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Fulltext Dynamic electronic institutions in agent oriented cloud robotic systems

Nagrath V, Morel O, Malik A, Saad N, Meriaudeau F

Springerplus, 2015;4:103.
PMID: 25763310 DOI: 10.1186/s40064-015-0810-4

The dot-com bubble bursted in the year 2000 followed by a swift movement towards resource virtualization and cloud computing business model. Cloud computing emerged not as new form of computing or network technology but a mere remoulding of existing technologies to suit a new business model. Cloud robotics is understood as adaptation of cloud computing ideas for robotic applications. Current efforts in cloud robotics stress upon developing robots that utilize computing and service infrastructure of the cloud, without debating on the underlying business model. HTM5 is an OMG's MDA based Meta-model for agent oriented development of cloud robotic systems. The trade-view of HTM5 promotes peer-to-peer trade amongst software agents. HTM5 agents represent various cloud entities and implement their business logic on cloud interactions. Trade in a peer-to-peer cloud robotic system is based on relationships and contracts amongst several agent subsets. Electronic Institutions are associations of heterogeneous intelligent agents which interact with each other following predefined norms. In Dynamic Electronic Institutions, the process of formation, reformation and dissolution of institutions is automated leading to run time adaptations in groups of agents. DEIs in agent oriented cloud robotic ecosystems bring order and group intellect. This article presents DEI implementations through HTM5 methodology.
Fulltext Hyperspectral venous image quality assessment for optimum illumination range selection based on skin tone characteristics

Shahzad A, Saad MN, Walter N, Malik AS, Meriaudeau F

Biomed Eng Online, 2014;13:109.
PMID: 25087016 DOI: 10.1186/1475-925X-13-109

Subcutaneous veins localization is usually performed manually by medical staff to find suitable vein to insert catheter for medication delivery or blood sample function. The rule of thumb is to find large and straight enough vein for the medication to flow inside of the selected blood vessel without any obstruction. The problem of peripheral difficult venous access arises when patient's veins are not visible due to any reason like dark skin tone, presence of hair, high body fat or dehydrated condition, etc.
Fulltext Evaluation of Deep Neural Networks for Semantic Segmentation of Prostate in T2W MRI

Khan Z, Yahya N, Alsaih K, Ali SSA, Meriaudeau F

Sensors (Basel), 2020 Jun 03;20(11).
PMID: 32503330 DOI: 10.3390/s20113183

In this paper, we present an evaluation of four encoder-decoder CNNs in the segmentation of the prostate gland in T2W magnetic resonance imaging (MRI) image. The four selected CNNs are FCN, SegNet, U-Net, and DeepLabV3+, which was originally proposed for the segmentation of road scene, biomedical, and natural images. Segmentation of prostate in T2W MRI images is an important step in the automatic diagnosis of prostate cancer to enable better lesion detection and staging of prostate cancer. Therefore, many research efforts have been conducted to improve the segmentation of the prostate gland in MRI images. The main challenges of prostate gland segmentation are blurry prostate boundary and variability in prostate anatomical structure. In this work, we investigated the performance of encoder-decoder CNNs for segmentation of prostate gland in T2W MRI. Image pre-processing techniques including image resizing, center-cropping and intensity normalization are applied to address the issues of inter-patient and inter-scanner variability as well as the issue of dominating background pixels over prostate pixels. In addition, to enrich the network with more data, to increase data variation, and to improve its accuracy, patch extraction and data augmentation are applied prior to training the networks. Furthermore, class weight balancing is used to avoid having biased networks since the number of background pixels is much higher than the prostate pixels. The class imbalance problem is solved by utilizing weighted cross-entropy loss function during the training of the CNN model. The performance of the CNNs is evaluated in terms of the Dice similarity coefficient (DSC) and our experimental results show that patch-wise DeepLabV3+ gives the best performance with DSC equal to 92 . 8 % . This value is the highest DSC score compared to the FCN, SegNet, and U-Net that also competed the recently published state-of-the-art method of prostate segmentation.
Fulltext Improved Active Disturbance Rejection Control for Trajectory Tracking Control of Lower Limb Robotic Rehabilitation Exoskeleton

Aole S, Elamvazuthi I, Waghmare L, Patre B, Meriaudeau F

Sensors (Basel), 2020 Jun 30;20(13).
PMID: 32630115 DOI: 10.3390/s20133681

Neurological disorders such as cerebral paralysis, spinal cord injuries, and strokes, result in the impairment of motor control and induce functional difficulties to human beings like walking, standing, etc. Physical injuries due to accidents and muscular weaknesses caused by aging affect people and can cause them to lose their ability to perform daily routine functions. In order to help people recover or improve their dysfunctional activities and quality of life after accidents or strokes, assistive devices like exoskeletons and orthoses are developed. Control strategies for control of exoskeletons are developed with the desired intention of improving the quality of treatment. Amongst recent control strategies used for rehabilitation robots, active disturbance rejection control (ADRC) strategy is a systematic way out from a robust control paradox with possibilities and promises. In this modern era, we always try to find the solution in order to have minimum resources and maximum output, and in robotics-control, to approach the same condition observer-based control strategies is an added advantage where it uses a state estimation method which reduces the requirement of sensors that is used for measuring every state. This paper introduces improved active disturbance rejection control (I-ADRC) controllers as a combination of linear extended state observer (LESO), tracking differentiator (TD), and nonlinear state error feedback (NLSEF). The proposed controllers were evaluated through simulation by investigating the sagittal plane gait trajectory tracking performance of two degrees of freedom, Lower Limb Robotic Rehabilitation Exoskeleton (LLRRE). This multiple input multiple output (MIMO) LLRRE has two joints, one at the hip and other at the knee. In the simulation study, the proposed controllers show reduced trajectory tracking error, elimination of random, constant, and harmonic disturbances, robustness against parameter variations, and under the influence of noise, with improvement in performance indices, indicates its enhanced tracking performance. These promising simulation results would be validated experimentally in the next phase of research.
Fulltext Towards health monitoring using remote heart rate measurement using digital camera: A feasibility study

Hassan MA, Malik AS, Fofi D, Karasfi B, Meriaudeau F

Measurement : journal of the International Measurement Confederation, 2020 Jan;149:106804.
PMID: 32287815 DOI: 10.1016/j.measurement.2019.07.032

The paper presents a feasibility study for heart rate measurement using a digital camera to perform health monitoring. The feasibility study investigates the reliability of the state of the art heart rate measuring methods in realistic situations. Therefore, an experiment was designed and carried out on 45 subjects to investigate the effects caused by illumination, motion, skin tone, and distance variance. The experiment was conducted for two main scenarios; human-computer interaction scenario and health monitoring scenario. The human-computer scenario investigated the effects caused by illumination variance, motion variance, and skin tone variance. The health monitoring scenario investigates the feasibility of health monitoring at public spaces (i.e. airports, subways, malls). Five state of the art heart rate measuring methods were re-implemented and tested with the feasibility study database. The results were compared with ground truth to estimate the heart rate measurement error. The heart rate measurement error was analyzed using mean error, standard deviation; root means square error and Pearson correlation coefficient. The findings of this experiment inferred promising results for health monitoring of subjects standing at a distance of 500 cm.
Novel health monitoring method using an RGB camera

Hassan MA, Malik AS, Fofi D, Saad N, Meriaudeau F

Biomed Opt Express, 2017 Nov 01;8(11):4838-4854.
PMID: 29188085 DOI: 10.1364/BOE.8.004838

In this paper we present a novel health monitoring method by estimating the heart rate and respiratory rate using an RGB camera. The heart rate and the respiratory rate are estimated from the photoplethysmography (PPG) and the respiratory motion. The method mainly operates by using the green spectrum of the RGB camera to generate a multivariate PPG signal to perform multivariate de-noising on the video signal to extract the resultant PPG signal. A periodicity based voting scheme (PVS) was used to measure the heart rate and respiratory rate from the estimated PPG signal. We evaluated our proposed method with a state of the art heart rate measuring method for two scenarios using the MAHNOB-HCI database and a self collected naturalistic environment database. The methods were furthermore evaluated for various scenarios at naturalistic environments such as a motion variance session and a skin tone variance session. Our proposed method operated robustly during the experiments and outperformed the state of the art heart rate measuring methods by compensating the effects of the naturalistic environment.
Fulltext Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images

Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibé D, Meriaudeau F

Biomed Eng Online, 2017 Jun 07;16(1):68.
PMID: 28592309 DOI: 10.1186/s12938-017-0352-9

BACKGROUND: Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers.
METHODS: The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations.
RESULTS AND CONCLUSION: Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.
Fulltext Investigation of Acetone Vapour Sensing Properties of a Ternary Composite of Doped Polyaniline, Reduced Graphene Oxide and Chitosan Using Surface Plasmon Resonance Biosensor

Usman F, Dennis JO, Mkawi EM, Al-Hadeethi Y, Meriaudeau F, Ferrell TL, et al.

Polymers (Basel), 2020 Nov 20;12(11).
PMID: 33233844 DOI: 10.3390/polym12112750

This work reports the use of a ternary composite that integrates p-Toluene sulfonic acid doped polyaniline (PANI), chitosan, and reduced graphene oxide (RGO) as the active sensing layer of a surface plasmon resonance (SPR) sensor. The SPR sensor is intended for application in the non-invasive monitoring and screening of diabetes through the detection of low concentrations of acetone vapour of less than or equal to 5 ppm, which falls within the range of breath acetone concentration in diabetic patients. The ternary composite film was spin-coated on a 50-nm-thick gold layer at 6000 rpm for 30 s. The structure, morphology and chemical composition of the ternary composite samples were characterized by FTIR, UV-VIS, FESEM, EDX, AFM, XPS, and TGA and the response to acetone vapour at different concentrations in the range of 0.5 ppm to 5 ppm was measured at room temperature using SPR technique. The ternary composite-based SPR sensor showed good sensitivity and linearity towards acetone vapour in the range considered. It was determined that the sensor could detect acetone vapour down to 0.88 ppb with a sensitivity of 0.69 degree/ppm with a linearity correlation coefficient of 0.997 in the average SPR angular shift as a function of the acetone vapour concentration in air. The selectivity, repeatability, reversibility, and stability of the sensor were also studied. The acetone response was 87%, 94%, and 99% higher compared to common interfering volatile organic compounds such as propanol, methanol, and ethanol, respectively. The attained lowest detection limit (LOD) of 0.88 ppb confirms the potential for the utilisation of the sensor in the non-invasive monitoring and screening of diabetes.
Fulltext SpecSeg Network for Specular Highlight Detection and Segmentation in Real-World Images

Anwer A, Ainouz S, Saad MNM, Ali SSA, Meriaudeau F

Sensors (Basel), 2022 Aug 30;22(17).
PMID: 36081012 DOI: 10.3390/s22176552

Specular highlights detection and removal in images is a fundamental yet non-trivial problem of interest. Most modern techniques proposed are inadequate at dealing with real-world images taken under uncontrolled conditions with the presence of complex textures, multiple objects, and bright colours, resulting in reduced accuracy and false positives. To detect specular pixels in a wide variety of real-world images independent of the number, colour, or type of illuminating source, we propose an efficient Specular Segmentation (SpecSeg) network based on the U-net architecture that is expeditious to train on nominal-sized datasets. The proposed network can detect pixels strongly affected by specular highlights with a high degree of precision, as shown by comparison with the state-of-the-art methods. The technique proposed is trained on publicly available datasets and tested using a large selection of real-world images with highly encouraging results.
Fulltext Acetone Vapor-Sensing Properties of Chitosan-Polyethylene Glycol Using Surface Plasmon Resonance Technique

Usman F, Dennis JO, Mkawi EM, Al-Hadeethi Y, Meriaudeau F, Fen YW, et al.

Polymers (Basel), 2020 Nov 04;12(11).
PMID: 33158093 DOI: 10.3390/polym12112586

To non-invasively monitor and screen for diabetes in patients, there is need to detect low concentration of acetone vapor in the range from 1.8 ppm to 5 ppm, which is the concentration range of acetone vapor in diabetic patients. This work presents an investigation for the utilization of chitosan-polyethylene glycol (PEG)-based surface plasmon resonance (SPR) sensor in the detection of trace concentration acetone vapor in the range of breath acetone in diabetic subjects. The structure, morphology, and elemental composition of the chitosan-PEG sensing layer were characterized using FTIR, UV-VIS, FESEM, EDX, AFM, and XPS methods. Response testing was conducted using low concentration of acetone vapor in the range of 0.5 ppm to 5 ppm using SPR technique. All the measurements were conducted at room temperature and 50 mL/min gas flow rate. The sensor showed good sensitivity, linearity, repeatability, reversibility, stability, and high affinity toward acetone vapor. The sensor also showed better selectivity to acetone compared to methanol, ethanol, and propanol vapors. More importantly, the lowest detection limit (LOD) of about 0.96 ppb confirmed the applicability of the sensor for the non-invasive monitoring and screening of diabetes.
Fulltext Dependence of the Optical Constant Parameters of p-Toluene Sulfonic Acid-Doped Polyaniline and Its Composites on Dispersion Solvents

Usman F, Dennis JO, Meriaudeau F, Ahmed AY, Seong KC, Fen YW, et al.

Molecules, 2020 Sep 25;25(19).
PMID: 32992942 DOI: 10.3390/molecules25194414

The optical constants of Para-Toluene sulfonic acid-doped polyaniline (PANI), PANIchitosan composites, PANI-reduced graphene-oxide composites and a ternary composite comprising of PANI, chitosan and reduced graphene-oxide dispersed in diluted p-toluene sulfonic acid (PTSA) solution and N-Methyl-2-Pyrrolidone (NMP) solvent have been evaluated and compared. The optical constant values were extracted from the absorbance spectra of thin layers of the respective samples. The potential utilization of the materials as the active sensing materials of surface plasmon resonance biosensors has also been assessed in terms of the estimated value of the penetration depth through a dielectric medium. The results show a reasonable dependence of the optical constant parameters on the solvent type. Higher real part refractive index (n) and real part complex dielectric permittivity (ε') values were observed for the samples prepared using PTSA solution, while higher optical conductivity values were observed for the NMP-based samples due to their relatively higher imaginary part refractive index (k) and imaginary part complex dielectric permittivity (ε″) values. In addition, NMP-based samples show improvement in terms of the penetration depth through a dielectric medium by around 9.5, 1.6, 4.4 and 2.9 times compared to PTSA-based samples for the PANI, PANI-chitosan, PANI-RGO and the ternary composites, respectively. Based on these, it is concluded that preparation of these materials using different dispersion solvents could produce materials of different optical properties. Thus, the variation of the dispersion solvent will allow the flexible utilization of the PANI and the composites for diverse applications.

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