Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
About

About

Miguel Velhote Correia graduated in Electrical and Computer Engineering from University of Porto, Faculty of Engineering (FEUP) in 1990. He obtained the Master and the Doctoral degrees also from FEUP in 1995 and 2001, in the fields of Industrial Automation and Computer Vision, respectively. Currently, he is an Assistant Professor at the Department of Electrical and Computer Engineering at FEUP, since 2002 and with tenure since 2007. Since March 2008, he is also a senior research member at INESC Technology and Science – Institute of Systems and Computer Engineering of Porto, head of the Bioinstrumentation Laboratory of the Centre for Biomedicla Engineering Research. Additionally, he is co-founder and technical advisor of Kinematix Sense S.A. (formerly Tomorrow Options - Microelectronics S.A), an electronic devices start-up company of University of Porto and INESCTEC. Between 1993 and 2007, he was a researcher at INEB – Institute of Biomedical Engineering, in the Biomedical Imaging and Vision Computing group and previously at the CIM Centre of Porto at FEUP. His main research interests are in Sensors and Electronics, Biomedical Instrumentation, Computational Vision and Image and Signal Processing, with focus in sensing methods, technologies and data fusion for the measurement and analysis of human movement, perception, action and performance. Since 1990, he participated in more than twenty funded research projects and co-authored over 100 research papers published in peer reviewed journals and conference proceedings. He is also member of the Portuguese Official Engineers Association, the International Association of Pattern Recognition, through its Portuguese chapter, and co-founder of the Portuguese Experimental Psychology Association.

Interest
Topics
Details

Details

  • Name

    Miguel Velhote Correia
  • Role

    Senior Researcher
  • Since

    01st March 2008
  • Nationality

    Portugal
  • Contacts

    +351222094106
    miguel.velhote.correia@inesctec.pt
006
Publications

2022

Muscle Synergies Estimation with PCA from Lower Limb sEMG at Different Stretch-Shortening Cycle

Authors
Rodrigues, C; Correia, M; Abrantes, J; Rodrigues, MAB; Nadal, J;

Publication
XXVII BRAZILIAN CONGRESS ON BIOMEDICAL ENGINEERING, CBEB 2020

Abstract

2022

Lower Limb Frequency Response Function on Standard Maximum Vertical Jump

Authors
Rodrigues, C; Correia, M; Abrantes, J; Rodrigues, MAB; Nadal, J;

Publication
XXVII BRAZILIAN CONGRESS ON BIOMEDICAL ENGINEERING, CBEB 2020

Abstract

2022

Subject Specific Lower Limb Joint Mechanical Assessment for Indicative Range Operation of Active Aid Device on Abnormal Gait

Authors
Rodrigues, C; Correia, M; Abrantes, J; Rodrigues, MAB; Nadal, J;

Publication
XXVII BRAZILIAN CONGRESS ON BIOMEDICAL ENGINEERING, CBEB 2020

Abstract

2022

Identity Recognition in Sanitary Facilities Using Invisible Electrocardiography

Authors
Silva, AS; Correia, MV; de Melo, F; da Silva, HP;

Publication
SENSORS

Abstract
This article proposes a new method of identity recognition in sanitary facilities based on electrocardiography (ECG) signals. Our team previously proposed a novel approach of invisible ECG at the thighs using polymeric electrodes, leading to the creation of a proof-of-concept system integrated into a toilet seat. In this work, a biometrics pipeline was devised, which tested four different classifiers, varying the population from 2 to 17 subjects and simulating a residential environment. However, for this approach to be industrially viable, further optimization is required, particularly regarding electrode materials that are compatible with industrial processes. As such, we also explore the use of a conductive silicone material as electrodes, aiming at the industrial-scale production of a toilet seat capable of recording ECG data, without the need for body-worn devices. A desirable aspect when using such a system is matching the recorded data with the monitored user, ideally using a minimal sensor set, further reinforcing the relevance of user identification through ECG signals collected at the thighs. Our approach was evaluated against a reference device for a population of 17 healthy and pathological individuals, covering a wide age range (24-70 years). With the silicone composite, we were able to acquire signals in 100% of the sessions, with a mean heart rate deviation between a reference system and our experimental device of 2.82 +/- 1.99 beats per minute (BPM). In terms of ECG waveform morphology, the best cases showed a Pearson correlation coefficient of 0.91 +/- 0.06. For biometric detection, the best classifier was the Binary Convolutional Neural Network (BCNN), with an accuracy of 100% for a population of up to four individuals.

2022

A systematic review with meta-analysis of the diagnostic test accuracy of pedicle screw electrical stimulation

Authors
Fonseca, P; Goethel, M; Vilas-Boas, JP; Gutierres, M; Correia, MV;

Publication
EUROPEAN SPINE JOURNAL

Abstract
Purpose To provide a systematic review with meta-analysis providing evidence of the current diagnostic test accuracy (DTA) of pedicle screw electrical stimulation. Methods A systematic database search on PubMed, Scopus and Web of Science was performed according to the PRISMA-DTA guidelines, and eligibility criteria applied to reduce the results to: (1) only journal articles reporting electrical stimulation of the pedicle screw head, (2) screw position confirmation by imaging techniques, and (3) enough information allowing the calculation of a 2 x 2 contingency table. Sample characteristics, image confirmation method, electrical current threshold and stimulation results were retrieved and analyzed using according to appropriate DTA analysis methods, and allowing the calculation of specificity, sensitivity for pedicle screws insertion at the lumbar and thoracic levels. Results Lumbar screw stimulation presents a higher sensitivity (0.586 [0.336, 0.798] and specificity (0.984 [0.958, 0.994]) than thoracic screws (sensitivity: 0.270 [0.096; 0.562]; specificity: 0.958 [0.931, 0.975]). The same is observed in terms of the diagnostic odds ratio for lumbar (88.32 [32.136, 242.962]) and thoracic (8.460 [2.139, 33.469]) levels. When performing a sub-group analysis, it is possible to divide the lumbar stimulation threshold as 8 and 10-12 mA, and the thoracic threshold as 6 and 9-12 mA. A threshold of 8 mA at the lumbar level provides higher sensitivity and specificity. Increasing the threshold results in higher specificity but not sensitivity. In fact, at the range of 10-12 mA, the diagnostic validity is too low to confer this technique any robust diagnostic validity. Similarly, at the thoracic level, lower threshold currents are associated with increased sensitivity, but their diagnostic validity is very low. Conclusion Electrical stimulation of the pedicle screw can be used as an adequate diagnostic capability at the lumbar level with a threshold of 8 mA. However, thoracic stimulation is currently not reliable, with very low sensitivity and diagnostic validity at 6 mA or higher.

Supervised
thesis

2021

Development of a neurophysiologic intraoperative monitoring system for spine surgical procedure

Author
Pedro Filipe Pereira da Fonseca

Institution
UP-FEUP

2021

Development of a monitoring and data communication system for application in Pavement Energy Harvesting

Author
Jose Gonçalo Correia Sousa Neto

Institution
UP-FEUP

2021

A dense and high throughput WLAN system using emerging Light Communications technology

Author
André da Silva Reis

Institution
UP-FEUP

2021

In vivo measurement of forces exerted on the trapeziometacarpal joint

Author
Inês Pereira de Fontes

Institution
UP-FEUP

2021

Processing and integrating handheld 3D optical sensor scanning data in an industrial environment

Author
Ricardo Miao Wang

Institution
UP-FEUP