Alternative Sensor Research Articles

Gesture Recognition through Mechanomyogram Signals: An Adaptive Framework for Arm Posture Variability.

In hand gesture recognition, classifying gestures across multiple arm postures is challenging due to the dynamic nature of muscle fibers and the need to capture muscle activity through electrical connections with the skin. This paper presents a gesture recognition architecture addressing the arm posture challenges using an unsupervised domain adaptation technique and a wearable mechanomyogram (MMG) device that does not require electrical contact with the skin. To deal with the transient characteristics of muscle activities caused by changing arm posture, Continuous Wavelet Transform (CWT) combined with Domain-Adversarial Convolutional Neural Networks (DACNN) were used to extract MMG features and classify hand gestures. DACNN was compared with supervised trained classifiers and shown to achieve consistent improvement in classification accuracies over multiple arm postures. With less than 5 minutes of setup time to record 20 examples per gesture in each arm posture, the developed method achieved an average prediction accuracy of 87.43% for classifying 5 hand gestures in the same arm posture and 64.29% across 10 different arm postures. When further expanding the MMG segmentation window from 200 ms to 600 ms to extract greater discriminatory information at the expense of longer response time, the intraposture and inter-posture accuracies increased to 92.32% and 71.75%. The findings demonstrate the capability of the proposed method to improve generalization throughout dynamic changes caused by arm postures during non-laboratory usages and the potential of MMG to be an alternative sensor with comparable performance to the widely used electromyogram (EMG) gesture recognition systems.

Realtime Monitoring and Analysis Based on Cloud Computing Internet of Things (CC-IoT) Technology in Detecting Forest and Land Fires in Riau Province

Forest and land fires in Riau are natural disasters that always repeat every time they enter the dry season. The solution of this research is to apply the leading technology of cloud computing internet of things (CC-IoT) to find out more quickly the existence of forest or land fires. This study uses Particle Argon (Photon) to connect to the internet and several IR Fire Detector sensors, DHT22 MQ2 and GPS Neo 6m. Particle Argon can receive input and perform processing so that it is connected using the CC-IoT concept to a web server so the users can monitor land conditions in real time. Based on the test results, it can be concluded that a fire detector using fire parameters (2000 = Normal and 2000 = Danger) , temperature (≤37 = Normal, 38 – 45 = Alert, and 46 = Danger), humidity (≤50 = Dry, 51 = Humid) , smoke (≤ 1700 = Normal, 1700 = Danger), and soil moisture can work well ( 3500 = Dry Moisture Content, 1500 to 3500 = Medium Moisture Content, and 1500 = High Moisture Content). The fire detection tool developed can detect fires in real time and also has a fire early detection function that is useful for anticipating land conditions to prevent fires. The results obtained from the test are that the sensor can read indications of fire, smoke, soil moisture with a success rate of 93% and send location data and sensor values to the website. The use of sensors has their respective roles so that if there is a problem with one of the sensors, the tool has an alternative sensor and can continue to function.

Polyfluoroalkyl Substances (PFASs) Detection Via Carbon Dots: A Review

PFASs are a class of highly persistent chemicals that are slowly infiltrating soils and waterways. Thus, there is a great need for fast, sensitive, and reliable techniques to detect PFASs. Conventional methods, such as LC-MS/SPE, allow high sensitivities. However, such methods can be complex and expensive. Considering this, it is not surprising that the scientific community has turned their attention to the search for alternatives. New types of PFAS sensors have been reported over the years, being generally part of three classes: optical, electrochemical, or hybrid sensors. Carbon dots (CDs) are new alternative fluorescent sensors that can present great affinity towards PFASs, while allowing for a fast response and promising sensitivity and selectivity. Furthermore, CDs have more attractive properties than traditional fluorophores and even metal-based nanomaterials that make them better candidates for sensing applications. Thus, CDs display great potential for permitting a fast and accurate quantification of PFASs. This review aims to serve as a basis for the future development and optimization of CD-based fluorescent sensors for PFASs.

Luminescent Metal-Organic Framework for the Selective Detection of Aldehydes.

The detection of toxic, hazardous chemical species is an important task because they pose serious risks to either the environment or human health. Luminescent metal-organic frameworks (LMOFs) as alternative sensors offer rapid and sensitive detection of chemical species. Interactions between chemical species and LMOFs result in changes in the photoluminescence (PL) profile of the LMOFs which can be readily detected using a simple fluorometer. Herein, we report the use of a robust, Zn-based LMOF, [Zn5(μ3-OH)2(adtb)2(H2O)5·5 DMA] (Zn-adtb, LMOF-341), for the selective detection of benzaldehyde. Upon exposure to benzaldehyde, Zn-adtb experiences significant luminescent quenching, as characterized through PL experiments. Photoluminescent titration experiments reveal that LMOF-341 has a detection limit of 64 ppm and a Ksv value of 179 M-1 for benzaldehyde. Furthermore, we study the guest-host interactions that occur between LMOF-341 and benzaldehyde through in situ Fourier transform infrared and computational modeling employing density functional theory. The results show that benzaldehyde interacts more strongly with LMOF-341 compared to formaldehyde and propionaldehyde. Our combined studies also reveal that the mechanism of luminescence quenching originates from an electron-transfer process.

SEISMIC WAVE TIME OF ARRIVAL AND PATH VELOCITY ESTIMATION USING 1-HZ GPS DATA

This paper proposed the method of determining seismic wave TOA and path velocity from Global Positioning System (GPS) data. High-rate GPS data from 13 Continuous Operational Reference Station (CORS) were utilized to obtain displacement and seismic waveform during the occurrence of Sumatra-Andaman 9.2Mw earthquake 2004. To detect seismic body waves using GPS data is difficult because of attenuated signal, therefore seismic surface waves have been used. The seismic wave TOA of between 116 s to 194 s was determined using time-frequency representation (TFR). The estimated seismic wave path velocities were found within the range of 3.8 km/s to 4.6 km/s, indicated as secondary wave or surface wave. To validate the estimated path velocity, it was compared with other research with an average value of 6 km/s to 13 km/s for body wave and 2 km/s to 5 km/s for surface wave. These results indicate that GPS CORS can be an alternative sensor for detecting earthquakes other than seismometers.

Sensor location models with reliable optimal solution for the observation of origin–destination matrix and route flows

Origin–destination matrix (ODM) is a key element in transportation studies. The emergence of new ITS technologies like Automatic Vehicle Identification (AVI) sensors makes the ODM observation problem more interesting in recent decades. However, sensors are subject to failure in reality which highlights the sensor failure phenomenon as a significant issue in real-case problems. This study intends to include the sensor failure phenomenon in AVI Sensor Location Model (SLM) for reliable observation of ODM and route flows. While reliability and cost are usually two conflicting objectives, we try to answer the following question “Is it possible to improve reliability without increasing the cost and only by changing sensor deployment?”. In addressing this study question, first, it is shown that the solution of recent AVI SLMs are not unique. Second, we show that the reliability level of multiple optimal solutions is not the same. Third, two Mixed Integer Linear Programming (MILP) AVI SLMs for reliable observation and parital observation of ODM/route flows are developed considering the sensor failure phenomenon. The models are formulated such that their solutions are selected from the set of multiple optimal solutions. Fourth, a linear surrogate term for reliability is introduced and mathematically proven to be included in the proposed models. Finally, the applicability of the proposed models is examined on several middle-scale networks and a real-size network. Furthermore, a heuristic algorithm is customized to solve the models for the real-size network. The results suggest that there might be alternative sensor deployment strategies with the same number of sensors as in the optimal solution but with higher level of reliability for ODM/route flows observation.

Influence of microphone tilt angle on instability identification in micromilling of thin-walled Ti6Al4V

High speed micromilling is the most preferred manufacturing process to fabricate complex 3D featured thin-walled structures like stents, turbine blades, micro-fins, cabin parts etc., that are extensively used in bio-medical, energy, automobile and aerospace sectors. Fabricating thin-walled structures with high dimensional and geometrical accuracy requires chatter free machining parameters. Different sensors like accelerometer, current sensors, laser based sensors, vibration pick-ups etc. are available for capturing the vibration while machining thin-walled structures. Each sensor has its own advantages and disadvantages, like laser based sensors adds noise to response as chips fly past in the laser path and accelerometer captures deflection of both micro-cutting tool and thin-walled workpiece. Hence, In-process chatter identification is a challenging task in thin-wall micromilling. Also, the Filtering of signals to remove the noise from signals captured via contact type and laser based sensors results in loss of low-amplitude chatter data in high-speed micromilling. Consequently, there is a need of sensor, which functions similar to contact type with less or no loss in low-amplitude chatter data. The proposed work compares vibration signal captured using accelerometer and microphone at different tilt angles for radial milling of thin-walled Ti6Al4V at different radial immersion to analyse the effect of tilt-angle on chatter identification. The recorded signals from both accelerometer and microphone are analysed in time domain by estimation and comparison of statistical parameters, in frequency domain by analysing the distributed frequency and in time–frequency domain by analysing the energy of the signals. Complete characterization of the signals in time, frequency and time–frequency domain/methods indicated that microphone at 60° tilt angle can be used as an alternative sensor for chatter onset identification without loss in chatter data irrespective of cutting condition. Machined surface analysis of thin-walled structures complements the microphone as an effective chatter identification sensor.

A Study on the Impact of Lombard Effect on Recognition of Hindi Syllabic Units Using CNN Based Multimodal ASR Systems

Research work on the design of robust multimodal speech recognition systems making use of acoustic and visual cues, extracted using the relatively noise robust alternate speech sensors is gaining interest in recent times among the speech processing research fraternity. The primary objective of this work is to study the exclusive influence of Lombard effect on the automatic recognition of the confusable syllabic consonant-vowel units of Hindi language, as a step towards building robust multimodal ASR systems in adverse environments in the context of Indian languages which are syllabic in nature. The dataset for this work comprises the confusable 145 consonant-vowel (CV) syllabic units of Hindi language recorded simultaneously using three modalities that capture the acoustic and visual speech cues, namely normal acoustic microphone (NM), throat microphone (TM) and a camera that captures the associated lip movements. The Lombard effect is induced by feeding crowd noise into the speaker’s headphone while recording. Convolutional Neural Network (CNN) models are built to categorise the CV units based on their place of articulation (POA), manner of articulation (MOA), and vowels (under clean and Lombard conditions). For validation purpose, corresponding Hidden Markov Models (HMM) are also built and tested. Unimodal Automatic Speech Recognition (ASR) systems built using each of the three speech cues from Lombard speech show a loss in recognition of MOA and vowels while POA gets a boost in all the systems due to Lombard effect. Combining the three complimentary speech cues to build bimodal and trimodal ASR systems shows that the recognition loss due to Lombard effect for MOA and vowels reduces compared to the unimodal systems, while the POA recognition is still better due to Lombard effect. A bimodal system is proposed using only alternate acoustic and visual cues which gives a better discrimination of the place and manner of articulation than even standard ASR system. Among the multimodal ASR systems studied, the proposed trimodal system based on Lombard speech gives the best recognition accuracy of 98%, 95%, and 76% for the vowels, MOA and POA, respectively, with an average improvement of 36% over the unimodal ASR systems and 9% improvement over the bimodal ASR systems.

Tools for Ground-Truth-Free Passive Client Density Mapping in MAC-Randomized Outdoor WiFi Networks.

In the past few years, data privacy legislation has hampered the ability of WiFi network operators to count and map client activity for commercial and security purposes. Indeed, since client device MAC devices are now randomized at each transmission, aggregating client activity using management frames such as Probe Requests, as has been common practice in the past, becomes problematic. Recently, researchers have demonstrated that, statistically, client counts are roughly proportional to raw Probe Request counts, thus somewhat alleviating the client counting problem, even if, in most cases, ground truth measurements from alternate sensors such as cameras are necessary to establish this proportionality. Nevertheless, localizing randomized MAC clients at a network site is currently an unsolved problem. In this work, we propose a set of nine tools for extending the proportionality between client counts and Probe Requests to the mapping of client densities in real-world outdoor WiFi networks without the need for ground truth measurements. The purpose of the proposed toolkit is to transform raw, randomized MAC Probe Request counts into a density map calibrated to an estimated number of clients at each position.

Thermal-Depth Odometry in Challenging Illumination Conditions

Traditional Visual Odometry (VO) methods that utilize visible cameras frequently degrade in challenging illumination environments. Alternative vision sensors such as thermal cameras are promising for all-day navigation since the delivered thermal images are invariant to ambient illumination. However, traditional VO techniques cannot be directly translated to the thermal domain due to poor thermal image quality. Besides, the thermal cameras stop image capture during the unique imaging mechanism (e.g., Non-Uniformity Correction (NUC)), making the thermal VO easily lose tracking. In this paper, we propose a thermal-depth odometry method that can fuse information from both types of sensors, thermal and depth cameras. The system front-end estimates 6-DoF camera motion via a semi-direct framework, fully exploiting thermographic data cues from raw thermal images. The depth information is aligned with the thermal images by extrinsic parameters to enhance the robustness of motion estimation. To overcome the challenge from the NUC, the proposed method introduces an NUC handling module, which can conduct pose estimation by registering multiple point clouds generated from depth images. The proposed method is evaluated on public datasets. The results demonstrate that the proposed method can provide competitive localization performance under different illumination.

Analysis of the Functional Capabilities of Technical tools for Measuring Aircraft Mechanical Parameters

The purpose of research is to analyze the functionality of classical and advanced sensors for controlling the mechanical parameters of aircraft using the example of strain and vibration sensors to identify their current areas of development.Methods. Research methods are based on the concepts of the theory of sensory systems, the theory of diagnostics and forecasting of the technical condition of aircraft. Methods of multi-criteria analysis, parametric and structural synthesis are used. The principles of operation, as well as the functionality of the main classical sensors used in aircraft to control the parameters of deformation and vibration, are analyzed. A critical assessment of the possibilities of using the analyzed sensors for implementation in various tasks of aviation diagnostics of mechanical parameters has been made.Results. It has been established that the impact of flight loads on the airframe and critical components of aircraft is accompanied by the appearance of hidden deformations in the form of mechanical stresses, which are divided into two components: normal and tangential. Analytical dependencies are obtained for calculating the above quantities using fiber-optic sensors with distributed Bragg cells that convert the change in their own linear dimensions into a change in the reflected wavelength. A necessary condition for obtaining correct measurement results is the temperature compensation of the cells, which makes it possible to localize the places of deformations with an accuracy up to the location of a particular cell. The practical results of using alternative sensors for detecting hidden deformations (cracks) based on radio frequency identification methods in various frequency ranges are presented.Conclusion. The development of the method for diagnosing stress-strain states of aircraft complex units is the use of frequency-Doppler fiber-optic sensors with a high signal-to-noise ratio and a spherical radiation pattern, which will allow developing technical means for monitoring the dynamics of internal deformations of controlled units in real time. As promising areas of research in the field of creating new sensors with new physical properties, fiber-optic Bragg sensors with an inclined grating should be considered.

Comparison of End-to-End Neural Network Architectures and Data Augmentation Methods for Automatic Infant Motility Assessment Using Wearable Sensors

Infant motility assessment using intelligent wearables is a promising new approach for assessment of infant neurophysiological development, and where efficient signal analysis plays a central role. This study investigates the use of different end-to-end neural network architectures for processing infant motility data from wearable sensors. We focus on the performance and computational burden of alternative sensor encoder and time series modeling modules and their combinations. In addition, we explore the benefits of data augmentation methods in ideal and nonideal recording conditions. The experiments are conducted using a dataset of multisensor movement recordings from 7-month-old infants, as captured by a recently proposed smart jumpsuit for infant motility assessment. Our results indicate that the choice of the encoder module has a major impact on classifier performance. For sensor encoders, the best performance was obtained with parallel two-dimensional convolutions for intrasensor channel fusion with shared weights for all sensors. The results also indicate that a relatively compact feature representation is obtainable for within-sensor feature extraction without a drastic loss to classifier performance. Comparison of time series models revealed that feedforward dilated convolutions with residual and skip connections outperformed all recurrent neural network (RNN)-based models in performance, training time, and training stability. The experiments also indicate that data augmentation improves model robustness in simulated packet loss or sensor dropout scenarios. In particular, signal- and sensor-dropout-based augmentation strategies provided considerable boosts to performance without negatively affecting the baseline performance. Overall, the results provide tangible suggestions on how to optimize end-to-end neural network training for multichannel movement sensor data.

A Variational Bayesian Blind Calibration Approach for Air Quality Sensor Deployments

Air pollution has become a global threat to urban environments and public health. Low-cost air quality sensor systems have been deployed to support fine-grained monitoring, and in-field calibration methods are necessary to assure the accuracy of sensor observation. Existing methods use in-field reference to train some specific calibration models. Nevertheless, collecting sufficient reference data after deployment is challenging. First, the synchronized data pair of reference observation is hard to obtain. Thus, many blind calibration approaches rely on some alternative reference like a historical reference before sensor observation. However, since the relationship model between alternative reference and sensor observation becomes more complicated, the amount of collected data is still insufficient to learn the complex model. To address the above challenge, we propose a Variational Bayesian Blind Calibration Algorithm. Our method introduces a supplement set that only involves historical reference and synchronized reference measurement without sensor observation to alleviate the few-reference pressure. Large amounts of the introduced supplement sets have been collected in different cities by accurate stations, which we adopt to conduct prediction tasks and build a Bayesian model to learn how to combine the formulated prediction task and target calibration task in a theoretically better way. Furthermore, we design a variational Bayesian framework to make good use of the easily obtained supplement set to train a better prediction model for improving calibration performance and relieving the pressure of collecting costly reference measurements after deployment. Evaluations of real-world and synthetic datasets show that the proposed approach has a better performance than previous baselines.

Fluorous Liquids for Magnetic Resonance-Based Thermometry with Enhanced Responsiveness and Environmental Degradation.

Accurate temperature measurement via magnetic resonance is valuable for both in vitro and in vivo analysis of local tissue for evaluating disease pathology and medical interventions. 1H MRI-based thermometry is used clinically but is susceptible to error from magnetic field drift and low sensitivity in fatty tissue and requires a reference for absolute temperature determination. As an alternative, perfluorotributylamine (PFTBA), a perfluorocarbon liquid for 19F MRI thermometry, is based on chemical shift responsiveness and approaches the sensitivity of 1H MRI thermometry agents; however, environmental persistence, greenhouse gas concerns, and multiple resonances which can lead to MRI artifacts indicate a need for alternative sensors. Using a 19F NMR-based structure-property study of synthetic organofluorine molecules, this research develops new organofluorine liquids with improved temperature responsiveness, high signal, and reduced nonmagnetically equivalent fluorine resonances. Environmental degradation analysis using reverse-phase HPLC and quantitative 19F NMR demonstrates a rapid degradation profile mediated via the aryl fluorine core of temperature sensors. Our findings show that our lead liquid temperature sensor, DD-1, can be made in high yield in a single step and possesses an improved responsiveness over our prior work and an 83% increase in aqueous thermal responsiveness over PFTBA. Degradation studies indicate robust degradation with half-lives of less than two hours under photolysis conditions for the parent compound and formation of other fluorinated products. The improved performance of DD-1 and its susceptibility to environmental degradation highlight a new lead fluorous liquid for thermometry applications.

Transmission-error- and vibration-based condition monitoring of gear wear with contaminated lubricant

The presence of contaminant particles in lubricants could cause severe gear wear and damage. Conventional condition monitoring techniques, i.e., gear inspection and oil/wear debris analysis, are often utilised to assess the presence of wear due to contaminants and its severity such as wear depth. However, the traditional approaches require experience or off-line analysis. Although vibration is a non-invasive technique for fault detection and diagnostics, it is less well developed for wear analysis. Recently, an alternative sensor technology, i.e., transmission error (TE), combining angular measurements obtained from the input and output shafts has shown strong potential for wear monitoring. This work aims to investigate the usage of vibration and TE to identify the existence of contaminants and measure the gear wear severity caused by the oil contamination. This is achieved by conducting gear wear tests on a spur gearbox rig, using clean and contaminated lubricants with silica sand. The test in a normal lubrication condition provides a healthy reference of the spur gearbox under a nominal operating condition. In addition to the nominal operating condition, TE and vibration measurements are collected throughout the tests at different operating conditions, together with moulds of the gear tooth surfaces for wear analysis. The results show that the absolute TE can quantitively measure wear depth, and the RMS of vibration can qualitatively correlate with the trend of the average wear depths. The proposed technique extends the field of using TE to diagnose and predict gear wear in contaminated lubricant conditions.

Towards Enhanced Eddy Current Testing Array Probes Scalability for Powder Bed Fusion Layer-Wise Imaging

This work presents a new eddy current testing array probe and readout electronics that target the layer-wise quality control in powder bed fusion metal additive manufacturing. The proposed design approach brings important benefits to the sensors' number scalability, exploring alternative sensor elements and minimalist signal generation and demodulation. Small-sized, commercially available surface-mounted technology coils were evaluated as an alternative to usually employed magneto-resistive sensors, demonstrating low cost, design flexibility, and easy integration with the readout electronics. Strategies to minimize the readout electronics were proposed, considering the specific characteristics of the sensors' signals. An adjustable single phase coherent demodulation scheme is proposed as an alternative to traditional in-phase and quadrature demodulation provided that the signals under measurement showed minimal phase variations. A simplified amplification and demodulation frontend using discrete components was employed together with offset removal, vector amplification, and digitalization implemented within the microcontrollers' advanced mixed signal peripherals. An array probe with 16 sensor coils and a 5 mm pitch was materialized together with non-multiplexed digital readout electronics, allowing for a sensor frequency of up to 1.5 MHz and digitalization with 12 bits resolution, as well as a 10 kHz sampling rate.

Structural health monitoring for corrosion induced thickness loss in marine plates subjected to random loads

In the highly corrosive environment where marine structures operate, the current industry practice is to overcompensate for corrosion induced thickness loss (CITL) and replace any parts that score below specified allowable limits during scheduled maintenance. Hence, there would be immediate benefits from the implementation of a Structural Health Monitoring (SHM) system that would allow for predictive, condition-based maintenance. The objective of this study is to investigate and assess the effectiveness of state-of-the-art statistical pattern recognition (SPR) and machine learning (ML) methods in association with alternative sensor grid architectures as an SHM scheme for detecting CITL, under highly variable operational conditions. For this purpose, a simple rectangular plate at different corrosion levels (uniform and pitting) was considered as a reference structural element, which was subjected to a stochastic pressure load. Strain response data were produced using Finite Element (FE) simulations and were treated under a probabilistic framework. Elements from detection theory and ML were taken under consideration in order to construct alternative detectors and assess their performance.

Gold nanoparticles-based colorimetric assay of pesticides: A critical study on aptamer’s role and another alternative sensor array strategy

Pesticide residue is an important problem in environmental pollution, and colorimetric methods are suitable for on-site fast assay of pesticides. Analytes-triggered aptamer desorption from gold nanoparticles (AuNPs) has been widely used to develop label-free colorimetric assays. But the direct interactions between pesticides and AuNPs have not been considered previously, leading to one possible misinterpretation of analysis results. In this work, we studied the direct adsorption of pesticides on AuNPs, and further performed a comprehensive analysis of aptamer’s role. Here the colorimetric response was independent on DNA sequences, and a similar aggregation of AuNPs was shown for all the tested DNA sequences. Our study demonstrated a misinterpretation in the previous assays, while pesticides adsorption by AuNPs was the main mechanism for these colorimetric assays. Finally, we designed another alternative sensor array based on different parameters (pH, ionic strength and [DNA]) for accurate discrimination of four pesticides including acetamiprid, carbendazim, pymetrozine and thiabendazole. Due to the different functional groups in these four pesticides, the fingerprints of AuNPs’ colorimetric responses allow intuitively identifying different pesticides for environmental or food application.

A Kinematically Constrained Kalman Filter for Sensor Fusion in a Wearable Origami Robot

AbstractSensing for wearable robots is an ongoing challenge, especially given the recent trend of soft and compliant robots. Recently, a wearable origami exoshell has been designed to sense the user’s torso motion and provide mobility assistance. The materials of the exoshell contribute to a lightweight design with compliant joints, which are ideal characteristics for a wearable device. Common sensors are not ideal for the exoshell as they compromise these design characteristics. Rotary encoders are often rigid metal devices that add considerable weight and compromise the flexibility of the joints. Inertial measurement unit sensors are affected by environments with variable electromagnetic fields and therefore not ideal for wearable applications. Hall effect sensors and gyroscopes are utilized as alternative compatible sensors, which introduce their own set of challenges: noisy measurements and drift due to sensor bias. To mitigate this, we designed the Kinematically Constrained Kalman filter for sensor fusion of gyroscopes and Hall effect sensors, with the goal of estimating the human’s torso and robot joint angles. We augmented the states to consider bias related to the torso angle in order to compensate for drift. The forward kinematics of the robot is incorporated into the Kalman filter as state constraints to address the unobservability of the torso angle and its related bias. The proposed algorithm improved the estimation performance of the torso angle and its bias, compared to the individual sensors and the standard Kalman filter, as demonstrated through bench tests and experiments with a human user.

Expanding the circularity of plastic and biochar materials by developing alternative low environmental footprint sensors

Flexible screen-printing technology modified with nano/material coating boosted the manufacturing of highly sensitive electrochemical sensors for fast and easy-to-handle tests in different application fields.