Theses

Rehabilitation Engineering and Robotics


Assessing the correctness of physiotherapeutical tasks using IMU data only

Supervisor: Prof. Simona Ferrante
Collaborations: Prof. Thomas Seel and Timo Kuhlgatz, Institute of Mechatronic Systems, Leibniz University Hanover
Aim: Physiotherapy is most promising when the exercises are carried out regularly. However, due to the acute shortage of staff in the healthcare sector, supervised physiotherapy appointments are rare. Performing the exercises independently is therefore of great importance for the success of the therapy. To provide automatic feedback that supports patients in correctly performing the exercise, the patient’s motions can be measured using wearable inertial sensors and assessed in real time using suitable classification algorithms
Requirements:
Python programming and interest in physiotherapeutic tasks

DisturbanceRejection due to Non-Rigid IMU Attachment

Supervisor: Prof. Simona Ferrante
Collaborations: Prof. Thomas Seel, Institute of Mechatronic Systems, Leibniz University Hanover; Simon Bachhuber, Friedrich-Alexander-Universität Erlangen-Nürnberg
Aim: Soft-tissue motion (STM) describes the motion of skin relative to the bone structure. Inertial Motion Tracking (IMT) is the field dedicated to estimating dynamic motion of humans (or robots, vehicles etc.) in 3D space. In humans, dynamic movements lead to STM, which then degrades the IMT results, since the sensors are attached to the skin. Can we use machine learning methods to implicilty learn kinematic constraints and successfully eliminate the impact of SMT and recover the underlying rigid body motion?
Requirements:
Python programming and Familarity with deep learning frameworks and working on HPC

Co-design of hybrid robotic solutions for the upper limb rehabilitation of neurological patients @Aalborg University (DK)

Supervisor: Prof. Simona Ferrante
Collaborations: Erika G Spaich  Department of Health Science and Technology, Aalborg University
Aim: In the recent year the impact of robotics in the rehabilitation of neurological patients is becoming evident. This thesis is aimed at integrating functional electrical stimulation in the control loop of the armeo spring passive exoskeleton in order to further enhance the benefits of rehabilitation combining the two methodologies. The thesis will include: 1) the integration of a stimulation device in the setup, 2) the definition with clinicians of training exercise to be implemented, 3) the development of the FES hybrid control system to support the exercises; the development of a GUI for the clinician and the patient; 4) the testing and evaluation of the results (feasibility, usability…)
Requirements:
Interest in learning Matlab and Python

Robust simultaneous recognition of appearance and type of movements

Supervisor: Prof. Simona Ferrante
Collaborations: Prof. Thomas Seel, Institute of Mechatronic Systems, Leibniz University Hannover; Ive Weygers, Friedrich-Alexander-Universität Erlangen-Nürnberg
Aim: Accurate classification of movements such as daily living activities and functional movements is a crucial requirement for a growing number of digital health applications. Classification of isolated recorded movements has been well studied and achieved in many application domains. However, this task becomes challenging with long continuous motion streams of data where the duration and type of the task may vary. The task now requires simultaneous recognition of the occurrence & classification of the specific task, in a robust and ideally near real-time manner using mobile phone data.
Requirements:
Python programming and Android SDK for mobile and smartphone applications

Virtual reality for neurological patients rehabilitation

Supervisor: Prof. Simona Ferrante
Co-Supervisor: Linda Greta Dui
Collaborations: IRCCS Istituti Clinici Scientifici Maugeri, Milano
Aim: Patients who suffer from a neuroogical disease show difficulties in performing movements, e.g., «freezing of gate» in Parkinson Disease. It is known that specific cues – e.g., a rhythmical pacing – could help in starting the movement and keeping it fluid. However, it is still unknown the role of psychological distress. All these elements can be combined and tested in a virtual reality scene, that allows to manipulate variables in an immersive virtual world, whilst keeping the patients monitored.
The aim of the thesis work is (1) to develop an app for a VR experience to be paired with movement analysis technologies, and (2) to test it in order to assess usability and the effect of different modifiers on movement performance.
Requirements:
Interest in learning C# and Unity

Multimodal analysis of the supraspinal locomotor control in patients with Parkinson

Supervisor: Prof. Alessandra Pedrocchi, Prof. Ioannis U. Isaias
Co-Supervisor: Ing. Chiara Palmisano Ph.D, Ing. Laura Caffi

Collaborations: Julius-Maximilians-Universität (Würzburg, Germany), Centro Parkinson e Parkinsonismi ASST G. Pini-CTO (Milano, Italy)
Aim: Gait modulation is a complex motor task involving multiple cortical and subcortical brain areas. In Parkinson’s disease (PD), gait disturbances result in a dramatic reduction in quality and quantity of life. The candidate will analyze FDG-PET data and EEG recordings of PD patients collected during resting and walking in a fully immersive virtual reality environment specifically designed to induce and study gait modulation. Project phases: Literature review on PD, supraspinal locomotor network, PET and EEG data analysis methods, Data recordings in a small subset of patients, Data analysis and statistics, Presentation of the data at a national conference (if the candidate proves merit), Thesis and article writing
Requirements:
Basic knowledge of MATLAB ,Proactive, independent and creative attitude, Availability to spend about 3-4 months in Würzburg (housing and travel costs reimbursed), Priority will be given to students interested in a future Ph.D.

Long-term home monitoring of subcortical neural signals and motor behavior in Parkinson’s patients

Supervisor: Prof. Alessandra Pedrocchi, Prof. Ioannis U. Isaias
Co-Supervisor: Ing. Chiara Palmisano Ph.D, Ing. Laura Caffi

Collaborations: Julius-Maximilians-Universität (Würzburg, Germany), Centro Parkinson e Parkinsonismi ASST G. Pini-CTO (Milano, Italy)
Aim: The student will analyze neural data recorded from the subthalamic nucleus over months in patients with Parkinson’s disease (PD) and deep brain stimulation (DBS). The goal is to correlate the neural signals with disease symptoms, response to neurostimulation therapy, and main activities of daily living (e.g., walking, talking, sleeping, etc.) for the development of novel stimulation paradigms (adaptive DBS).
Project phases: Literature review on PD, DBS, and home monitoring techniques, Training on wearable sensors and DBS recordings, Data recordings in a small subset of patients, Data analysis and statistics, Presentation of the data at a national conference (if the candidate proves merit), Thesis and article writing.
Requirements:
Basic knowledge of MATLAB ,Proactive, independent and creative attitude, Availability to spend about 3-4 months in Würzburg (housing and travel costs reimbursed), Priority will be given to students interested in a future Ph.D.

Study of the impact of robotic therapy in children with autism

Supervisor: Prof. Emilia Ambrosini, prof Alessandra Pedrocchi
Co-Supervisor: Laura Santos, Gabriele Fassina
Collaborations: Fondazione Don Gnocchi Instituto Superior Técnico – Universidade de Lisboa
Aim: Autism still does not have any cure. Several therapies have been developed using robots but their impact is still not clear. To clarify this aspect, a Randomised Controlled Trial has been conceived and has just started in Fondazione Don Gnocchi. The aim of this work is to analyse the results of the ongoing therapy to draw solid conclusion about its effectiveness.
Project phases:
1. Literature analysis about the Robot Assisted Therapies for autism and their assessment
2. Acquisitions at Fondazione don Gnocchi
3. Analysis of the therapy outcomes regarding motion and cognitive parameters extracted from Azure Kinect.
Requirements:
Motivation to interact with the therapists, Good Programming skills in Matlab; availability to move to Fondazione don Gnocchi.

Development of a quantitative tool for the assessment of Autism Spectrum Disorder

Supervisor: Prof. Emilia Ambrosini; prof. Alessandra Pedrocchi
Co-Supervisor: Gabriele Fassina, Laura Santos
Collaborations: Fondazione Don Gnocchi Instituto Superior Técnico – Universidade de Lisboa
Aim: Autism Spectrum Disorder (ASD) is currently assessed through clinical scales and questionnaires which suffer from high inter/intra-subject variability. Hence, research seeks to identify new quantitative and objective assessment tool which could benefit from the new advances in technology (e.g. AI).  In this view, Joint Attention, i.e. the capability of sharing attention with a partner, could be analysed quantitatively. This project aims at developing an algorithm to quantify Joint Attention from the videos recorded during the administration of a clinical scale. Project Phases:
1. Literature review on ASD symptoms and diagnosis, Computer Vision tools for body and face recognition;
2. Development of a Deep Learning algorithm to quantify Joint Attention;
3. Acquisitions of sessions involving both Typically Developing children and ASD children to apply the proposed algorithm.
Requirements:
Motivation to interact with the therapists, Good Programming skills: Python and Matlab; basic knowledge of Deep Learning.

Design and test of control solutions for upper limb exoskeleton

Supervisor: Prof. Alessandra Pedrocchi
Co-Supervisor: Beatrice Luciani
Collaborations: Prof. Marta Gandolla – WE-COBOT
Aim: Motorized upper limb exoskeletons have been proposed to support motor rehabilitation and/or to assist disabled people during daily life activities. We have developed and tested a 4DOF upper limb exoskeleton – AGREE. The thesis is aimed at developing and testing control solutions for the support of rehabilitation sesions (i.e., tunneling, force fields, assisted-as-needed solutions, synergy-based control, etc.)
Requirements:
Basic knowledge of Robotics​,Good knowledge of C/C++ and MATLAB​,Interest in learning ROS/MoveIt

Service robotics in nursing home: applications for elderly care

Supervisor: Prof. Alessandra Pedrocchi
Co-Supervisor: Luca Pozzi
Collaborations: Prof. Marta Gandolla – WE-COBOT
Aim: The rapidly growing demand for long-term care will become a hard challenge for the healthcare system. An investigation of the unmet-needs of nursing homes and the possible use-cases for a service robot, highlighted the following main areas of focus: (i) communication and socialization; (ii) cognitive stimulation and entertainment; (iii) physical activity and rehabilitation. In this context, the student activity will consist in the design, implementation and testing of service robot software applications (or new features for existing applications), addressing one or more of the identified area of focus. The activity will be based at the inter-dipartimental WE-COBOT  (Wearable and Collaborative Robotics) lab in Lecco
Requirements:
Basic knowledge of Robotics​,Good knowledge of C/C++ and MATLAB​,Interest in learning ROS/MoveIt

Evaluation of a exoskeleton-mediated therapy: RCT study

Supervisor: Prof. Alessandra Pedrocchi
Co-Supervisor: Beatrice Luciani
Collaborations: Prof. Marta Gandolla – WE-COBOT, Casa di Cura del Policlinico «Dezza»
Aim: We have conducted a RCT study with the AGREE exoskeleton – an upper limb motorized 4DOF exoskeleton with neurological patients at Casa di Cura del Policlinico Dezza. We have collected instrumental and clinical data and outcome measures, which need to be processed to evaluate the effectiveness of the inclusion of the robotic-based therapy with respect to the standard care alone.
Requirements:
Basic knowledge of MATLAB​

Benchmarking of upper limb functions in neurological disorders

Supervisor: Prof. Alessandra Pedrocchi
Co-Supervisor:
Collaborations: Prof. Marta Gandolla – WE-COBOT, Hospial Los Madronos (Madrid)
Aim: We have developed a benchmarking scheme to evaluate upper limb functions in neurological disorders, and we have tested it on a group of healthy subjects. The scheme foresees a protocol and a list of relevant outcome measures. So that the scheme is effectively useful in the clinical context, this project wants to develop an application that guides the acquisition, data analysis and interpretation.
Requirements:
Basic knowledge of MATLAB​/C++

Estimation of lower limb loads in overground running using a minimal set of wearables

Supervisor: Prof. Emilia Ambrosini
Co-Supervisor: Dr Ezio Preatoni
Collaborations: Department for Health, University of Bath
Aim: Running is one of the most common modes of physical activity, with nearly 7 (UK) and 50 (EU) million people (Sports England, 2018; Scheerder et al., 2015) running for health, recreational or competitive purposes. The large majority of runners (~75%) regularly uses consumer technology (e.g. smart-phones/watches, HR-monitors) and consider it an essential aid to monitor training, improve performance and correct movement behaviours that could increase injury risk (Clermont et al, 2019). Wearable technologies show great potential: not only do they provide valuable feedback to runners, but they also offer sport performance and injury prevention research the opportunity to remove the limitations of lab-based analyses, collect “real-world” data for extensive periods of time, and enable prospective studies on a wide scale. However, these technologies still require rigorous scientific scrutiny to test and improve the quality of their measurements (Camomilla et al., 2018). Also, the availability of large datasets makes it necessary to develop bespoke methods for the extraction of meaningful and readily interpretable information. The aim of this dissertation project is to develop and test new methods that use a minimal set of wearables to estimate lower limb loading during running.

Project phases: Literature review; EthData collection; Data analysis and interpretation ; ics & participant recruitment

Requirements: Good skills / independence in coding (Matlab and/or Python) and data analysis (ideally including machine learning); Certificate of English proficiency (or demonstrating to have attended University Courses taught in English)

Optimization and testing of a hybrid trike system for Functional Electrical Stimulation (FES) – cycling in Spinal Cord Injury and post stroke people 

Supervisor: Prof. Emilia Ambrosini
Co-Supervisor: Davide Savona (davide.savona@polimi.it)
Aim: In the last few years, some studies have presented FES trikes equipped with an electric motor, capable of providing pedaling assistance when needed, guaranteeing a minimum constant cadence when FES is insufficient, preventing and compensating for the presence of muscle fatigue and enhancing therapeutic outcomes. Inside the Active3 project, a prototype of an hybrid FES trike was presented featuring an initial cooperative control design that seeks to merge the FES control with the electric motor control. The aim of the project is to optimize the control strategy and to support the testing of the system, both during preliminary tests on healthy subjects and during the clinical study on SCI and stroke patients.

Project phases: 1.Literature review on Functional Electrical Stimulation and motor assisted FES-cycling, familiarization with previous works and the prototype; 2. Optimization of the control algorithm; 3. Design of a protocol to test the prototype both with healty people and patients ;Data acquisition and support during tests; Data analysis and interpretation.
Requirements:
Experience in programming (C++); Knowledge of Matlab and Matlab Signal Processing Toolbox; Availability to move to Polo Territoriale di Lecco where the prototype is located and to Villa Beretta (Costa Masnaga LC) where some tests may be conducted.

Integrating Functional Electrical Stimulation in an upper limb exosuit: a hybrid approach

Supervisor:  Prof. Emilia Ambrosini
Co-Supervisor: Elena Bardi, Davide Savona
Collaborations:  DMEC
Aim: In the context of rehabilitation and assistance, soft exoskeletons (exosuits) represent a promising technology. Nevertheless, the rehabilitative potential of such devices could be boosted with the integration of FES (Functional Electrical Stimulation). The aim of the thesis is to integrate FES in an exosuit for the upper limb and to develop a hybrid control strategy.
Requirements:
Good knowledge in programming (Python, C++)

Quantifying daily impact of spinal cord stimulation for locomotion restoration in spinal cord injury through wearable sensors and deep learning

Supervisor:  Prof. Emilia Ambrosini
Co-Supervisor: Dr. Andrea Bandini
Collaborations:  Scuola Superiore Sant’Anna, Pisa; Ospedale San Raffaele, Milano
Aim: The goal of this study is to investigate the impact on daily life of spinal cord stimulation for restoring locomotion among individuals with spinal cord injury. To enhance the efficacy of the intervention and tailor treatment strategies to individual needs, it is crucial to monitor patients during their daily activities. Wearable sensors offer the opportunity to gather continuous data in unstructured scenarios. We aim to identify and analyze the activities of individuals with spinal cord injury throughout their neurorehabilitation, shedding light on the impact of spinal cord stimulation on their daily lives. The project will employ deep learning algorithms to recognize various activities, such as walking, wheelchair movement, and static positions, providing a quantitative and objective assessment of patients’ mobility post-rehabilitation.

Project phases: Literature review; Data collection Development and validation of deep learning algorithms for activity recognition.

Requirements: Knowledge of Matlab and python; Availability to move to San Raffaele Hospital to conduct data collection

Methods for MR Image Segmentation and Quantitative Multiparametric MRI Analysis of Skeletal Muscle

Supervisor:  Prof. Ambrosini
Co-Supervisor: Dott. Alfonso Mastropietro
Collaborations:  Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA) del Consiglio Nazionale delle Ricerche – Milano
Aim: The primary objective of this thesis is to develop and evaluate innovative methods for Magnetic Resonance (MR) image segmentation and quantitative multiparametric-MRI analysis of skeletal muscle. We aim to enhance the accuracy and reliability of identifying and analyzing skeletal muscle tissues, leading to improved diagnostic and treatment strategies for related medical conditions.

Project Phases: 1. Literature Review: Comprehensive study of existing methods and technologies in MR image segmentation and multiparametric MRI quantitative analysis; 2. Methodology Development and Implementation: Creation of advanced algorithms or enhancement of existing ones for more precise skeletal muscle identification and quantitative analysis; 3. Evaluation: Comparative analysis with existing methods using predefined metrics to evaluate performance improvements; 4. Final Analysis & Reporting: Comprehensive assessment of all phases with detailed reporting on findings, improvements, challenges, and recommendations for future research.

Requirements: Knowledge of Python, MATLAB, 3D-Slicer, ITK-SNAP

ParkAGILE: Valutazione cinematica ed elettromiografica di protocolli fisioterapici di movimento per il benessere muscolare dei lavoratori digitali

Supervisor:  Prof. Pedrocchi
Co-Supervisor:
Collaborations:  https://www.parcfor.com/
Aim: ParcAGILE è un attrezzo fisioterapico, compatto e completo, in grado di prevenire e riabilitare il disturbo muscolo scheletrico di origine professionale.
Un sistema unico e innovativo che consente di eseguire, nelle pause fisiologiche di lavoro, movimenti defaticanti, di allungamento muscolare e di mobilità delle articolazioni principalmente coinvolte dalle attività lavorative. L’obbitettivo della tesi è la valutazione cinematica ed elettromiografica necessaria ad ottenere dei feedback di efficacia sull’uso del dispositivo ParcAGILE. Ad integrazione, si ipotizza la realizzazione di un’applicazione che consenta all’utente di svolgere in autonomia una serie di programmi fisioterapici utilizzando ParcAGILE

Digital Health


RAN-IO: Radiomics as biomArker imaging for NSCLC patients treated with ImmunOtherapy

Supervisor: Prof. Alessandra Pedrocchi
Co-Supervisor: Dr. Arsela Prelaj
Aim: Predicting Immunotherapy (IO) efficacy in Non-Small Cell Lung Cancer (NSCLC) is a crucial unmet clinical need. Beyond Programmed Death Ligand-1 (PD-L1) no other biomarkers are approved. However, PD-L1 is quite Imperfect. Radiomics was recently used as imaging biomarker in NSCLC patients treated with IO. This thesis aim is to find a predictive radiomic signature for NSCLC patients treated with IO.
Requirements: Knowledge of Matlab, Knowledge of Python (scikit-learn, TensorFlow/Keras); Basic knowledge of Git and Linux is welcome

CURE-T790M: NSCLC EGFR mutated patients predicting T790M+ using AI

Supervisor: Prof. Alessandra Pedrocchi
Co-Supervisor: Dr. Arsela Prelaj
Collaborations:
Aim: Osimertinib (OSI), a third-generation tyrosine kinase inhibitor (TKI) is now become the standard of care and is given as first line therapy in NSCLC patients with EGFR mutation (mut) since it demonstrated superiority compare to 1st and 2nd generation (G) TKI. Previously, OSI was received in 2nd line  after disease progression upon 1st or 2nd  generation TKI in around 50% of patients who acquired an exon20 resistance mutation called T790M. Recent data demonstrated that combination therapy with 1st G TKI in first Line obtained the same PFS compare to OSI. Thus predicting the onset of T790M can prolong survival of around 12 months in around 50% using combo therapy in first line and OSI in second line as sequence therapy. The aim of this study is to predict T790M using AI techniques by integrating all data of NSCLC patients with EGFR mut.
Requirements: Knowledge of Matlab, Knowledge of Python (scikit-learn, TensorFlow/Keras); Basic knowledge of Git and Linux is welcome

Multimodaldata integration using explainable AI to predict the efficacy of immunotherapy in lung cancer patients

Supervisor: Prof. Alessandra Pedrocchi
Co-Supervisor: Vanja Miskovic PhD
Collaborations: Istituto Nazionale dei Tumori Milano (INT), Arsela Prelaj MD
Aim: Lung cancer is the leading cause of cancer-related death globally. In the last years Immunotherapy (IO), changed the therapeutic and prognostic process. The big challenge is to select the patients that would benefit from the IO treatment. Machine and Deep Learning (ML and DL) methodologies are able to analyze complex behaviors, from different types of data and increase the accuracy of prediction biomarkers leading to the selection of patients who can benefit from IO. In this project, we aim to develop an explainable AI model that uses multi-omics and real-world data to predict the efficacy of IO in Non-small-cell-lung-cancer patients (NSCLC).
Requirements: Good knowledge of Python; Basic knowledge of Machine and/or Deep Learning

Prendiciting Immuno-therapy response using AI in NSCLC patients integrating multiomics data

Supervisor:  Prof. Alessandra Pedrocchi
Co-Supervisor: Dr. Arsela Prelaj
Collaborations:
Aim: Predicting Immunotherapy (IO) efficacy oin NSCLC is a crucial unmet clinical need. Beyond Programmed Death Ligand-1 (PD-L1) no other biomarkers are approved. However, PD-L1 is quite Imperfect. Thus, AI which is able to integrate high dimensional real world and multiomics data could be a unique approach able To find an algorithm able to predict IO in NSCLC patients. The later will be this thesis aim.
Requirements: Knowledge of Matlab; Knowledge of Python (scikit-learn, TensorFlow/Keras);Basic knowledge of Git and Linux is welcome

Development of an application to promote upper limb exosuit user engagement and monitor motor performance

Supervisor:  Prof Simona Ferrante, Prof. Emilia Ambrosini
Co-Supervisor: Chiara Piazzalunga, Elena Bardi
Collaborations:
Aim: In the context of rehabilitation and assistance, soft exoskeletons (exosuits) represent a promising technology. Nevertheless, the acceptance of assistive devices is a challenge to be targeted. The aim of the thesis is to develop an application to provide daily feedback on the use of an upper limb soft robotic suit to promote user engagement and to monitor the motor ability of the user on a daily basis. The metrics are extracted from a set of wireless inertial measurement units (IMUs) placed on the user’s thorax and arm.
Requirements: Good knowledge in programming (Python, Matlab)

Machine Learning to longitudinally monitor graphical abilities, towards the early diagnosis of Dysgraphia

Supervisor: Prof Simona Ferrante
Co-Supervisor: Linda Greta Dui
Collaborations: Indipote(dn)s project, Prof. Cristiano Termine (Università dell’Insubria)
Aim: Disentangling transient handwriting difficulties from Dysgraphia is not a trivial task. To facilitate the process, an observational and empowerment study longitudinally followed 200 children for three years, starting from kindergarten and reaching second grade. The aim of this work is to leverage Machine Learning techniques on longitudinal data to (1) predict the level of risk and (2) evaluate the effectiveness of interventions, towards an early screening of Dysgraphia.
Requirements: Matlab, R or Python knowledge; interest in machine learning

Voice analysis to evaluate the effect of treatments in neurological patients

Supervisor: Prof Simona Ferrante, Prof. Emilia Ambrosini
Co-Supervisor: Chiara Giangregorio
Collaborations: Istituto Neurologico “Carlo Besta” | Fondazione IRCCS, Milano
Aim: Neurological conditions, such as dementia and the several associated neurodegenerative diseases (e.g. Parkinson’s), are a significant health concern globally, impacting around 10 million new cases annually. Rapid assessment is needed and in this regard voice has been widely studied as a digital biomarker for potential mass-screening of these pathologies. In this way,  timely administration of innovative therapies can help improve quality of life, slowing down the degenerative process.  The project aims at analyzing acoustic features extracted from voice data to assess the  effectiveness of these treatments.  Pre- and post- treatment assessment are conducted on a cohort of patients recruited at the Besta Institute. Evaluation are conducted through clinical scales and through a mobile app which will capture acoustic features from speech.
Requirements: Basic knowledge of Matlab; interest in machine learning

Voice analysis for dysphagia screening among neurological patients

Supervisor: Prof Simona Ferrante
Co-Supervisor: Monica Parati
Collaborations: IRCCS Istituti Clinici Scientifici Maugeri (Milan, Italy)
Aim: Oropharyngeal dysphagia (i.e. swallowing disorders) is an often overlooked and underdiagnosed condition in patients with a neurological disorder (e.g. stroke, Parkinson’s disease, and amyotrophic lateral sclerosis), despite its high prevalence and associated serious pneumonia complications. In clinical practice, its diagnosis requests a specialized team and invasive exams. The analysis of voice signals could provide objective indices to detect oropharyngeal dysphagia through a noninvasive, easy-to-use tool to be applied at the patient’s bedside, but also in remote settings. The aim of this work is to develop and validate a software tool able to automatically extract a set of acoustic features sensitive and sensible enough to identify patients at high risk of oropharyngeal dysphagia from voice signals.
Requirements: Knowledge of Matlab; interest in machine learning

Detection and analysis of cough in lung cancer patients

Supervisor: Prof Simona Ferrante; Prof. Emilia Ambrosini
Co-Supervisor: Chiara Giangregorio
Collaborations: Istituto Nazionale dei Tumori Milano (INT), Arsela Prelaj MD
Aim: It has been proved that 65% of people with lung cancer have a chronic cough by the time they’re diagnosed (up to 80% or higher for those with advanced disease). In the context of I3Lung, a European project which “ensures access to innovative, sustainable and high-quality health care”, an app has been developed for the automatic and ecological monitoring of cough in lung cancer patients. This thesis aims to develop a machine (or deep) learning algorithm to analyze cough to ensure therapy’s effectiveness.
Requirements: Basic knowledge of Matlab; Basic knowledge of Machine Learning and Python

Machine Learning for premature babies care

Supervisor: Prof Simona Ferrante
Co-Supervisor: Linda Greta Dui
Collaborations: Marco Frontini (Link Up s.r.l.); Valentina Bozzetti (Ospedale San Gerardo, Monza)
Aim: Premature babiesneed special care programs to overcome their frailty condition. Their organs are immature and the healthcare support to assure their survival is often invasive (e.g., ventilation, parenteral nutrition). It is important to investigate the effect of different care strategies to personalize the care to maximize the outcome
Requirements: Interest in machine learning

Development of a quantitative tool for the assessment of Autism Spectrum Disorder

Supervisor: Prof. Emilia Ambrosini; prof. Alessandra Pedrocchi
Co-Supervisor: Gabriele Fassina, Laura Santos
Collaborations: Fondazione Don Gnocchi Instituto Superior Técnico – Universidade de Lisboa
Aim: Autism Spectrum Disorder (ASD) is currently assessed through clinical scales and questionnaires which suffer from high inter/intra-subject variability. Hence, research seeks to identify new quantitative and objective assessment tool which could benefit from the new advances in technology (e.g. AI).  In this view, Joint Attention, i.e. the capability of sharing attention with a partner, could be analysed quantitatively. This project aims at developing an algorithm to quantify Joint Attention from the videos recorded during the administration of a clinical scale. Project Phases:
1. Literature review on ASD symptoms and diagnosis, Computer Vision tools for body and face recognition;
2. Development of a Deep Learning algorithm to quantify Joint Attention;
3. Acquisitions of sessions involving both Typically Developing children and ASD children to apply the proposed algorithm.
Requirements:
Motivation to interact with the therapists, Good Programming skills: Python and Matlab; basic knowledge of Deep Learning.

Monitoring of patients with Parkinson’s disease during walking in free-living and challenging conditions

Supervisor: Prof. Simona Ferrante
Co-Supervisor: Monica Parati
Collaborations: IRCCS Istituti Clinici Scientifici Maugeri, Milano
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.
Requirements: Interest in working with patients; Basic knowledge of Matlab and programming languages

Handwriting Analysis in Primary School Children with an IoT Smart Ink Pen

Supervisor: Prof. Simona Ferrante
Co-Supervisor: Simone Toffoli, Linda Greta Dui
Collaborations: Università degli Studi dell’Insubria
Aim: 10 to 30% of pupils in primary school show difficulties in mastering handwriting movement skills, a condition known as dysgraphia. Such difficulties have a negative impact on many aspects, like the child’ self-esteem, personal relationship, academic and employment achievements. The diagnosis is performed during grade 3 and is based on the qualitative observation of the handwritten product. National guidelines suggest the development of quantitative approaches for the analysis of children’s handwriting performance (process analysis) in order to: i) create normative data for Italian children of primary school; ii) compare normally developing pupils and children classified as dysgraphic by current clinical protocols. The thesis aim is the collection of handwriting data from pupils with a smart ink pen developed at Nearlab, followed by data  analysis.
Requirements: Knowledge of Matlab and Python; Interest in data acquisition in schools

Automation of a serious game for the early screening of dysgraphia

Supervisor: Prof. Simona Ferrante
Co-Supervisor: Linda Greta Dui
Collaborations: Università degli Studi dell’Insubria
Aim: Disentangling transient handwriting difficulties from Dysgraphia is not a trivial task. To facilitate the process, a serious game has been devised, but data analysis needs to be refined to allow the game to be used in a pre-clinical scenario. The aim of the thesis is to optimise data analysis algorithms to automate the prediction of the risk of dygraphia.
Requirements: Knowledge of Matlab, R or Python; Interest in data analysis and machine learning

IoT smart ink pen to evaluate the effect of Focused UltraSound (FUS) treatment in patients with essential tremor

Supervisor: Prof. Simona Ferrante
Co-Supervisor: Simone Toffoli
Collaborations: Istituto Neurologico “Carlo Besta” | Fondazione IRCCS, Milano
Aim: Essential tremor (ET) is the most common movement disorder, affecting an estimated 3% of the population. Focused ultrasound is a noninvasive therapeutic technology that focuses multiple beams of ultrasound energy precisely and accurately on targets deep in the brain without damaging surrounding normal tissue. Pre- and post- treatment assessment are conducted on a cohort of patients recruited at the Besta Institute. Evaluation are conducted through clinical scales and through an instrumented smart ink pen able to extract relevant clinical parameters. 
Requirements: Basic knowledge of Matlab; interest in statistics and data analysis

IoT smart inkpen for early detection and monitoring of patients with Parkinson’s disease

Supervisor: Prof. Simona Ferrante
Co-Supervisor: Simone Toffoli, Monica Parati
Collaborations: IRCCS Istituti Clinici Scientifici Maugeri, Milano
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.
Requirements: Knowledge of Matlab and Python

IoT smart ink pen for early detection and monitoring of patients with mild cognitive impairments and dementia

Supervisor: Prof. Simona Ferrante
Co-Supervisor: Simone Toffoli
Collaborations: Alice Naomi Preti Università degli Studi di Milano Bicocca
Aim: Dementia affects millions of people worldwide. 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 characterise the longitudinal evolution of handwriting in patients affected by dementia, using both an IoT smart ink and a digitizing tablet. Evaulated tasks include spontaneous handwriting (e.g., signature) and dictated handwriting. 
Requirements: Basic knowledge of Matlab and Python

Teleconsultation to remotely assess learning abilities

Supervisor: Prof. Simona Ferrante
Co-Supervisor: Linda Greta Dui, Simone Toffoli
Collaborations: Università dell’Insubria
Aim:

Remote health has several advantages, from widening access to the healthcare service to reducing costs, and its importance became even more evident during the years of the pandemic, when access to hospitals was limited. However, the feasibility of performing clinical tests from a remote setting still needs to be verified. During the ESSENCE project, a teleconsultation app was developed and tested with children, that were evaluated for their reading and writing abilities, also by the use of an IoT smart ink pen.
The aim of the thesis is to further develop the application based on firts pilot tests already done and to test it in primary schools in collaboration with neuropsychologists. 


Requirements: Interest in data analysis

Automation and enrichment of dysgraphia screening

Supervisor: Prof. Simona Ferrante
Co-Supervisor: Linda Greta Dui
Collaborations: Università dell’Insubria
Aim: Dysgraphia diagnosis is mainly based on writing speed on paper, but clinical test evaluation is subjective, time expensive, and the causes which underlie such slowness are rarely investigated in clinical practice. The aim of this thesis is to provide an autmatic scoring for dysgraphia tests staring from images, and to enrich it with data coming from an IoT smart ink pen used diring their execution
Requirements: Basic knowledge of Matlab, R or Python; interest in Deep Learning

Validation of a digital graphomotor testing system for neuropsychological evaluation

Supervisor: Prof. Simona Ferrante
Co-Supervisor: Monica Parati, Chiara Piazzalunga
Collaborations: IRCCS Istituti Clinici Scientifici Maugeri, Milano
Aim: Using digital technology for neuropsychological assessment offers many benefits over traditional paper-pencil assessments for testing visuospatial abilities in patients with neurological diseases. It can provide new quantitative, standardized, and more precise measures that are not available in traditional tests (i.e. spatial sequencing and movement velocity), it can speed up testing sessions, and enable assessments outside of clinical/research settings. A tablet-based application has been recently developed for the digitalization of three neuropsychological tests. The aim of this thesis is to refine and validate graphomotor features collected from the tablet-based application in a cohort of neurological patients during neuropsychological assessments.
Requirements: Interest in working in a clinical environment and availability to participate in data collection; knowledge of Matlab and intention to learn other programming languages

Computational Neuroscience


Signal encoding in a morphologically detailed cerebellar spiking model for simulating an adaptive reaching task

Supervisor: Prof. Alberto Antonietti, Alessandra Pedrocchi
Co-Supervisor: Eng. Benedetta Gambosi
Collaborations: University of Pavia

Aim: The bottom-up strategy is one possible approach to studying the brain. It attempts to construct biologically realistic neuronal network models based on the detailed knowledge of the constituting components. The assumption is that with sufficient accuracy in structure and dynamics, the correct functions will emerge. Toward this aim, we want to simulate a control system integrated with a detailed model of the cerebellum able to perform an adaptive reaching task.
The candidate will first investigate how to encode motion-related signals as cerebellar inputs; then, they will analyse how these signals propagate into the cerebellar circuit and turn into the physiological cerebellar output signals. The model’s overall performance will be evaluated by embedding it in a comprehensive control system to drive a closed-loop reaching task simulation.

Requirements: Knowledge of Python; Knowledge of C++ (not mandatory, but preferable); Willingness to work in a collaborative environment

Development of realistic mouse whiskers for a mouse neurorobot

Supervisor: Prof. Alberto Antonietti, Prof. Alessandra Pedrocchi
Collaborations: University of Pavia
Aim: The aim of the thesis is to enhance the existing robotic whisking system by incorporating more realistic properties into the design. The research focuses on a mouse robot model that currently consists of four rigid whiskers. The goal is to extend this model by introducing whiskers with elastic properties, thereby making them more similar to actual mouse whiskers. Additionally, the thesis aims to integrate these realistic whiskers into an active whisking task, where the robot can actively engage with its environment using its whiskers.
In summary, the main objectives of the thesis are as follows:
Enhancing Realism: Modify the existing rigid whiskers to possess elastic properties, mirroring the flexibility and sensitivity of real mouse whiskers. This enhancement is crucial for the robot to accurately perceive and interact with its surroundings, replicating the way a mouse uses its whiskers for tactile sensing.
Integration into an Active Whisking Task: Develop a control system that enables the robot to actively whisk and perform tasks based on the sensory input received from its whiskers. This involves programming the robot to move its whiskers purposefully, interpret the feedback obtained through whisker interactions, and respond accordingly.
Sensorimotor Integration: Study the integration of sensory information from the whiskers with the motor control system of the robot. This involves understanding how the robot processes tactile data from its whiskers and translates it into appropriate motor responses, enabling it to navigate and interact with the environment effectively.
Validation and Testing: Rigorously test the enhanced robotic whisking system to validate its effectiveness and reliability. This includes assessing its ability to accurately perceive different textures, distances, and objects through whisker interactions and evaluating its performance in various real-world scenarios.

By achieving these objectives, the thesis aims to contribute valuable insights into the development of advanced robotic systems that can mimic the sensory and motor capabilities of living organisms, specifically focusing on the tactile sensing abilities of mice. This research has the potential to advance the field of robotics, leading to the creation of more sophisticated and capable robots for various applications, including those in neuroscience, artificial intelligence, and human-robot interaction.

Requirements: Strong coding skills in Python and C++, passion for robotics and soft robotics topics, and attitude to independent work.

Modelling the mammalian brainstem to explore the neural mechanisms of sound localization

Supervisor: Prof. Alberto Antonietti
Co-Supervisor: Francesco De Santis
Collaborations: Imperial College London, Università di Genova


Aim: Implementing bioinspired neural networks in a computational environment is a formidable instrument to shed light into the complexity of brain processes. Such networks grant access to the dynamic activities of individual neurons within intricate circuitry, particularly those engaged in executing neurosensory functions.

This thesis proposes to study how the mammalian brainstem implements sound localization: the ability to identify an acoustic source in the surrounding space.  We have reconstructed a model made of thousands of spiking neurons tailored to the auditory brainstem circuitry and its tonotopic organization.

The candidate will work hand by hand with supervisor and co-supervisor in attempt to improve the overall performance of the brainstem computational model, to replicate the experimental evidences obtained by several studies in mammals and to introduce realistic learning mechanisms in the network, such as spike timing dependent plasticity (STDP) and other synaptic processes concerning different physiological signaling molecules.

Requirements: knowledge of Python; curiosity for neuroscientific topics; willingness to work in a collaborative environment

Simulating a thalamic motor controller

Supervisor: Prof. Alberto Antonietti, Alessandra Pedrocchi
Co-Supervisor: Eng. Francesco Sheiban
Collaborations: University of Pavia

This master’s thesis focuses on the refinement and validation of a spiking neural network (SNN) model representing mouse motor thalamic nuclei. The network has been anatomically designed integrating data at multiple scales to place and connect neurons, yet it remains unexplored in terms of simulation outcomes.

The primary objective is to tune the model parameters systematically to align simulated neural dynamics with experimental electrophysiological data. Upon successful parameterisation, the model will serve as a powerful tool for investigating system controller properties and simulating behavioural tasks associated with mouse motor thalamic function.

Methodology:
Detailed examination of the existing spiking neural network model, dynamical systems analysis and characterisation, validation of the tuned model through rigorous comparison with empirical data. The thesis will be carried out using the NEST Simulator.

Requirements:
The student undertaking this project should possess strong Python programming skills and a foundational understanding of basic computational neuroscience concepts. While the spiking neural network has already been implemented, a grasp of dynamical systems theory is crucial for effective parameter tuning and interpretation of simulation outcomes.

Implementation of methods and analysis for cardiac excitable tissue on a multi electrode array system

Supervisor: Prof. Alessandra Pedrocchi, Prof. Alberto Antonietti, Dott. Andrea Menegon, Dott. Giovanni Peretto

Co-Supervisor: Francesco De Santis

Collaborations: IRCCS San Raffaele Hospital

Aim:

The use of MEA (Multi Electrode Array) is a well-accepted technique for recording electrical signals from excitable cells and tissues with high spatial and temporal resolution. The ability to record multiple evoked-responses in parallel at different electrodes within a single slice and within the same region of interest increases the reliability of the data and their subsequent statistical power. However, data from MEA recordings have traditionally been analysed manually, which is labour-intensive, slow and often user dependent.

This thesis proposes a comparative study on cardiac tissue affected by myocarditis, with particular emphasis to the development of an innovative software for the MEA readout. The work will be held at the IRCCS San Raffaele Hospital, in strict collaboration with the doctors, and it will cover each step of the experimental workflow, from the realization of in vitro cultures of cardiomyocytes to the design of the electronic set-up and the implementation of the the readout system.

Requirements: knowledge of Python; electronic background; willingness to develop different skills; interest in medicine and in pathologies

Development of Methods for Assessing Structural Connectivity from Diffusion Tensor Imaging  

Supervisor: Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it)Co-Cosupervisor: Dott. Alfonso Mastropietro
Collaborations: Istituto di Sistemi e Tecnologie Industriali Intelligenti per il Manifatturiero Avanzato (STIIMA) del Consiglio Nazionale delle Ricerche – Milano
Aim: to develop and evaluate methods for assessing structural connectivity from diffusion tensor imaging (DTI), a technique that measures the diffusion of water molecules in brain tissue. Structural connectivity refers to the anatomical connections between different brain regions, which can be inferred from the direction and magnitude of water diffusion along white matter tracts. DTI can provide valuable information about the integrity and organization of the brain’s wiring, which is relevant for various neurological and psychiatric disorders.

The project phases are: 1.Literature review: A comprehensive survey of the existing methods for structural connectivity analysis from DTI, including tractography, graph theory, network analysis, and machine learning approaches: 2. Method development: The design and implementation methods for structural connectivity assessment from DTI, based on the state-of-the-art techniques; 3. Method evaluation: The testing and validation of the proposed methods on real datasets, using appropriate metrics and benchmarks.

Requirements: Knowledge of Python, MATLAB, 3D-Slicer, ITK-SNAP, MrTrix