Augmented Reality for Health Monitoring Laboratory

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Education

Projects

Research

  • eXtended Reality (XR) for Parkinson’s disease

      Application fields Biomedical Engineering, Neurology, Human-Computer Interaction Technologies eXtended Reality (Hololens 2) Goals Parkinson’s Disease (PD) is a neurodegenerative disorder of the central nervous system. PD is the most frequent of movement disorders, resulting chronic and slowly progressive, involving different motor, behavioural and cognitive functions, affecting consequently patient’s quality life. In the last few years, technological progress has provided several extended reality (XR) applications for PD, both in diagnostic and rehabilitation fields. In this scenario, the main aim of the present study is to investigate the use of an XR-based system such as a support diagnostic tool to assess bradykinesia, referring to finger-tapping gestures, in parkinsonian patients. Bradykinesia is the most common symptom of PD. It is characterized by extreme difficulty and slowness on the part of the patient in performing even the simplest movements, resulting in a general sense of...

    Read more: eXtended...

  • Artificial Intelligence-assisted near-infrared fluorescence angiography with indocyanine green after colorectal resections

        Application field Laparoscopic surgery for colorectal cancer Technology Artificial Intelligence (AI), Computer Vision, Data Analysis Goals Development of artificial intelligence-based analysis method in indocyanine green (ICG) angiography to perform real-time perfusion analysis: Identification of the optimal line for cancer resection   Assessment of the goodness of the vascularization The AI techniques applied to near-infrared fluorescence angiography with indocyanine green is proposed as objective method to support surgeons in their decision-making processes, improving treatment and post-operative complications prevention. Activities Identification of appropriate tracker algorithms to enable fast and accurate tracking of the selected region of interest. Data pre-processing (e.g., appropriate color space for analysis) to make it suitable for the machine learning system. Design and implementation of AI systems for the evaluation of bowel perfusion during colorectal surgery. Challenges...

    Read more: Artificial...

  • AI for IoT cybersecurity

    Application field Cybersecurity, IoT Technology Cyberattacks, Machine Learning Goals Development of methodologies for assessing the performance of cyberattacks, along with its uncertainty. Profiled attacks relying on machine learning are mainly considered. Methods like cross-validation and Monte Carlo can be exploited to assess the uncertainty and the final aim is to evaluate the vulnerability of an embedded device from the hardware point of view. In this process, measurements are strongly involved with reference to the acquisition of power traces or other leakages during the operation of the device under test. Then, data processing has a crucial role in the vulnerability assessment in case of presence or absence of countermeasures. Furthermore, analysis of a sensor network behavior in the event of cyberattacks, such as Denial of Service or Man-In-The-Middle is critical for development of first reaction behavior methodologies. Development of methodologies for anomaly detection,...

    Read more: AI for IoT...

  • eXtended Reality (XR) to Foster the EEG-based Measurement of Mental States

      Application fields Psychology, Psychiatry, Wellbeing Technologies eXtended Reality (XR), Electroencephalography (EEG), Machine Learning (ML) Goals In psychometric measurements, the measurand is a 'latent variable' of a given subject, accessible through the perception of the participant in the experimental activity. Mental states comprise a heterogeneous class that includes perception, pain experience, belief, desire, intention, emotion and memory. EEG-based measurements of mental states constitute a new approach that can provide real-time monitoring. eXtended Reality (XR) is increasingly being used as an umbrella term that includes virtual reality (VR), augmented reality (AR) and mixed reality (MR) technologies. Especially with reference to VR, the high sense of presence offered, the possibility of immersion of the user in realistic scenarios, and the multi-sensory and multi-perceptual stimulation offer the subject extremely evocative experiences. The following research goals are...

    Read more: eXtended...

  • Active Brain-Computer Interface

    Application field Rehabilitation, gaming, control Technology Electroencephalography (EEG), artificial intelligence, extended reality Goals Development of a fully wearable brain-computer interface system based on motor imagery that leverages multiple modalities of extended reality delivered neurofeedback. The aims are to enhance the detection of the user's sensorimotor rhythms and to speed up motor imagery skills acquisition. Different types of feedback are delivered both as standalone or jointly by relying on the results of online processing of brain signal. Activities Development of artifact removal technique for low density EEG. Evaluation of motor imagery EEG feature extraction and classification algorithms exploiting both classic machine learning and deep learning approaches. Classification of multiple motor imagery tasks. Development of extended reality applications to deliver neurofeedback. Design and implementation of experimental campaigns. Metrological analysis of the...

    Read more: Active...

Application field

Psychotherapy, Neuroscience

Technology

eXtended Reality (XR), Brain Computer Interface (BCI), Machine Learning

Goals

  • Identifying typical EEG features for acrophobia;
  • Prototyping a flexible rehabilitation device with a high level of customization that can monitor the variation in phobic intensity experienced by the subject;
  • Automatic modulation of the stimulating context.

Activities

  • Identification of standardized metrics for assessing the severity of acrophobia;
  • Creation of an experimental apparatus for stimulation with variable intensity levels based on specified psychometric scales;
  • EEG measurement during exposure to stimuli in virtual reality;
  • Identification of EEG features related to variation of the level of phobic intensity experienced by the subject;
  • Realization of an adaptive prototype able to modulate the phobic stimulus in response to the phobic intensity experienced by the subject;
  • Identification of a therapeutic protocol capable of automatically adapting the stimulus to the subject’s response measured on the EEG basis;
  • Experimental validation.

Challenges

  • Exploring neurocorrelates related to specific phobias
  • Create a real-time adaptive stimulation context

Partnership

  • Graz University of Technology
  • IDEGO srl

Bibliography

  • Apicella, S. Barbato, L.A. Barradas Chacon, G. D’Errico, L.T. De Paolis, L. Maffei, P. Massaro, G. Mastrati, N. Moccaldi, A. Pollastro, S.C. Wriessenegger. EEG Correlates of Fear of Heights in a VR Environment. Acta Imeko, 2023 [submitted]

 

 

Application fields

Neurological Diseases

Technologies

Medical Statistics ,Machine Learning (ML)

Goals

Diagnosis of neurological diseases is a growing concern and one of the most difficult challenges for modern medicine due to high social impact and costs. To study MS epidemiology, it is preferable to use population-based studies. These studies are useful also to describe comorbidities, care pathways, the burden of this disease and to plan the management strategies and resource allocation necessary to cope with it. The presence of comorbid disease is a critical issue for clinicians given the breadth of adverse impacts with which it is associated. Especially in MS, comorbidity is associated with a longer delay between MS symptom onset and diagnosis, more severe disability at diagnosis even after accounting for diagnostic delays, greater disability progression, increased health-care utilization, and higher mortality . Therefore, using routinely collected data to evaluate the comorbidity is a key importance to manage MS. An example is a study conducted by Palladino et al.  that showed an association between multiple sclerosis and increased risk of macrovascular disease by using a population-based cohort of 84,823 people with or without multiple sclerosis. Factors affecting the distribution of MS, that may help determine what causes the disease include age, sex, race, genetics, and geographical location. Also in this setting, big data have been used, in fact, a retrospective cohort of more than 9 million person-years has been used to calculate the Incidence of multiple sclerosis in multiple racial and ethnic group . Regarding the geographical location factor, a population-based cohort study about 6,6 million has been conducted to assess the association between residential proximity to major roadways and the incidence of MS in Ontario, Canada  . The above demonstrates the usefulness of routinely collected data to investigate about MS in order to improve patient health and quality of life and to optimize health resource use.

 Merging of health data-sets Nowadays, extracting information about a certain matter, i.e. merging data-sets belonging to different settings is the new challenge in current research. Especially in a health care setting, integrating clinical data with financial and operational data, is important to get a more complete picture of care and for improvement value of the health system. The paper by Vemulapalli et al.  highlights the possibilities offered by merging registries with large administrative databases. The strength of registries is to comprise relevant variables for the considered disease and that are not available when using a clinical database. In fact, administrative databases provide detailed information on hospitalizations and individual health care costs, which are otherwise difficult to collect during patient follow-up . Merging of the different datasets has been used also in multiple sclerosis setting in the Campania region. A linkage between the administrative and clinical datasets has been utilized for MS case-finding algorithm validation . The dataset was created by merging different data sources of the Campania Region. In particular, the cohort comprised three database: Hospital Discharge Record database, Regional Drug Prescription database and Outpatient database. This heterogeneous database, representing 10% of the Italian population, could be utilized to validate healthcare economics, public health actions existing or pending implemented and clinical scales such as the Expanded Disability Status Scale (EDSS) for multiple sclerosis.

 

Activities

  • Explore geographical variations accounting for undetected cases: Understanding the real epidemiology condition is an essential factor for implement public health initiatives focusing on health promotion.
  • Validation of clinical scales: Measurement of a condition as variable as MS is notoriously difficult but the need for evidence-based decisions has highlighted the importance of the development of adequate measures.
  • Economic evaluation of health resource: An economic evaluation of healthcare resources helps us to assure the efficient use of them.
  • Public health policy evaluation : Integrated care models have been proposed to improve outcomes and optimize resource allocation for the care of individuals with complex chronic conditions.

 

Challenges

In research scientific setting, the re-adaptability of research methods is an important concept. The challenge is to provide elasticity to an analysis method to may contextualize it in several settings optimizing its use. Therefore, developed methodology to answer the about propose aims could be applied to other condition to develop and more comprehensive analysis of the public healthcare.

Bibliography

  • L. Brodie, M. Greaves and J.A. Hendler, Databases and AI: The Twain Just Met, 2011 STI Semantic Summit,Riga, Latvia, July 6-8, 2011
  • Christian Bizer, Peter A Boncz , Michael L. Brodie , Orri Erling The meaningful use of big data: four perspectives – four challenges, ACM SIGMOD RecordJanuary 2012
  • Benke K.K. Uncertainties in Big Data when using Internet Surveillance Tools and Social Media for determination of Patterns in Disease Incidence. JAMA Ophthalmol. 2017;135:402. doi: 10.1001/jamaophthalmol.2017.0138.
  • Rubin R.A. Precision Medicine Approach to Clinical Trials. JAMA. 2016;316:1953-1955. doi:10.1001/jama.2016.12137.
  • Rubin R. Precision Medicine: The Future or Simply Politics? JAMA. 2015;313:1089-1091. doi:10.1001/jama.2015.0957.
  • Ashley E.A. Towards Precision Medicine. Nat. Rev. Genet. 2016;17:507-522. doi: 10.1038/nrg.2016.86.
  • Gulshan V., Peng L., Coram M., Stumpe M.C., Wu D., Narayanaswamy A., Venugopalan S., Widner K., Madams T., Cuadros J., et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.
  • David W. Bates, Suchi Saria, Lucila Ohno-Machado, Anand Shah, and Gabriel Escobar Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients.
  • Travis B. Murdoch, MD, MSc; Allan S. Detsky, MD, PhD The Inevitable Application of Big Data to Health Care
  • Kalincik, T.; Butzkueven, H. Observational data: Understanding the real MS world. Mult. Scler. J. 2016,22, 1642-1648.
  • Birnbaum, H.G.; Cremieux, P.Y.; Greenberg, P.E.; LeLorier, J.; Ostrander, J.A.; Venditti, L. Using healthcare claims data for outcomes research and pharmacoeconomic analyses. Pharmacoeconomics 1999, 16, 1-8.
  • Marcello Moccia , Vincenzo Brescia Morra , Roberta Lanzillo , Ilaria Loperto ,Roberta Giordana , Maria Grazia Fumo , Martina Petruzzo , Nicola Capasso , Maria Triassi2,Maria Pia Sormani and Raffaele Palladino Multiple Sclerosis in the Campania Region (South Italy): Algorithm Validation and 2015-2017 Prevalence
  • BergamaschiaC.MontomolibE.CandeloroaM.C.MontibR.CioccalebL.BernardinellibP.FratinocV.Cosiathe PREMS Bayesian mapping of multiple sclerosis prevalence in the province of Pavia, northern Italy
  • Eleonora Cocco , Claudia Sardu, Rita Massa, Elena Mamusa, Luigina Musu, Paola Ferrigno, Maurizio Melis, Cristina Montomoli, Virginia Ferretti, Giancarlo Coghe, Giuseppe Fenu, Jessica Frau, Lorena Lorefice, Nicola Carboni, Paolo Contu, Maria G Marrosu. Epidemiology of Multiple Sclerosis in South-Western Sardinia
  • Martina Petruzzo, Raffaele Palladino, Antonio Nardone, Roberta Lanzillo, Vincenzo Brescia Morra, Marcello Moccia. The impact of diagnostic criteria and treatments on the 20-year costs for treating relapsing-remitting multiple sclerosis.
  • Raffaele Palladino, Ruth Ann Marrie, ; Azeem Majeed, ; et al, Evaluating the Risk of Macrovascular Events and Mortality Among People With Multiple Sclerosis in England

 

 

Application field

Affective computing, psychology, rehabilitation, education, learning

Technology

Electroencephalography, artificial intelligence, extended reality, statistics

Goals

Development of adaptive systems capable to recognize and adjust to the current emotional or cognitive state of the user. Therefore, specific neural patterns for a determined phenomenon must be identified in order to maximize the recognition performances and the cross-subject generalizability of the results.

In the emotional state detection, the aims are the EEG-based assessment of valence and arousal states using as reference theory the Circumplex Model of Affect and the assessment of different levels of fear of fear of height. In the engagement detection, the aim is to assess both the cognitive and the emotional engagement.

In cognitive mental state detection, the main focus concerns the real-time EEG-based detection of executive functions (working memory, inhibition, flexibility) fatigue for fall prediction and targeted rehabilitation intervention in cases of neurodegenerative disorders or ageing.

 

Activities

  • Literature search and design of the experimental protocol.
  • Initial screening of the sample and experimental campaign for the EEG signal acquisitions.
  • Pre-processing of the EEG signal.
  • Feature extraction and selection.
  • Prototyping of the adaptive system able to detect and adjust to the current state of the user and experimental validation.

Challenges

  • Improving the recognition performances and the cross-subject generalizability
  • Management of the uncertainty: for the emotion recognition, the uncertainty is related to the reference theory, the eliciting stimuli, and the subjective reaction of the subject; in case of executive function fatigue detection, the uncertainty is related to the actual assessment that the subject is in a fatigued condition.
  • Minimizing the number of EEG channels.

 

Bibliography

  • Apicella, A., Arpaia, P., Mastrati, G., & Moccaldi, N. (2021). EEG-based detection of emotional valence towards a reproducible measurement of emotions. Scientific Reports, 11(1), 1-16.
  • Apicella, A., Arpaia, P., Isgrò, F., Mastrati, G., & Moccaldi, N. (2022). A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels. IEEE Access, 10, 117411-117428.
  • Apicella, A., Arpaia, P., Giugliano, S., Mastrati, G., & Moccaldi, N. (2021). High-wearable EEG-based transducer for engagement detection in pediatric rehabilitation. Brain-Computer Interfaces, 1-11.
  • Apicella, A., Arpaia, P., Esposito, A., Mastrati, G., & Moccaldi, N. (2022, May). Metrological foundations of emotional valence measurement through an EEG-based system. In 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1-6). IEEE.
  • Apicella, A., Arpaia, P., Mastrati, G., Moccaldi, N., & Prevete, R. (2020, June). Preliminary validation of a measurement system for emotion recognition. In 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA) (pp. 1-6). IEEE.
  • Angrisani, L., Arpaia, P., Esposito, A., Gargiulo, L., Natalizio, A., Mastrati, G., ... & Parvis, M. (2021). Passive and active brain-computer interfaces for rehabilitation in health 4.0. Measurement: Sensors, 18, 100246.
  • Arpaia, P., Coyle, D., D’Errico, G., De Benedetto, E., De Paolis, L. T., du Bois, N., ... & Vallefuoco, E. (2022). Virtual Reality Enhances EEG-Based Neurofeedback for Emotional Self-regulation. In International Conference on Extended Reality (pp. 420-431). Springer, Cham.
  • Apicella, A., Arpaia, P., Cataldo, A., D’Errico, G., Marocco, D., Mastrati, G., ... & Vallefuoco, E. (2022, October). Reproducible Assessment of Valence and Arousal Based on an EEG Wearable Device. In 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (pp. 661-666). IEEE.
  • Arpaia, P., Calabrese, L., Chiarella, S. G., D’Errico, G., De Paolis, L. T., Grassini, S., ... & Vallefuoco, E. (2022, October). Mindfulness-based Emotional Acceptance in Combination with Neurofeedback for Improving Emotion Self-Regulation: a Pilot Study. In 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (pp. 465-470). IEEE.
  • Arpaia, P., Mastrati, G., & Moccaldi, N. MetroXRaine competition-Emotion dataset.
  • Arpaia, P., Covino, A., Cristaldi, L., Frosolone, M., Gargiulo, L., Mancino, F., ... & Moccaldi, N. (2022). A Systematic Review on Feature Extraction in Electroencephalography-Based Diagnostics and Therapy in Attention Deficit Hyperactivity Disorder. Sensors, 22(13), 4934.
  • Angrisani, Leopoldo, et al. "Passive and active brain-computer interfaces for rehabilitation in health 4.0." Measurement: Sensors 18 (2021): 100246.
  • Apicella, Andrea, et al. "EEG-based system for Executive Function fatigue detection." 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). IEEE, 2022.
  • Leopoldo Angrisani, Andrea Apicella, Pasquale Arpaia, Andrea Cataldo, Anna Della Calce, Allegra Fullin, Ludovica Gargiulo, Luigi Maffei, Nicola Moccaldi, Andrea Pollastro. Instrumentation for EEG-based monitoring of the executive functions in a dual-task framework (MEMEA 2022)

 

Application field

Health 4.0, Industry 4.0, Daily Life

Technologies

Brain-Computer Interface, eXtended Reality, Artificial Intelligence

Goals

Development of highly wearable, non-invasive Brain-Computer Interfaces (BCIs) based on the detection of the Steady-State-Visually Evoked Potentials (SSVEPs).

eXtended Reality (XR) is proposed as an innovative technology to render the flickering stimuli necessary to the SSVEPs elicitation. The user, by wearing an XR headset and a portable EEG acquisition unit, is able to send the desired command to the target application, by simply looking at the desired icon, which is overlayed to the surrounding world.

In this way, the user is not forced to stand in front of a PC Monitor and will be able to use the proposed BCI in a wider variety of applications, from daily life to Health and Industry 4.0.

 

Activities

  • Experimental characterization of EEG acquisition units and XR headsets.
  • Development of algorithms for classifying EEG data by also exploiting Machine Learning and Deep Learning technologies.
  • Prototyping of portable and wearable devices to be used for daily-life, health, and industry applications.

 

Challenges

  • Improving the accuracy and information transfer rate of the system.
  • Reducing the users’ eye fatigue while using the system.

Bibliography

  • Apicella et al., "Adoption of Machine Learning Techniques to Enhance Classification Performance in Reactive Brain-Computer Interfaces," 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2022, pp. 1-5, doi: 10.1109/MeMeA54994.2022.9856441.
  • Angrisani et al., "A ML-based Approach to Enhance Metrological Performance of Wearable Brain-Computer Interfaces," 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2022, pp. 1-5, doi: 10.1109/I2MTC48687.2022.9806518.
  • Apicella et al., "Enhancement of SSVEPs Classification in BCI-Based Wearable Instrumentation Through Machine Learning Techniques," in IEEE Sensors Journal, vol. 22, no. 9, pp. 9087-9094, 1 May1, 2022, doi: 10.1109/JSEN.2022.3161743.
  • Arpaia, Pasquale, et al. "Performance enhancement of wearable instrumentation for AR-based SSVEP BCI." Measurement196 (2022): 111188.
  • Arpaia, Pasquale, et al. "Highly wearable SSVEP-based BCI: Performance comparison of augmented reality solutions for the flickering stimuli rendering." Measurement: Sensors18 (2021): 100305.
  • Arpaia, Pasquale, Egidio De Benedetto, and Luigi Duraccio. "Design, implementation, and metrological characterization of a wearable, integrated AR-BCI hands-free system for health 4.0 monitoring." Measurement177 (2021): 109280.
  • Arpaia, Pasquale, et al. "A Wearable SSVEP BCI for AR-Based, Real-Time Monitoring Applications." 2021 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2021
  • Arpaia, S. Criscuolo, E. D. Benedetto, N. Donato and L. Duraccio, "Evaluation of the Effectiveness of a Wearable, AR-based BCI for Robot Control in ADHD Treatment," 2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), 2022, pp. 630-634, doi: 10.1109/MetroXRAINE54828.2022.9967655.
  • Arpaia, S. Criscuolo, E. De Benedetto, N. Donato and L. Duraccio, "A Wearable AR-based BCI for Robot Control in ADHD Treatment: Preliminary Evaluation of Adherence to Therapy," 2021 15th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), 2021, pp. 321-324, doi: 10.1109/TELSIKS52058.2021.9606352.
  • Arpaia, C. Bravaccio, G. Corrado, L. Duraccio, N. Moccaldi and S. Rossi, "Robotic Autism Rehabilitation by Wearable Brain-Computer Interface and Augmented Reality," 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2020, pp. 1-6, doi: 10.1109/MeMeA49120.2020.9137144.
  • Arpaia, L. Duraccio, N. Moccaldi and S. Rossi, "Wearable Brain–Computer Interface Instrumentation for Robot-Based Rehabilitation by Augmented Reality," in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 9, pp. 6362-6371, Sept. 2020, doi: 10.1109/TIM.2020.2970846.
  • Angrisani, P. Arpaia, A. Esposito and N. Moccaldi, "A Wearable Brain–Computer Interface Instrument for Augmented Reality-Based Inspection in Industry 4.0," in IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 4, pp. 1530-1539, April 2020, doi: 10.1109/TIM.2019.2914712.
  • Arpaia, L. Callegaro, A. Cultrera, A. Esposito and M. Ortolano, "Metrological Characterization of Consumer-Grade Equipment for Wearable Brain–Computer Interfaces and Extended Reality," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-9, 2022, Art no. 4002209, doi: 10.1109/TIM.2021.3127650.

 

 

Application field

Diabetology, Orthopedy, and Aesthetic Medicine

Technology

Bio-Impedance spectroscopy, Finite Element Modeling

Goals

Development of innovative methodologies for assessing the effictiveness of topical and transdermal administration of drugs.

The impedance spectroscopy applied to biological tissue is proposed as quantitative method for assessing the dosage in case of transdermal administration.

In case of subcutaneous administration, the method assesses the rate of drug bioavailability to provide immediate evidence on the rapid systemic absorption of a drug (e.g., diabetic patient receiving ultra-fast insulin).

 

Activities

  • Biological tissue modelling (e.g. by finite element simulations) and validated through experimental campaign.
  • Classification of different kinds of drugs according their electrical behavior.
  • Creation of algorithms for assessing concentration and localization of a drug by elaborating the variation of the impedance spectrum in a biological tissue before and after administration.
  • Prototyping of portable and wearable devices able to quantify and localize a drug administered topically or transdermally delivered will be prototyped.

 

Challenges

  • Improving the accuracy and the repeatability of the method
  • Mechanical uncertainty sources management

 

Bibliography

  • Balato, M., Petrarca, C., Arpaia, P., Moccaldi, N., Mancino, F., Carleo, G., ... & Balato, G. (2022). Detecting and Monitoring Periprosthetic Joint Infection by Using Electrical Bioimpedance Spectroscopy: A Preliminary Case Study. Diagnostics12(7), 1680.

 

  • Arpaia, P., Cuneo, D., Grassini, S., Mancino, F., Minucci, S., Moccaldi, N., & Sannino, I. (2021). A finite element model of abdominal human tissue for improving the accuracy in insulin absorption assessment: A feasibility study. Measurement: Sensors18, 100218.

 

  • Annuzzi, G., Arpaia, P., Bozzetto, L., Carleo, G., Cuomo, O., Mancino, F., ... & Taglialatela, M. (2022, June). Measuring insulin absorption by impedance spectroscopy. A feasibility study. In 2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)(pp. 1-5).

 

 

  • Arpaia, P., Cuneo, D., Mancino, F., & Moccaldi, N. (2022, May). A Bioimpedance-based Transducer for Insulin Bioavailability Assessment after Subcutaneous Administration. In 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)(pp. 1-5).

 

  • Arpaia, P., Cuneo, D., Mancino, F., Minucci, S., Moccaldi, N., & Sannino, I. Preliminary Investigation of the Impact of Mechanical Stresses on Bioimpedance Spectroscopy-based Insuline Bioavailability Assessment. In 2021 International Workshop on Impedance Spectroscopy (IWIS)(pp. 52-55). IEEE.

 

  • Arpaia, P., Cesaro, U., Frosolone, M., Moccaldi, N., & Taglialatela, M. (2020). A micro-bioimpedance meter for monitoring insulin bioavailability in personalized diabetes therapy. Scientific Reports10(1), 1-11.

 

  • Annuzzi, G., Arpaia, P., Cesaro, U., Cuomo, O., Frosolone, M., Grassini, S., ... & Sannino, I. (2020, May). A customized bioimpedance meter for monitoring insulin bioavailability. In 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)(pp. 1-5).

 

  • Arpaia, P., Cuomo, O., Moccaldi, N., Smarra, A., & Taglialatela, M. (2018, August). Non-invasive real-time in-vivo monitoring of insulin absorption from subcutaneous tissues. In Journal of Physics: Conference Series(Vol. 1065, No. 13, p. 132008). IOP Publishing.

ARHeMLab

Augmented Reality for Health Monitoring Laboratory

80100 Naples (ITALY) - Via Claudio, 21 - DIETI block
Tel  (+39) 081 123456 -  Fax (+39) 081 654321
e-mail: info.arhemlab.dieti@unina.it

ARHeMLab
80100 Naples (ITALY) - Via Claudio, 21
Tel (+39) 081 123456
Fax (+39) 081 654321
e-mail: info.arhemlab.dieti@unina.it
Copyright © 2019 ARHeMLab: All rights reserved.

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