Available master thesis

Thesis Artificial Intelligence integration in Clinical Trial Management System (CTMS)
Supervisor Simona Ferrante (simona.ferrante@polimi.it)
Co-Supervisors Davide Di Febbo (davide.difebbo@polimi.it)
Collaborations Advice Pharma Group S.R.L. http://www.advicepharma.com/
Description
  • Aim:

Identify and analyse the impact of adopting artificial intelligence technologies to support decision making processes in clinical research projects.

  • Project phases:

Starting from the web-based editor of EDC/eCRF solution and Clinical Trial Management System (CTMS) module of the ICE (Integrated Clinical Environment) platform, evaluate the options available to integrate the system with virtual assistants which may facilitate the process database development, management and trial management in general.

  1. Literature research on Artificial intelligence in clinical research
  2. Learning of AI technologies for decision support for Clinical Trial Management Systems (CTMS)
  3. Design and implementation of a control system able to support users with decision support solutions in proprietary software technology in the sector of Clinical Trial Software technologies.
  4. Tests on a pilot study the solution adopted
  5. Data analysis of the results
Thesis Recording neuronal activity from induced pluripotent stem cells using Multi-Electrode Arrays
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors Alice Geminiani (alice.geminiani@polimi.it)
Collaborations Dr. Andrea Menegon – San Raffaele Hospital, Experimental Imaging Center 
Description
  • Aim:

Multi-Electrode Arrays (MEAs) allow recording the network activity from in vitro neuronal cultures in different conditions (e.g. after pharmacological treatment). Traditional acquisitions are performed on hippocampal neurons, but the recent development of biotechnologies for generating induced Pluripotent Stem cells (iPS) opens the possibility to exploit MEAs for characterizing the activity of neuron-like iPS. Within this thesis: validation of a multi-MEA setup for long-term recordings from multiple MEAs in parallel, and acquisitions on neuron-like iPS with the final aim to test new cellular models and suggest treatments for Parkinson Disease.

  • Project phases:

Starting from a prototype for parallel MEA recordings in controlled environmental conditions:

  1. Literature research on MEA systems and iPS.
  2. Validation of the parallel 4-channel recording system with hippocampal neurons.
  3. Design of an experimental protocol for iPS activity recording.
  4. Data analysis of the results.
Thesis Large-scale cerebellar Spiking Neural Networks to simulate sensorimotor paradigms
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors Alberto Antonietti (alberto.antonietti@polimi.it)
Alice Geminiani (alice.geminiani@polimi.it)
Collaborations Prof. Egidio D’Angelo and Claudia CasellatoUniversity of Pavia
EPFL (Lausanne, Switzerland)
                         
Description
  • Aim:

Starting from a scaffold of the cerebellar circuit, to implement a cerebellar Spiking Neural Network in the NEST simulator, embedding detailed single neuron dynamics, plasticity mechanisms and geometry-based connectivity; the network will be exploited to simulate sensorimotor paradigms in physiological and pathological conditions.

  • Project phases:

– Literature research on the main properties of the cerebellar circuit.
– Integration of new properties in the NEST-based cerebellar Spiking Neural Network
Design and analysis of closed-loop simulations of cerebellum-driven protocols.

Thesis Replicability of Computational Neuroscience studies
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors Alberto Antonietti (alberto.antonietti@polimi.it)
Alice Geminiani (alice.geminiani@polimi.it)
Description
  • Aim:

Reproducibility is critical to scientific inquiry, which relies on the independent verification of results. Progress in science also requires that we determine whether conclusions were obtained using a rigorous process, and we must know whether results are robust to small changes in conditions. Computational approaches present unique challenges for these requirements. A foundational research work in the computational neuroscience field, published in the last years, will be replicated by means of up-to-date models, in order to verify the results claimed by the original authors.

  • Project phases:

– Literature study about important neuronscience foundational papers.
– Identification of the target problem to replicate
– Development of the verification pipeline and appropriate codes (e.g. Spiking Neural Networks with NEST simulator)
– Analysis of the results and comparison with the original claims

Thesis Mobile App for Auditory Cueing to reduce Freezing of Gait in patients with Parkinson’s Disease
Supervisor Simona Ferrante (simona.ferrante@polimi.it)
Co-Supervisors Stefano Tolomeo (stefano.tolomeo@polimi.it)
Francesca Lunardini (francesca.lunardini@polimi.it)
Collaborations Dr Giorgio Ferriero, Fondazione Salvatore Maugeri, Istituto di Lissone
Description
  • Aim:

Freezing of gait (FOG) is a debilitating symptom of Parkinson’s disease (PD) and occurs when patients are unable to move their feet when trying to walk.
Auditory Cueing is the process whereby movement is synchronized to (rhythmic) sounds.
Although many studies have reported a positive effect of auditory cueing in reducing FOG in PD patients, the underlying mechanisms are still unclear.
The development of a mobile app that provides PD patients with auditory cues to guide their gait may represent an efficient solution to investigate FOG reduction in everyday life.

  • Project phases:

thanks to the availability of PD patients at the Salvatore Maugeri Foundation:

– Literature research on auditory cueing to reduce FOG in PD patients
– Design, development and validation of Mobile App in clinical and home settings
– Investigate the effect of different auditory cueing strategies in improving FOG in a group of PD patients

Thesis Voice analysis to diagnose neurodegenerative diseases
Supervisor Simona Ferrante (simona.ferrante@polimi.it)
Co-Supervisor Emilia Ambrosini (emilia.ambrosini@polimi.it)
Collaborations Dott. 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. Within this thesis: 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:

– 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)
Design and validation of an algorithm to compute voice signal characteristics (e.g pitch, jitter, pace, glottal closure openings and closing instants, pauses, speech rate, etc.)
Analysis of correlation between voice features and different neurodegenerative disease and between voice features and neurodegenerative biomarkers (magnetic resonance atrophy, cerebrospinal fluid proteins concentration)

Thesis Exergaming for early detection and prevention of dysgraphia in children
Supervisor Simona Ferrante (simona.ferrante@polimi.it)
Co-Supervisor Francesca Lunardini (francesca.lunardini@polimi.it)
Collaborations Prof. Alberto Borghese, AIS Lab, Università degli studi di Milano
Istituto Neurologico C. Besta, Milano
Description
  • Aim:

Dysgraphia is a problem with the writing process probably caused by a dysfunction within the processing system involved with sequencing. Dysgraphia affects the 11% of students. Although it is characterized by well-defined spatial and dynamic features, there is scarcity of diagnostic tools (BHK scale and Denver test). Dysgraphia is usually diagnosed late, around the 2nd or 3rd year of primary school, and the consequent lack of early intervention may have a severe impact on the life of these children. The aim of this thesis is the development of exergames for children may represent a useful solution to further investigate handwriting features in children with dysgraphia, thus providing a diagnostic tool for early detection and intervention.

  • Project phases:

– Additional literature research on dysgraphia and use of exergames in children
– Design and development of exergames
– Test on healthy children and on some children with dysgraphia
– Data Analysis

Thesis Monitoring of patients with vegetative and minimally conscious state diagnosis
Supervisor Simona Ferrante (simona.ferrante@polimi.it)
Co-Supervisor Francesca Lunardini (francesca.lunardini@polimi.it)
Collaborations Istituto Neurologico C. Besta, Milano
Empatica
Description
  • Aim:

Sleep pathologies can be frequently encountered in Vegetative State (VS) and Minimally Conscious State (MCS) patients. Treatments to restore sleep function have the potential to represent an important goal for rehabilitation purposes in these patients.
An ICT system, encompassing inertial and EMG sensors, has been developed to be used in combination with cognitive assessment to:
– test efficacy of tailored interventions for sleep pathologies in patients with Disorders of Consciousness (DOC)
– analyze relationship between sleep patterns of patients with DOC and their cognitive/consciousness level over time

  • Project phases:

-Literature research
-Optimization of the integrated ICT monitoring system
-Data acquisition on patients
-Data analysis and design of algorithms to estimate and detect voluntary/unvoluntary contractions during long term acquisitions

 

Thesis EMG-based grasping classifier for the control of a hand grasping support electromagnetic system
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors Marta Gandolla (marta.gandolla@polimi.it)
Collaborations Villa Beretta Rehabilitation Center
Scientific Institute Eugenio Medea
 
Description
  • Aim:

At the Nearlab, we have developed a hand grasping support system based on an electromagnetic device. The aim of the work is to design and develop an EMG-based controller (MYO armband) to trigger the electromagnetic device support.

  • Project phases:

– Literature research on EMG-based hand control
– Understanding the actual prototype functioning, and the way to go
– Design and implementation of the EMG-based controller
– Usability tests on healthy controls and target patients
– Data analysis of the results

Thesis Graphical user interface (GUI) for the control of motorized upper limb exoskeleton
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors Marta Gandolla (marta.gandolla@polimi.it)
Collaborations Villa Beretta Rehabilitation Center
Scientific Institute Eugenio Medea
Prof. Francesco Braghin, Mechanical Dept., Politecnico di Milano
 
Description
  • Aim:

Severely disabled people, such as muscular dystrophic patients, would greatly benefit from a support in performing daily life activities independently with their own arm under their directly control. At the Nearlab we have developed a first prototype of a fully motorized upper limb 5 DoF exoskeleton. The aim of the project is to develop a graphical user interface (GUI) which is able to guide the operator/caregiver, and the user itself through setting and use of the system.

  • Project phases:

– Literature research on robotic assistive devices
– Understanding the actual prototype functioning, and the way to go
– Design and implementation of a graphical user interface (GUI)
– Usability tests on healthy controls and dystrophic patients
– Data analysis of the results

Thesis Voice-based speech recognition control system for assistive robotics
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisors Marta Gandolla (marta.gandolla@polimi.it)
Stefano Dalla Gasperina (stefano.dallagasperina@polimi.it)
Collaborations Villa Beretta Rehabilitation Center
Scientific Institute Eugenio Medea
Prof. Francesco Braghin, Mechanical Dept., Politecnico di Milano
 
Description
  • Aim:

A huge amount of people worldwide lives with some form of disability limiting their ability to execute activities of daily living. The use of robots as assistive devices provides great potential for these individuals to regain independence and return to being an active part of society. Given their limited motor functionalities, reliable and intuitive human-machine interfaces based on speech recognition algorithm could be used for the control of assistive robotic devices such as exoskeletons, motorized beds, wheelchairs, etc. The aim of the project is to design and develop a standalone voice-controlled interface able to recognize user intentions by means of vocal commands. The system will be implemented on single board computer or embedded system.

  • Project phases:

– Literature research on robotic assistive devices and voice-controlled interfaces
– Preliminary tests on existing speech recognition engines
– Design and implementation of a voice-controlled human-machine interface
– Usability tests on healthy controls and disabled patients
– Integration with existing motorized exoskeleton for upper limb

Thesis Functional electrical stimulation for grasping
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisor Emilia Ambrosini (emilia.ambrosini@polimi.it)
Collaborations Thierry Keller, Tecnalia – San Sebastian (Spain) (thierry.keller@tecnalia.com)
Description
  • Aim:

40% percent of people that suffer a stroke present severe impairments on the upper limb, which affects their quality of life as they are not able to carry out activities of daily living (eating, dressing, cleaning,…). FES has shown several benefits in the neurorehabilitation field with both assistive and therapeutic effects. Surface FES parameters need to be adapted (to each person, to each session,…) and it is critical in the case of the hand due to its neuroanatomical complexity. The aim of the current thesis is the design and development of a closed loop system for adapting stimulation parameters to prono-supination movements of the arm for generating different types of grasps

  • Project phases:

Starting from available inertial sensor based hand movement sensor system and multi-field FES system for the forearm muscles (extrinsic hand muscles):

– Literature research on voluntary and FES-assisted hand grasps and closed-loop control techniques
– Design and execution of data acquisition sessions with healthy subjects
– Design and development of a closed-loop system for adapting the stimulation parameters to pronosupination movements of the forearm
– Validation of the approach in lab tests with healthy subjects

 

Thesis EMG-based vibro-tactile biofeedback training in children with dystonia
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisor Emilia Ambrosini (emilia.ambrosini@polimi.it)
Collaborations Prof. T. Sanger, SangerLab, University of Southern California, Los Angeles
Dott. G. Zorzi, Istituto Neurologico C. Besta, Milano
Dr. Emilia Biffi, Scientific Institute Eugenio Medea, Bosisio Parini
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 cross-over study has been designed in order to evaluate the effects of a wearable EMG-based biofeedback training in improving motor control in children with dystonia. Both children with primary and secondary dystonia have been recruiting in order to test the hypothesis that the failure of motor learning due to sensory deficits is specific for children with secondary dystonia.

  • Project phases:

Starting from an EMG-based vibro-tactile Biofeedback device to create awareness of the activity of individual muscles during motor tasks’ execution, ethical approval for the cross-over study (first patients have already concluded the protocol):

– Literature research on biofeedback training in children with primary and secondary dystonia
– Data collection and analysis
– Statistical analysis
– Interpretation and discussions of the results

Thesis Effects of lower limb training on motor coordination after stroke
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Simona Ferrante (simona.ferrante@polimi.it)
Co-Supervisor Emilia Ambrosini (emilia.ambrosini@polimi.it)
Collaborations Fondazione Salvatore Maugeri, Istituto di Lissone

Muscle synergies during pedaling after stroke
Description
  • Aim:

Motor system seems to rely on a modular organization (muscle synergies activated in time) to execute different biomechanical tasks. Stroke patients exhibit poor inter-muscular coordination (poor timing and merging of modules that are normally independent in healthy subjects) both during locomotion and cycling, which is correlated to the level of the motor impairment. Different rehabilitative programs might have different effects on motor coordination. This study aims at investigating the effects of a lower limb training on modular muscle coordination during cycling in post-acute stroke patients. Specifically, the effects of a cycling training induced by electrical stimulation will be compared with conventional training.

  • Project phases:

Starting point: one article has been recently published in Annals of Biomedical Engineering from our group to better understand the neuro-mechanics of recumbent cycling in stroke patients; normative data on healthy older adults are available.  Next steps:

– Literature research on muscle synergies and motor control
– Data collection on stroke patients
– Data analysis of EMG and force data pre- and post-intervention
– Statistical analysis
– Interpretation and discussions of the results

 

Thesis Lower limb rehabilitation in children with neurological disorders: robotics or virtual reality?
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisor Emilia Ambrosini (emilia.ambrosini@polimi.it)
Emilia Biffi (emilia.biffi@bp.lnf.it)
Collaborations Dr. Emilia Biffi, Scientific Institute Eugenio Medea, Bosisio Parini
Description
  • Aim:

The improvement of walking abilities is a major goal in the rehabilitation of children affected by neurological impairments. Robot-aided rehabilitation supports traditional methods with some potential advantages including movement repeatability. New platforms integrating treadmills, motion capture systems and virtual reality (VR) offer a more engaging environment.  The assessment of  the effectiveness of these technologies and the identification of determinants of responsiveness is fundamental to support the choice of the best therapeutic approach

  • Project phases:

Starting from functional and instrumental data measured before and after robot-aided or VR-aided therapy in children with acquired brain injuries:

– Literature research on methods to evaluate the determinants of responsiveness to a treatment
– Development of algorithms to assess gait pattern
– Assessment of the effectiveness of robot-aided and VR-aided treatment
– Identification of determinants of responsiveness

Thesis Validation of sensor-based assessments of upper-limb function during rehabilitation
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisor Emilia Ambrosini (emilia.ambrosini@polimi.it)
Collaborations Serena Maggioni, Hocoma AG -Zurich (Switzerland) (serena.maggioni@hocoma.com)
ETH Zurich – RELab
Description
  • Aim:

Instrumented assessments of motor function are needed to objectively measure the patient’s progresses and to adjust the therapy accordingly. To be used in clinical practice, the validity and reliability of the assessments needs to be proven.

ArmeoSenso is a sensor-based medical device that provides functional movement therapy for upper extremity rehabilitation and assessments of range of motion, workspace and quality of movement.

The validity and reliability of the  ArmeoSenso assessments needs to be studied in able-bodied subjects and patients with neurological disorders.

  • Project phases:

ArmeoSenso provides continuous off-line joint angles and hand position data during training and during 3 assessments (range of motion, workspace and quality of movement). Next steps include:

– Literature research on methods to evaluate the validity and reliability of assessments
– Application for ethical approval
– Development of protocol to study validity and reliability
– Data collection and analysis

Thesis Development of novel sensor-based assessments of upper-limb function during rehabilitation
Supervisor Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)
Co-Supervisor Emilia Ambrosini (emilia.ambrosini@polimi.it)
Collaborations Serena Maggioni, Hocoma AG -Zurich (Switzerland) (serena.maggioni@hocoma.com)
ETH Zurich – RELab
Description
  • Aim:

Instrumented assessments of motor function are needed to objectively measure the patient’s progresses and to adjust the therapy accordingly.

Assessment of quality of movement and workspace have been proven to be sensitive to recovery.

ArmeoSenso is a sensor-based medical device that provides functional movement therapy for upper extremity rehabilitation and assessments of range of motion, workspace and quality of movement.

Assessments of quality of movement and workspace can provide rich information about recovery, which is currently not entirely exploited.

  • Project phases:

Starting point:

ArmeoSenso provides continuous off-line joint angles and hand position data during training and during 3 assessments (range of motion, workspace and quality of movement). Next steps include:

– Literature research on methods to assess workspace and quality of movement
– Application for ethical approval
– Development of a new concept and metrics to assess workspace and quality of movement
– Data collection on able-bodied subjects and patients and data analysis

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