Antonio NoceraUniversità Politecnica delle Marche | Università degli Studi di Ancona · Department of Information Engineering (DII)
Antonio Nocera
Master of Biomedical Engineering
Analysis of biomedical signal extracted from contactless sensors, such as mmWave radar and RGB-D cameras
About
4
Publications
396
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
4
Citations
Citations since 2017
Introduction
I graduated in the bachelor of Biomedical Engineering in December 2020 with a thesis regarding machine learning for the estimation of muscle onset activation in the electromyography signal. Then, I graduated in the master's degree of Biomedical Engineering in July 2022 with a thesis titled "Bioengineering of sport: American Football". Since November 2022 he is a PhD fellow at the department of Information Engineering (DII) .
Publications
Publications (4)
We propose a model composed of a Convolutional Neural Network followed by a bidirectional Long-Short Term Memory for the classification of the range-Doppler-time data obtained from radar acquisition. The approach reaches at least a 90% f1 score for the abnormal walking patterns class observing just one gait cycle or one second of acquisition and it...
![Figure 1. Flow chart describing the selection process (modified from [38]).](publication/369602925/figure/fig1/AS:11431281131415177@1680112436493/Flow-chart-describing-the-selection-process-modified-from-38_Q320.jpg)
American football is the sport with the highest rates of concussion injuries. Biomedical engineering applications may support athletes in monitoring their injuries, evaluating the effectiveness of their equipment, and leading industrial research in this sport. This literature review aims to report on the applications of biomedical engineering resea...
Background:
Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-based approach (DEMANN) in detecting the onset/offset timing of muscle activation from sEMG signals.
M...
