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Giorgio Biagetti
Researcher at Marche Polytechnic University
Publications - 96
Citations - 969
Giorgio Biagetti is an academic researcher from Marche Polytechnic University. The author has contributed to research in topics: Computer science & Speaker recognition. The author has an hindex of 17, co-authored 84 publications receiving 738 citations.
Papers
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Journal ArticleDOI
Multicomponent AM–FM Representations: An Asymptotically Exact Approach
TL;DR: A multicomponent sinusoidal model that allows an asymptotically exact reconstruction of nonstationary speech signals, regardless of their duration and without any limitation in the modeling of voiced, unvoiced, and transitional segments is presented.
Journal ArticleDOI
Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes
TL;DR: A low-cost wearable wireless system specifically designed to acquire surface electromyography (sEMG) and accelerometer signals for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications is presented.
Journal ArticleDOI
Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM–FM Decomposition
TL;DR: A novel approach for simultaneously obtaining exercise repetition frequency and evaluating muscular fatigue, as functions of time, by only using the EMG signal is presented.
Journal ArticleDOI
Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data
TL;DR: The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity.
Book ChapterDOI
Human Activity Recognition Using Accelerometer and Photoplethysmographic Signals
TL;DR: This paper presents an efficient technique for real-time recognition of human activities by using accelerometer and photoplethysmography data based on singular value decomposition and truncated Karhunen-Loeve transform for feature extraction and reduction, and Bayesian classification for class recognition.