Luigi Borzí

Luigi Borzí
Politecnico di Torino | polito · DAUIN - Department of Control and Computer Engineering

PhD Student
Wearable Sensors and Artificial Intelligence for Human Movement Analysis

About

15
Publications
8,638
Reads
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77
Citations
Introduction
Research interests: Movement Disorders, Postural Control, Fall Risk assessment, Sleep Disorders, Speech Analysis Methods: Wearable Sensors, Artificial Intelligence Current work: Wearable inertial and physiological sensors for Parkinson's Disease monitoring of voice, postural control, motor control.
Additional affiliations
October 2018 - May 2019
Politecnico di Torino
Position
  • Research Assistant
Education
October 2015 - July 2018
Politecnico di Torino
Field of study
  • Biomedical Engineering

Publications

Publications (15)
Conference Paper
Voice is a reservoir of valuable health data. Recent studies highlighted its efficacy in predicting sleep quality, and its potential as biomarker of neurodegeneration. This study assesses the feasibility of a Telemedicine system for the evaluation of sleep quality through brief vocal recordings. Machine Learning models were employed in the binary c...
Article
Full-text available
Background: Current telemedicine approaches lack standardised procedures for the remote assessment of axial impairment in Parkinson's disease (PD). Unobtrusive wearable sensors may be a feasible tool to provide clinicians with practical medical indices reflecting axial dysfunction in PD. This study aims to predict the postural instability/gait dif...
Article
Full-text available
The study of the influence of Parkinson’s Disease (PD) on vocal signals has received much attention over the last decades. Increasing interest has been devoted to articulation and acoustic characterization of different phonemes. Method: In this study we propose the analysis of the Transition Regions (TR) of specific phonetic groups to model the los...
Article
Full-text available
Introduction Automatic assessment of speech impairment is a cutting edge topic in Parkinson’s disease (PD). Language disorders are known to occur several years earlier than typical motor symptoms, thus speech analysis may contribute to the early diagnosis of the disease. Moreover, the remote monitoring of dysphonia could allow achieving an effectiv...
Article
Full-text available
Rapid‐eye movement (REM) sleep, or paradoxical sleep, accounts for 20–25% of total night‐time sleep in healthy adults and may be related, in pathological cases, to parasomnias. A large percentage of Parkinson's disease patients suffer from sleep disorders, including REM sleep behaviour disorder and hypokinesia; monitoring their sleep cycle and rela...
Article
Full-text available
Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson's disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the admi...
Article
Full-text available
Parkinson's disease is a neuro-degenerative disorder characterized by the progressive death of dopamine neurons. This leads to delayed and uncoordinated movements, and impacts on the patients' motor performance with reduced movement intensity, increased axial rigidity and impaired cadence regulation. Turning provides privileged insights in postural...
Article
Full-text available
Background: Postural instability is one of the most troublesome motor symptoms of Parkinson's Disease (PD). It impairs patients' quality of life and results in high risk of falls. The aim of this study is to provide a reliable tool for the automated assessment of postural instability. Methods: Data acquisition was performed on 42 PD patients and 7...
Article
Full-text available
Goal: We investigated the use of smartphone sensors and Artificial Intelligence techniques for automatic quantification of the MDS-UPDRS-Part III Leg Agility task, representative of lower limb bradykinesia in Parkinson's Disease (PD) patients. Method: We collected inertial data from 93 PD subjects, and four expert neurologists provided concurrent c...
Preprint
Full-text available
Freezing of Gait (FOG) is one of the most troublesome motor symptoms associated with Parkinson's disease (PD), characterised by brief episodes of inability to step. It involves increased risk of falls and reduced quality of life, and correlates with motor fluctuations and progression of the disease. Hence, the knowledge of FOG event frequency, dura...
Conference Paper
Full-text available
We propose a deep-learning method for feature extraction from gait data of Parkinson's disease patients. Our goal is to verify whether a fine classification of gait between similar groups can be achieved. To this end, we refer as a case study to the Freezing of Gait (FOG), and we measure gait data from two groups of patients, which exhibit (respect...
Article
Full-text available
In Parkinson’s disease, motor fluctuations (worsening of tremor, bradykinesia, freezing of gait, postural instability) affect up to 70% of patients within 9 years of \(\textsc {l}\)-dopa therapy. Nevertheless, the assessment of motor fluctuations is difficult in a medical office, and is commonly based on poorly reliable self-reports. Hence, the use...

Questions

Questions (3)
Question
"Wearable Sensors for Supporting Diagnosis, Prognosis, and Monitoring of Neurodegenerative Diseases" in MDPI Electronics (IF 2.2397, CiteScore Scopus 2.7).
The aim is to collect high-quality studies related to recent developments and applications in the field of wearable sensors for neurodegenerative diseases.
Please feel free to contact us if you are interested in contributing as well. Submissions will be welcome at any point until 31 December 2022.
Question
Can you help me find a nice solution to plot different CNN architectures automatically?
At the moment, I have a 3 head 1D-CNN, with 2 convolutional layers, 2 max-pooling layers, and 2 fully connected layers.
I used 3 heads to have different resolutions (kernel size) on the same signals.
Question
Can you please provide a rationale for choosing a particular feature selection algorithm over the others?
Which are the pros and cons of each algorithm?

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Projects

Projects (2)
Project
Atomated clinical assessment of Parkinson's Disease motor symptoms, using Wearable Sensors and Artificial Intelligence techniques
Project
Build a FOG detection algorithm using inertial data acquired from a waist-worn smartphone