cost kits was set up, the first one including a Raspberry Pi 3 Model B, and the other having an Intel Edison System on Chip. This experimental environment was tested under different conditions for comparison. The results obtained from low-cost experimental kits, based on IoT architectures and including estimators based on Kalman filters, provide accurate roll angle estimation. Also, these results show that the processing time to get the data and execute the estimations based on Kalman Filters fulfill hard real time constraints.

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Nowadays, the current vehicles are incorporating control systems in order to improve their stability and handling. These control systems need to know the vehicle dynamics through the variables (lateral acceleration, roll rate, roll angle, sideslip angle, etc.) that are obtained or estimated from sensors. For this goal, it is necessary to mount on vehicles not only low-cost sensors, but also low-cost embedded systems, which allow acquiring data from sensors and executing the developed algorithms to estimate and to control with novel higher speed computing. All these devices have to be integrated in an adequate architecture with enough performance in terms of accuracy, reliability and processing time. In this article, an architecture to carry out the estimation and control of vehicle dynamics has been developed. This architecture was designed considering the basic principles of IoT and integrates low-cost sensors and embedded hardware for orchestrating the experiments. A comparison of two different low-cost systems in terms of accuracy, acquisition time and reliability has been done. Both devices have been compared with the VBOX device from Racelogic, which has been used as the ground truth. The comparison has been made from tests carried out in a real vehicle. The lateral acceleration and roll rate have been analyzed in order to quantify the error of these devices.

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Interest in low-cost diagnostic devices has recently gained attention, in part due to the rising cost of healthcare and the need to serve populations in resource-limited settings. A major challenge in the development of such devices is the need for hydrophobic barriers to contain polar bio-fluid analytes. Key approaches in lowering the cost in diagnostics have centered on (i) development of low-cost fabrication techniques/processes, (ii) use of affordable materials, or, (iii) minimizing the need for high-tech tools. This communication describes a simple, low-cost, adaptable, and portable method for patterning paper and subsequent use of the patterned paper in diagnostic tests. Our approach generates hydrophobic regions using a ball-point pen filled with a hydrophobizing molecule suspended in a solvent carrier. An empty ball-point pen was filled with a solution of trichloro perfluoroalkyl silane in hexanes (or hexadecane), and the pen used to draw lines on Whatman® chromatography 1 paper. The drawn regions defined the test zones since the trichloro silane reacts with the paper to give a hydrophobic barrier. The formation of the hydrophobic barriers is reaction kinetic and diffusion-limited, ensuring well defined narrow barriers. We performed colorimetric glucose assays and enzyme-linked immuno-sorbent assay (ELISA) using the created test zones. To demonstrate the versatility of this approach, we fabricated multiple devices on a single piece of paper and demonstrated the reproducibility of assays on these devices. The overall cost of devices fabricated by drawing are relatively lower (

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Interest in low-cost diagnostic devices has recently gained attention, in part due to the rising cost of healthcare and the need to serve populations in resource-limited settings. A major challenge in the development of such devices is the need for hydrophobic barriers to contain polar bio-fluid analytes. Key approaches in lowering the cost in diagnostics have centered on (i) development of low-cost fabrication techniques/processes, (ii) use of affordable materials, or, (iii) minimizing the need for high-tech tools. This communication describes a simple, low-cost, adaptable, and portable method for patterning paper and subsequent use of the patterned paper in diagnostic tests. Our approach generates hydrophobic regions using a ball-point pen filled with a hydrophobizing molecule suspended in a solvent carrier. An empty ball-point pen was filled with a solution of trichloro perfluoroalkyl silane in hexanes (or hexadecane), and the pen used to draw lines on Whatman® chromatography 1 paper. The drawn regions defined the test zones since the trichloro silane reacts with the paper to give a hydrophobic barrier. The formation of the hydrophobic barriers is reaction kinetic and diffusion-limited, ensuring well defined narrow barriers. We performed colorimetric glucose assays and enzyme-linked immuno-sorbent assay (ELISA) using the created test zones. To demonstrate the versatility of this approach, we fabricated multiple devices on a single piece of paper and demonstrated the reproducibility of assays on these devices. The overall cost of devices fabricated by drawing are relatively lower (

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