Available Master Thesis

AVAILABLE MASTER THESIS

Robotics and Neuroprosthetics for Rehabilitation

ThesisOptimization of an outdoor cycling FES-system for paraplegics
SupervisorProf. Emilia Ambrosini (emilia.ambrosini@polimi.it)
Co-SupervisorsProf. Simona Ferrante (simona.ferrante@polimi.it)
CollaborationsMarco Tarabini, Dipartimento di meccanica (https://www.mecc.polimi.it)
 
Description

Aim:

Cycling induced by Functional Electrical Stimulation (FES) is a well-established rehabilitation technique for people with Spinal Cord Injury (SCI) and stroke patients. When proposed on a mobile system (trike, see figure), it becomes a recreational activity for individuals with SCI, favoring social inclusion and quality of life. A team from POLIMI has just participated to Cybathlon 2020.

This work aims to improve our prototype and our training strategy in order to get ready for the next Edition. We would like to exploit spatial distributed stimulation to maximize power output and minimize fatigue, to integrate cardiovascular measurements and real-time acquisition of pedal forces.

Project phases:

  • Literature review
  • Prototype optimation (HW and SW)
  • Pilots training and data collection

Requirements:

  • Knowledge of Matlab
  • Interest in HW and SW development
ThesisDesign of a hybrid robotic system to support locomotion in Spinal Cord Injury people
SupervisorProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-SupervisorsProf. Emilia Ambrosini (emilia.ambrosini@polimi.it)
CollaborationsFrancesco Braghin, Marta Gandolla (Dipartimento di Meccanica, Politecnico di Milano)
 
Description

Aim:

In the last ten years, hybrid robotic systems, combining Functional Electrical Stimulation (FES) and exoskeletons, have been proposed to support locomotion in Spinal Cord Injury (SCI) people. This combination helps to overcome the limitations of each single approach, but current solutions do not implement a cooperative control strategy of the two systems.

Within the recently started project FESleg, funded by INAIL, this work aims to design a a cooperative control strategy of an exoskeleton for walking (i.e. TWIN developed by IIT), which combines the torque produced by the motors with the residual effort of the subject as well as with the contribution provided by FES.

Project phases:

  • Literature review
  • Design of a biomimetic stimulation strategy for locomotion
  • Integration of the FES control within the the exoskeleton control loop

Requirements:

  • Knowledge of Matlab
  • Experience in programming (C++)
ThesisModel-based control of a hybrid robotic system for locomotion
SupervisorProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-SupervisorsProf. Emilia Ambrosini (emilia.ambrosini@polimi.it)
CollaborationsProf. Francesco Braghin, Prof. Marta Gandolla (Dipartimento di Meccanica, Politecnico di Milano)
 
Description

Aim:

In the last ten years, hybrid robotic systems, combining Functional Electrical Stimulation (FES) and exoskeletons, have been proposed to support locomotion in Spinal Cord Injury (SCI) people. This combination helps to overcome the limitations of each single approach, but current solutions do not implement a cooperative control strategy of the two systems.

Within the recently started project FESleg, funded by INAIL, this work aims to design a simulation environment for testing cooperative control strategies for locomotion. The model will include both the muscle response to FES and the exoskeleton.

Project phases:

  • Literature review
  • Simulation environment development using a biomechanical modelling software (e.g. Opensim, AnyBody)
  • Design of cooperative control strategies

Requirements:

  • Knowledge of Matlab
  • Experience in programming
ThesisEMG-based vibro-tactile biofeedback training in children with dystonia
SupervisorProf. Emilia Ambrosini (emilia.ambrosini@polimi.it)
Co-SupervisorsProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Collaborations Terry Sanger, University of California, Irvine
 
Description

Aim:

Childhood dystonia is a movement disorder characterized by involuntary sustained or intermittent muscle contractions. In case of sensory deficits, children with dystonia may not be aware of their altered patterns of muscle activity and, consequently, they are not able to compensate for unwanted activity. Biofeedback techniques, which provide the subject with augmented task-relevant information, might help improve motor control and accelerate motor learning. A multi-center cross-over study to evaluate the effects of a wearable EMG-based device on motor learning in children with dystonia was carried out.

This work will be focused on the analysis of the data collected on 26 patients and 28 age-matched control subjects. This work is suitable to be a “TESINA”.

Project phases:

  • Literature review on motor control in children with dystonia
  • Data analysis

Requirements:

  • Knowledge of Matlab
Thesis Bio-inspired design of an upper limb cable-driven soft exoskeleton in simulation environment
SupervisorProf Emilia Ambrosini (emilia.ambrosini@polimi.it)
Prof. Marta Gandolla (marta.gandolla@polimi.it)
Co-Supervisors Elena Bardi (elena.bardi@polimi.it)
CollaborationsDipartimento di meccanica (https://www.mecc.polimi.it)
 
Description

Aim:

 

In the context of rehabilitation and assistance, soft exoskeletons represent a promising technology. Being made of fabric, they are intrinsically compliant, lightweight and their cost is reduced with respect to traditional stiff exoskeletons. Thanks to these unique characteristics, soft exoskeletons can augment the user’s capabilities without constraining the physiological movements and are good candidates to be used in daily life activities.

 

The aim of this project is to develop an appropriate simulation environment to optimize, in a bio-inspired fashion, the mechanical design of a cable-driven soft exoskeleton. The optimal number of cables and the positioning of the anchor points, as well as the design of the base structure will be investigated in order to minimize shear and contact forces with the aim to maximize the comfort and the safety of the user.

Project phases:

 

· Literature review on upper limb soft exoskeletons design

·   Simulation environment development using a biomechanical modelling software (e.g. Opensim, AnyBody)

·       Exoskeleton design optimization

Requirements:

  • Basic knowledge in Solidworks
  • Experience in programming
  • Matlab
Thesis Test bench development of an upper limb cable-driven soft exoskeleton 
SupervisorProf Emilia Ambrosini (emilia.ambrosini@polimi.it)
Prof. Marta Gandolla (marta.gandolla@polimi.it)
Co-Supervisors Elena Bardi (elena.bardi@polimi.it)
CollaborationsDipartimento di meccanica (https://www.mecc.polimi.it)
  
Description

Aim:

 

In the context of rehabilitation and assistance, soft exoskeletons represent a promising technology. Being made of fabric, they are intrinsically compliant, lightweight, and their cost is reduced with respect to traditional stiff exoskeletons. Thanks to these unique characteristics, soft exoskeletons can augment the user’s capabilities without constraining the physiological movements and are good candidates to be used in daily life activities.

 

The aim of this project is to develop a one-degree-of-freedom test bench for the preliminary testing of a soft exoskeleton control strategy. The exoskeleton will be actuated by an electric motor with a cable-driven mechanism, which does not constrain other degrees of freedom. The system performance, in terms of accuracy and bandwidth, will be evaluated.

 

Project phases:

 

  • Literature review on upper limb soft exoskeletons and cable-driven actuators
  • Test bench development
  • Control strategy development and testing

Requirements:

  • Experience in programming
ThesisEffects of TMS-induced inter-hemispheric inhibition on the recovery of reaching tasks during robotic rehabilitation
SupervisorProf Emilia Ambrosini (emilia.ambrosini@polimi.it)
Co-Supervisors 
CollaborationsDr. L. Sebastianelli, Neuroriabilitazione Ospedale di Vipiteno
 
Description

Aim:

In post-acute stroke patients, repetitive Transcranial Magnetic Stimulation (rTMS) of the  contralesional motor cortex has shown functional improvements of the ipsilesional motor cortex. Most of the studies have investigated cortical areas related to distal muscles (hand) on slightly compromised patients, while the effects on cortical areas which control proximal muscles of the upper limb are still unknown.

This project aims at evaluating whether inhibitory rTMS on the contralesional cortical area of the triceps improves the performance in reaching tasks of stroke survivors. A group of 10 patients were involved in a cross-over pilot study to compare the effects of robotic training (ARMEO, HOCOMA) performed alone or in combination with rTMS. The training consisted of 25 sessions and assessment tests on ARMEO were performed before and after each session.

Project phases:

  • Literature review
  • Data analysis
  • Statistical analysis

Requirements:

  • Basic knowledge of Matlab
ThesisInteractive mirroring games with the social robot NAO for autism therapy
SupervisorProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-SupervisorsAlice Geminiani (alice.geminiani@polimi.it)
Laura Santos (laura.santos@mail.polimi.it
)
CollaborationsUniversità dell’Insubria/Fondazione Macchi (prof Cristiano Termine)
Provveditorato di Varese (Luigi Macchi, Simonetta Bralia)
 
Description

Aim:

Autism is a complex neurodevelopmental disorder, whose causes and effective treatments are still unknown. Autistic children show social, emotional and also motor deficits. Social robots have been suggested as potentially powerful tools to enhance traditional therapy, increasing motivation and engagement, while improving interaction and motor skills. To this aim, the current project exploits an interactive mirroring setup based on the social robot NAO, to develop new robot-mediated therapies for autism.

Project phases:

  • Literature study about the use of social robots in autism therapy.
  • Improvement of the setup, design of a protocol for the interactive game, design of a clinical trial in collaboration with clinicians.
  • Clinical acquisitions with patients.
  • Data analysis to validate the setup and evaluate the impact of the interactive mirroring game in autism therapy.

Requirements:

  • Basic knowledge on robotics and motion tracking systems.
  • Good programming skills in Python and MATLAB are a plus
  • Motivation to take part in clinical acquisitions and interact with therapists – italian language mother tongue/proficiency
ThesisEffects of a robot-assisted therapy on motor coordination after stroke
SupervisorsEmilia Ambrosini (emilia.ambrosini@polimi.it)
Simona Ferrante (simona.ferrante@polimi.it)
CollaborationsIng. Monica Parati, Laboratorio di Bioingegneria,  IRCCS Istituti Clinici Scientifici Maugeri, Lissone (MB)
 
Description

Aim:

Muscle recruitment process involved in the planning and execution of complex movements seem to be simplified in low-dimensional modules, termed muscle synergies.
Multi-muscle activity recorded through surface EMG and quantitatively analyzed in terms of muscle synergies represents a promising tool to examine motor impairments in post-stroke patients.
Few studies have already explored the changes of muscle synergies after a rehabilitative intervention.
This study aims at investigating the effects of a upper-limb robot-assisted therapy on motor coordination in post-acute stroke survivors.

Project phases:

A randomized controlled study investigating the effectiveness of a robot-assisted therapy on stroke patients is currently ongoing. Nest steps:

  • Literature research on muscle synergies and upper limb robot-assisted therapy in stroke survivors
  • Data collection
  • Analysis of trajectories, force and EMG signals pre- and post- robot-assisted therapy
  • Statistical analysis
  • Interpretation and discussions of the results

Requirements:

  • Knowledge of Matlab
  • Availability to participate to data collection
ThesisDoes a hybrid robotic system improve motor recovery in stroke survivors?
SupervisorProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-SupervisorProf. Emilia Ambrosini (emilia.ambrosini@polimi.it)
CollaborationsRETRAINER consortium
 
Description

Aim:

A hybrid robotic system for arm recovery, combining EMG-triggered Functional Electrical Stimulation (FES) with a passive exoskeleton for upper limb suspension, has been developed within the European project RETRAINER. A multi-center clinical study was conducted on 68 stroke survivors and showed the superiority of the RETRAINER system with respect to usual care in improving arm functionalities based on clinical scales. Data collected during training by the system itself might support the definition of the intervention and provide a deeper and more quantitative analysis of motor recovery. 

This study aims at analyzing EMG and kinematics data collected on a daily basis by the RETRAINER system in a group of 35 stroke survivors to drive further conclusion on motor recovery and help defining training intensity and duration.

Project phases:

Starting from EMG and kinematics data collected on 35 stroke survivors longitudinally during the intervention, and pre and post clinical scales:

  • Literature research
  • Data analysis in Matlab
  • Statistical analysis
  • Interpretation and discusion of the results

Requirements:

  • Knowledge of Matlab
  • Software for statistical analysis (R or SPSS)

Digital Health

ThesisMachine Learning for premature babies parenteral nutrition
SupervisorProf Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsLinda Greta Dui (lindagreta.dui@polimi.it)
CollaborationsMarco Frontini (Link Up s.r.l.)
Valentina Bozzetti (Ospedale San Gerardo, Monza)
  
Description

Aim:

Premature babies need a special nutrition program to overcome their frailty condition. It must balance different nutrients apport, to achieve a target growth. However, the real effect of different nutrition programs is still unclear. The aim of this work is the creation of a machine learning model on babies’ response to nutritional programs and the impact on their global health.

Project phases:

  • Literature review
  • Machine Learning models implementation

Requirements:​

  • Interest in machine learning
ThesisMonitoring of patients with Parkinson’s disease during walking in free-living and challenging conditions
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsMilad Malavolti (milad.malavolti@polimi.it)
Monica Parati (monica.parati@polimi.it)
CollaborationsIRCCS Istituti Clinici Scientifici Maugeri, Milano
  
Description

Aim:

Gait impairments, including bradykinesia and freezing of gait (FoG), are the most common and disabling symptoms in Parkinson’s disease patients. Quantifying gait impairments under free-living and challenging condition (e.g. FoG-provoking test) using sensing technologies is a promising avenue to assess and monitor disease severity. The aim of this work is to use sensing technologies to quantify gait impairment in Parkinson’s disease, with the final aim of defining tailored interventions.

Project phases:

  • Literature review​
  • Set-up and protocol refinement
  • Pilot testing on patients with Parkinson’s disease
  • Data analysis

Requirements:​

  • Interest in working with patients
  • Basic knowledge of Matlab and programming languages
ThesisSerious games for specific learning disabilities detection
SupervisorProf Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsLinda Greta Dui (lindagreta.dui@polimi.it)
CollaborationsProf Termine, (Università dell’Insubria/Fondazione Macchi)
 
Description

Aim:

Specific Learning Disabilities (SLDs) first screening starts from direct teachers’ observation. If it is not possible, e.g., in distance learning during COVID-19 lockdown, they exacerbate and prevent children from proper learning. The aim of this work is to provide serious games which children would like to play at home and could provide hints of difficulties in reading, writing or calculation.

Project phases:

  • Literature review
  • Exergame design and implementation
  • Preliminary tests on target users

Requirements:

  • Interest in game development
  • Availability to learn programming languages
ThesisIoT smart ink pen for early detection and monitoring of patients with mild cognitive impairments and dementia
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsFrancesca Lunardini (francesca.lunardini@polimi.it)
CollaborationsCarlo Abbate IRCCS Fondazione Don Carlo Gnocchi ONLUS
 
Description

Aim:

Dementia affects millions of people worldwide. Unfortunately, it cannot be cured, but an early diagnosis can help to better manage the disease evolution. Handwriting results from a complex network made up of cognitive, kinesthetic, and perceptual-motor abilities and it is one of the daily’s activities affected in patients with dementia. 

The aim of this work is to use an IoT smart ink pen for the characterization of patients with dementia and mild cognitive impairment with the final goal of supporting early diagnosis. 

Project phases:

  • Literature review
  • Data collection
  • Data analysis and algorithm development

Requirements:

  • Basic knowledge of Matlab
  • Basic knowledge of Python
ThesisIoT smart ink pen for early detection and monitoring of patients with Parkinson’s disease
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsFrancesca Lunardini (francesca.lunardini@polimi.it)
Monica Parati (monica.parati@polimi.it)
CollaborationsIRCCS Istituti Clinici Scientifici Maugeri, Milano
 
Description

Aim:

The diagnosis of clinically probable Parkinson’s Disease (PD) in the early stages relies primarily on clinical assessment by a neurologist. When motor symptoms affect the dominant hand, patients may report worsening of

handwriting as of the initial symptoms. The aim of this work is to use an IoT smart ink pen for the characterization of PD patients handwriting, with the final goal of supporting PD patients’ diagnosis and remote monitoring

Project phases:

  • Literature review​
  • Data collection
  • Data analysis and algorithm development

Requirements:​

  • Basic knowledge of Matlab
  • Basic knowledge of Python
ThesisIoT smart ink pen for longitudinal monitoring of daily-life handwriting
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Prof. Matteo Matteucci (matteo.matteucci@polimi.it)
Co-SupervisorsFrancesca Lunardini (francesca.lunardini@polimi.it)
Davide Di Febbo (davide.difebbo@polimi.it)
  
  
Description

Aim:

Handwriting is a high-value task entailing a unique blend of cognitive, perceptual, and fine motor skills and for this reason its assessment is leveraged in a number of health-related applications, including the diagnostic process. The aim of this work is to profile normal handwriting and potential deviations through data longitudinally collected by an IoT smart ink pen and anomaly detection techniques.

Project phases:

  • Literature review
  • Data collection
  • Data analysis ald algorithm development

Requirements:

  • Basic knowledge of Matlab or Python
ThesisCompanion interface for an IoT smart ink pen
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsMilad Malavolti (milad.malavolti@polimi.it)
Collaborations 
 
Description

Aim:

Handwriting is a high-value task entailing a unique blend of cognitive, perceptual, and fine motor skills and for this reason its assessment is leveraged in a number of health-related applications,  One of the aim of the European ESSENCE project is to leverage a novel IoT smart in pen to monitor user’s handwriting. 

The aim of this thesis work is to develop a companion interface to manage – via Bluetooth – communication, data recording, data download and important  functionalities related to the pen use.

Project phases:

  • Research for identification of solutions​
  • Companion Interface design of automated algorithms
  • Companion interfacedevelopment
  • Companion Interface testing

Requirements:

  • KnowledgeAndroid programming or otherlanguages (Python, Javascript…)
  • Interest in programming and problem solving
ThesisThe DYSPA System: a novel neuro-motor assessment to quantify dystonia and spasticity in children with movement disorders
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorsFrancesca Lunardini (francesca.lunardini@polimi.it)
CollaborationsDott. Giovanna Zorzi, Dott. Davide Rossi, Fondazione IRRCS Istituto Neurologico Carlo Besta
 
Description

Aim:

Selecting and evaluating appropriate treatment for children with hypertonic movement disorders is nontrivial. One challenge is the ability of quantifying the presence and importance of motor impairments, especially when more than one coexist. This is the case, for instance, of mixed hypertonia with components of spasticity and dystonia. Against this background, the DYSPA System aims at achieving quantitative assessment, encompassing kinematic and electromyographic measures, that quantifies neuro-motor performance during functional tasks and measures the presence and extent of motor impairments through specific dystonia and spasticity indices.

Project phases:

  • Literature review
  • System design and development (choice, integration, and synchronization of different devices)​
  • Preliminary testing on children (healthy and patients) ​
  • Data analysis for system validation​

Requirements:

  • Interest in learning programming languages​
ThesisMobile app for monitoring emotion valence and cognitive decline from voice signals
SupervisorProf. Emilia Ambrosini (emilia.ambrosini@polimi.it)
Co-SupervisorsProf. Simona Ferrante (simona.ferrante@polimi.it)
CollaborationsSignalGeneriX (Limassol, Cyprus)
 
Description

Aim:

Speech conveys important information about health condition and quality of life. Voice parameters can inform about cognitive decline, stress, emotions and arousal. Therefore, automatic voice analysis is a good candidate to be included in mobile-health technologies, offering an ecological and continuous monitoring. Within a previous European project (MoveCare), a mobile app was developed to identify cognitive decline based on voice parameters computed on the fly during phone conversations. This work, included in the European project ESSENCE, aims at extending this app including features correlated also to emotional valence and arousal.

Project phases:

  • Literature review
  • Implementation of an algorithm to automatically derive voice features
  • Machine Learning models implementation
  • Design of a mobile app

Requirements:

  • Knowledge of Matlab
  • Interest in machine learning and app development
ThesisIoT smart ink pen for handwriting longitudinal monitoring in children
SupervisorProf Simona Ferrante (simona.ferrante@polimi.it)
Prof Matteo Matteucci (matteo.matteucci@polimi.it)
Co-SupervisorsLinda Greta Dui (lindagreta.dui@polimi.it)
CollaborationsUniversità dell’Insubria/Fondazione Macchi (prof Cristiano Termine)
Provveditorato di Varese (Luigi Macchi, Simonetta Bralia)
 
Description

Aim:

In early school years, poor handwriting development has negative consequences on children self esteem and behaviour. Monitoring subtle changes is important, but they cannot be disclose by pure observation only.
The aim of this work is to profile the development of children’s normal handwriting and potential deviations through data longitudinally collected by an IoT smart ink pen and anomaly detection techniques.

Project phases:

  • Literature review
  • Data collection
  • Data analysis

Requirements:

  • Basic knowledge of Matlab or Python
ThesisComputer Vision and IoT to detect grasping difficulties
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Prof. Matteo Matteucci (matteo.matteucci@polimi.it)
Co-SupervisorsLinda Greta Dui (lindagreta.dui@polimi.it)
CollaborationsUniversità dell’Insubria/Fondazione Macchi (prof Cristiano Termine)
Provveditorato di Varese (Luigi Macchi, Simonetta Bralia)
 
Description

Aim:

An inefficient grasping position causes difficulties in handwriting and an early correction can avoid persistent problems. In distance learning, teachers’ or experts’ direct observation was not possible.
The aim of this work is to leverage computer vision to detect the grasping strategy of children, through video recordings of handwriting production, and to relate it to pen movements collected by an IoT smart ink pen, with the final goal of understanding grasping problems from IoT sensors only.

Project phases:

  • Literature review
  • Data collection
  • Computer vision algorithms implementation
  • IoT sensors’ data analysis

Requirements:

  • Basic knowledge of computer vision and deep learning
  • Availability to reach schools for data collection (province of Varese)
ThesisVoice analysis to diagnose neurodegenerative diseases
SupervisorProf. Emilia Ambrosini (emilia.ambrosini@polimi.it)
Co-SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
CollaborationsDott. Andrea Arighi, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico
 
Description

Aim:

Voice signals provide important information to measure human behaviors and cognitive functions. Various types of neurodegenerative dementia (Alzheimer’s disease, frontotemporal dementia, primary progressive aphasia, Lewy body dementia) affect human speech in different manners. Disorders or irregularities in language domain, evaluated in terms of temporal features, such as pauses, speech rate, pitch, etc, could be strong predictors of different neurodegenerative pathologies affecting the brain. This thesis aims at the development and validation of a software tool able to automatically extract a set of language features sensitive and to classify neurodegenerative disorders from voice signals.

Project phases:

Starting from a Matlab-algorithm to automatically extract voice features from recordings and a dataset of recordings on healthy elderly subjects:

– Literature review on vocal indicators of cognitive decline
– Creation of a database of normal and pathological voice samples from elderly (control subjects, patients with Alzheimer’s disease, frontotemporal dementia, primary progressive aphasia and Lewy body dementia)
– Optimization of the algorithm to extract voice features
– Development of a classification algorithm to support the diagnosis of neurodegenerative diseases based on voice features and neurodegenerative biomarkers (magnetic resonance atrophy, cerebrospinal fluid proteins concentration)

Requirements:

  • Knowledge of Matlab
  • Knowledge (or intention to know) Python
  • Availability to participate to data collection
ThesisSerious games for hand function rehabilitation
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorFrancesca Lunardini (francesca.lunardini@polimi.it
CollaborationsAISLab – Università degli Studi di Milano
Clinical partners
 
Description

Aim:

Deficits in the upper extremity, especially in the hand usually affects motor control ability, leading to difficulties in performing the daily life activities.

This work aims at developing a digital solution for hand rehabilitation using serious games controlled through a sensorized smart ball to aid patients and older adults in rehabilitating their hand.

Project phases:

– Literature review
– Design of serious games
– Tests on healthy subjects and patients
– Data analysis

Requirements:

  • Interest in game development
  • Availability to learn programming languages
ThesisMonitoring of patients with vegetative and minimally conscious state diagnosis
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorFrancesca Lunardini (francesca.lunardini@polimi.it)
Milad Malavolti (milad.malavolti@polimi.it)
CollaborationsFondazione Istituto Neurologico C. Besta – Milano
Fondazione IRCCS San Raffaele
Centro di Riabilitazione Villa Beretta – Ospedale Valduce
Empatica
 
Description

Aim:

An ICT system, encompassing inertial, temperature, electrodermal activity, and EMG sensors has been developed to be used in combination with cognitive assessment to achieve longitudinal monitoring of patients with Disorders of Consciousness (DOC), with the final aim of testing the efficacy of tailored interventions.

Project phases:

– Literature review
-Data acquisition on patients
-Data analysis and design of algorithms to estimate pain and detect voluntary/unvoluntary contractions during long term acquisitions

ThesisEye tracking for action observation treatment in neurological patients
SupervisorProf. Simona Ferrante (simona.ferrante@polimi.it)
Co-SupervisorFrancesca Lunardini (francesca.lunardini@polimi.it)
CollaborationsDr. Davide Sebastiano RossiFondazione Istituto Neurologico C. Besta, Milano
Prof. Giovanni BuccinoUniversità Vita-Salute San Raffaele and Division of Neuroscience
 
Description

Aim:

Studies show that the use of systematic observation of meaningful actions followed by their execution (action observation treatment [AOT]) may become a rehabilitative strategy to accelerate the process of functional recovery in patients with motor impairment.

This work aims at developing a home-based system for neurological patients that leverages eye tracking technology during the observation of video clips showing appropriate actions, with the aim of evaluating compliance to the required task. 

Project phases:

– Literature review
-Design of technological solution with definition of requirements and specifications
Integration of eye tracking with tablet/laptop
Data acquisition and analysis

ThesisMachine Learning for digital Dysgraphia diagnosis
SupervisorSimona Ferrante (simona.ferrante@polimi.it)
Matteo Matteucci (matteo.matteucci@polimi.it)
Co-SupervisorLinda Greta Dui (lindagreta.dui@polimi.it)
Francesca Lunardini (francesca.lunardini@polimi.it)
CollaborationsProf. Cristiano TermineUniversità degli Studi dell’Insubria and Fondazione Macchi 
 
Description

Aim:

Dysgraphia diagnosis is mainly based on writing speed on paper, but the causes which underlie such slowness are rarely investigated in clinical practice. Besides the evaluation of the overall speed, the digitalization of a Dysgraphia test would provide additional parameters related to gesture production, such as, fluidity, pressure, and tremor, and their variability during the execution.

The aim of this work is to validate the digital version of a test for Dysgraphia, to develop an end-to-end expert system which automates the diagnosis, and to leverage Machine Learning techniques to provide additional insights on gesture execution, towards a more targeted diagnosis.

Project phases:

– Literature review
– Data collection on healthy and dysgraphic subjects
– Data analysis to extract the score and the features of interest from the acquired data
– Statistical analysis
– Development of Machine Learning models

Requirements:

  • MATLAB, R or Python knowledge
  • Interest in Machine Learning and Deep Learning
  • Availability to reach schools for data acquisition
ThesisMachine Learning to longitudinally monitor graphical abilities, towards the early diagnosis of Dysgraphia
SupervisorSimona Ferrante (simona.ferrante@polimi.it)
Matteo Matteucci (matteo.matteucci@polimi.it)
Co-SupervisorLinda Greta Dui (lindagreta.dui@polimi.it)
Francesca Lunardini (francesca.lunardini@polimi.it)
CollaborationsProf. Cristiano TermineUniversità degli Studi dell’Insubria and Fondazione Macchi 
Dott. Luigi MacchiDott.ssa Simonetta BraliaProvveditorato, CTI, CTS di Varese
 
Description

Aim:

Disentangling transient handwriting difficulties from Dysgraphia is not a trivial task. To facilitate the process, an observational and empowerment study started two years ago.

The aim of this work is to longitudinally monitor handwriting-related problems, starting from preschoolers, to leverage Machine Learning techniques to predict the level of risk and evaluate the effectiveness of interventions, towards an early screening of Dysgraphia.

Project phases:

– Literature review
– Data collection
– Data Analysis
– Machine Learning algorithms implementation

Requirements:

  • MATLAB, R, or Python knowledge
  • Interest in machine learning
  • Availability to reach schools for data acquisition (province of Varese)

Computational Neuroscience

ThesisLarge-scale cerebellar Spiking Neural Networks to simulate sensorimotor paradigms in a virtual robotic environment.
SupervisorProf. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-SupervisorsAlessandra Trapani (alessandramaria.trapani@polimi.it)
Massimo Grillo (massimo.grillo@polimi.it)
Francesco Sheiban (francescojamal.sheiban@mail.polimi.it)
CollaborationsProf. Egidio D’Angelo and Claudia Casellato, Università di Pavia
 

 Description

Aim:

IN PROGRESS

Project phases:

  • Literature review
  •  

Requirements:

  • Basic knowledge of Python 
ThesisNeural Networks for the Decoding of Neural Signals from Behaving Monkeys
SupervisorAlessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Silvestro Micera (silvestro.micera@santannapisa.it)
CollaborationsPatrizia Fattori, Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna
  
Description

Aim:

Neural decoding is a critical step in BCI technologies. Different machine learning algorithms have been used to guide neural prosthetic limbs but results are far to get close at the natural body performance. In the last years, the availability of multi-electrode array system equipped with more and more recording channels is requiring a big data approach necessary. Together with the rising in computing power, the artificial neural networks (ANN) are a promising tool to address neural decoding problem. We propose to take advantage of modern ANN implementations to decode motor intentions from neural data recorded from behaving monkeys. Exploring different ANN architectures, the aim is to increase decoder robustness and reliability to be effectively implemented in clinical neuroprosthesis.

Project phases:

  • Literature research
  • Implementation of the ANN architecture
  • Data analysis
  • Interpretation and discusion of the results

This thesis foresees a development part at Scuola Superiore Sant’Anna in Pisa.

 
ThesisWavelet-based Analysis of Neural Population Dynamics in Reaching Tasks
SupervisorAlessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Silvestro Micera (silvestro.micera@santannapisa.it)
CollaborationsPatrizia Fattori, Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna
  
Description

Aim:

During tasks and actions of humans and animals, neuronal populations communicate using a complex interplay of transient oscillatory rhythms. Neural signals generated by the activity of neuronal populations also display this type of transient behavior. This makes non-stationary spectral analysis of neural signals using wavelet functions a useful tool to investigate neural activity.

Wavelet–based techniques are suitable for this type of signals since they possess one key property called multiresolution. Multiresolution allows to accurately determines low frequency components and simultaneously localize time rapidly transient events.

Here, we propose to analyze extracellular signals recorded from multi-electrode array implanted in the posterior parietal area of monkeys during reach-to-grasp and reach-to-target task. Specifically, we will consider the low frequency part (< 300 Hz) of the extracellular field, called Local Field Potential (LFP). LFPs will be analyzed using wavelet-based techniques, such as Continuous Wavelet Transform and Wavelet Coherence to determine which type of scales (frequencies) characterizes the different tasks both at single and multi-electrode levels. This is a key step in the decoding of neuronal population dynamics for brain-machine-interfaces and biomedical applications

Project phases:

– Literature research
– Data analysis
– Interpretation and discusion of the results

This thesis foresees a development part at Scuola Superiore Sant’Anna in Pisa.

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