AVAILABLE MASTER THESIS
Robotics and Neuroprosthetics for Rehabilitation
Thesis | Optimization of an outdoor cycling FES-system for paraplegics |
Supervisor | Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it) |
Co-Supervisors | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Collaborations | Marco Tarabini, Dipartimento di meccanica (https://www.mecc.polimi.it) |
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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:
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Thesis | Design of a hybrid robotic system to support locomotion in Spinal Cord Injury people |
Supervisor | Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it) |
Co-Supervisors | Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it) |
Collaborations | Francesco Braghin, Marta Gandolla (Dipartimento di Meccanica, Politecnico di Milano) |
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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:
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Thesis | Model-based control of a hybrid robotic system for locomotion |
Supervisor | Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it) |
Co-Supervisors | Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it) |
Collaborations | Prof. Francesco Braghin, Prof. Marta Gandolla (Dipartimento di Meccanica, Politecnico di Milano) |
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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:
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Thesis | EMG-based vibro-tactile biofeedback training in children with dystonia |
Supervisor | Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it) |
Co-Supervisors | Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it) |
Collaborations | Terry Sanger, University of California, Irvine |
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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:
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Thesis | Effects of TMS-induced inter-hemispheric inhibition on the recovery of reaching tasks during robotic rehabilitation |
Supervisor | Prof Emilia Ambrosini (emilia.ambrosini@polimi.it) |
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Collaborations | Dr. L. Sebastianelli, Neuroriabilitazione Ospedale di Vipiteno |
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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:
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Thesis | Interactive mirroring games with the social robot NAO for autism therapy |
Supervisor | Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it) |
Co-Supervisors | Alice Geminiani (alice.geminiani@polimi.it) Laura Santos (laura.santos@mail.polimi.it) |
Collaborations | Università dell’Insubria/Fondazione Macchi (prof Cristiano Termine) Provveditorato di Varese (Luigi Macchi, Simonetta Bralia) |
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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:
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Thesis | Effects of a robot-assisted therapy on motor coordination after stroke |
Supervisors | Emilia Ambrosini (emilia.ambrosini@polimi.it) Simona Ferrante (simona.ferrante@polimi.it) |
Collaborations | Ing. Monica Parati, Laboratorio di Bioingegneria, IRCCS Istituti Clinici Scientifici Maugeri, Lissone (MB) |
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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. Project phases: A randomized controlled study investigating the effectiveness of a robot-assisted therapy on stroke patients is currently ongoing. Nest steps:
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Thesis | Does a hybrid robotic system improve motor recovery in stroke survivors? |
Supervisor | Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it) |
Co-Supervisor | Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it) |
Collaborations | RETRAINER consortium |
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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:
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Thesis | Human-Machine Interaction Monitoring using Galvanic Skin Response (GSR) sensors |
Supervisor | Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it) |
Co-Supervisor | Prof. Marta Gandolla (marta.gandolla@polimi.it) Mattia Pesenti (mattia.pesenti@polimi.it) |
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Description | Aim Monitoring the state of humans during the interaction with robots is fundamental to optimize the behavior of the robot and assess the quality of such interaction. This is particularly helpful to evaluate the active participation of patients that undergo rehabilitation using robotic exoskeletons. This master thesis project has the following main objectives:
Expected Project Development After an intensive study of the state of the art, we plan to tackle each objective described above sequentially. Required skills
Basic knowledge (or willingness to learn) of ROS and Git/GitHub are welcome |
Thesis | Machine Learning Algorithms for Exoskeleton Control |
Supervisor | Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it) |
Co-Supervisor | Prof. Marta Gandolla (marta.gandolla@polimi.it) Mattia Pesenti (mattia.pesenti@polimi.it) |
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Description | Aim Industrial exoskeletons are used by workers that perform intense manual material handling (MMH) and/or undergo to prolonged awkward body postures throughout the working day. Exoskeletons are often used to assist such workers, reducing the effort required from musculoskeletal system. Active industrial exoskeletons are still lacking advanced control strategies that can adapt the assistive torque to the intention of the human wearer. The aim of this master thesis is the development of adaptive control algorithms for industrial exoskeletons exploiting machine learning. Expected Project Development After an intensive study of the state of the art, we plan to perform experimental setup design and data acquisition. These data will be used to train and test machine learning algorithms. Required skills
Basic knowledge (or willingness to learn) of Python and Git/GitHub are welcome |
Digital Health
Thesis | Machine Learning for premature babies parenteral nutrition |
Supervisor | Prof Simona Ferrante (simona.ferrante@polimi.it) |
Co-Supervisors | Linda Greta Dui (lindagreta.dui@polimi.it) |
Collaborations | Marco Frontini (Link Up s.r.l.) Valentina Bozzetti (Ospedale San Gerardo, Monza) |
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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:
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Thesis | Monitoring of patients with Parkinson’s disease during walking in free-living and challenging conditions |
Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Co-Supervisors | Milad Malavolti (milad.malavolti@polimi.it) Monica Parati (monica.parati@polimi.it) |
Collaborations | IRCCS Istituti Clinici Scientifici Maugeri, Milano |
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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:
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Thesis | Serious games for specific learning disabilities detection |
Supervisor | Prof Simona Ferrante (simona.ferrante@polimi.it) |
Co-Supervisors | Linda Greta Dui (lindagreta.dui@polimi.it) |
Collaborations | Prof Termine, (Università dell’Insubria/Fondazione Macchi) |
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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:
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Thesis | IoT smart ink pen for early detection and monitoring of patients with mild cognitive impairments and dementia |
Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Co-Supervisors | Francesca Lunardini (francesca.lunardini@polimi.it) |
Collaborations | Carlo Abbate IRCCS Fondazione Don Carlo Gnocchi ONLUS |
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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:
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Thesis | IoT smart ink pen for early detection and monitoring of patients with Parkinson’s disease |
Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Co-Supervisors | Francesca Lunardini (francesca.lunardini@polimi.it) Monica Parati (monica.parati@polimi.it) |
Collaborations | IRCCS Istituti Clinici Scientifici Maugeri, Milano |
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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:
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Thesis | IoT smart ink pen for longitudinal monitoring of daily-life handwriting |
Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) Prof. Matteo Matteucci (matteo.matteucci@polimi.it) |
Co-Supervisors | Francesca Lunardini (francesca.lunardini@polimi.it) Davide Di Febbo (davide.difebbo@polimi.it) |
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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:
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Thesis | Companion interface for an IoT smart ink pen |
Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Co-Supervisors | Milad Malavolti (milad.malavolti@polimi.it) |
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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:
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Thesis | The DYSPA System: a novel neuro-motor assessment to quantify dystonia and spasticity in children with movement disorders |
Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Co-Supervisors | Francesca Lunardini (francesca.lunardini@polimi.it) |
Collaborations | Dott. Giovanna Zorzi, Dott. Davide Rossi, Fondazione IRRCS Istituto Neurologico Carlo Besta |
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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:
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Thesis | Mobile app for monitoring emotion valence and cognitive decline from voice signals |
Supervisor | Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it) |
Co-Supervisors | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Collaborations | SignalGeneriX (Limassol, Cyprus) |
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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:
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Thesis | IoT smart ink pen for handwriting longitudinal monitoring in children |
Supervisor | Prof Simona Ferrante (simona.ferrante@polimi.it) Prof Matteo Matteucci (matteo.matteucci@polimi.it) |
Co-Supervisors | Linda Greta Dui (lindagreta.dui@polimi.it) |
Collaborations | Università dell’Insubria/Fondazione Macchi (prof Cristiano Termine) Provveditorato di Varese (Luigi Macchi, Simonetta Bralia) |
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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. Project phases:
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Thesis | Computer Vision and IoT to detect grasping difficulties |
Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) Prof. Matteo Matteucci (matteo.matteucci@polimi.it) |
Co-Supervisors | Linda Greta Dui (lindagreta.dui@polimi.it) |
Collaborations | Università dell’Insubria/Fondazione Macchi (prof Cristiano Termine) Provveditorato di Varese (Luigi Macchi, Simonetta Bralia) |
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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. Project phases:
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Thesis | Voice analysis to diagnose neurodegenerative diseases |
Supervisor | Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it) |
Co-Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Collaborations | Dott. Andrea Arighi, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico |
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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 Requirements:
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Thesis | Serious games for hand function rehabilitation |
Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Co-Supervisor | Francesca Lunardini (francesca.lunardini@polimi.it) |
Collaborations | AISLab – Università degli Studi di Milano Clinical partners |
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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 Requirements:
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Thesis | Monitoring of patients with vegetative and minimally conscious state diagnosis |
Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Co-Supervisor | Francesca Lunardini (francesca.lunardini@polimi.it) Milad Malavolti (milad.malavolti@polimi.it) |
Collaborations | Fondazione Istituto Neurologico C. Besta – Milano Fondazione IRCCS San Raffaele Centro di Riabilitazione Villa Beretta – Ospedale Valduce Empatica |
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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 |
Thesis | Eye tracking for action observation treatment in neurological patients |
Supervisor | Prof. Simona Ferrante (simona.ferrante@polimi.it) |
Co-Supervisor | Francesca Lunardini (francesca.lunardini@polimi.it) |
Collaborations | Dr. Davide Sebastiano Rossi, Fondazione Istituto Neurologico C. Besta, Milano Prof. Giovanni Buccino, Università Vita-Salute San Raffaele and Division of Neuroscience |
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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 |
Thesis | Machine Learning for digital Dysgraphia diagnosis |
Supervisor | Simona Ferrante (simona.ferrante@polimi.it) Matteo Matteucci (matteo.matteucci@polimi.it) |
Co-Supervisor | Linda Greta Dui (lindagreta.dui@polimi.it) Francesca Lunardini (francesca.lunardini@polimi.it) |
Collaborations | Prof. Cristiano Termine, Università degli Studi dell’Insubria and Fondazione Macchi |
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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 Requirements:
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Thesis | Machine Learning to longitudinally monitor graphical abilities, towards the early diagnosis of Dysgraphia |
Supervisor | Simona Ferrante (simona.ferrante@polimi.it) Matteo Matteucci (matteo.matteucci@polimi.it) |
Co-Supervisor | Linda Greta Dui (lindagreta.dui@polimi.it) Francesca Lunardini (francesca.lunardini@polimi.it) |
Collaborations | Prof. Cristiano Termine, Università degli Studi dell’Insubria and Fondazione Macchi Dott. Luigi Macchi, Dott.ssa Simonetta Bralia, Provveditorato, CTI, CTS di Varese |
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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 Requirements:
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Computational Neuroscience
Thesis | Large-scale cerebellar Spiking Neural Networks to simulate sensorimotor paradigms in a virtual robotic environment. |
Supervisor | Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it) |
Co-Supervisors | Alessandra Trapani (alessandramaria.trapani@polimi.it) Massimo Grillo (massimo.grillo@polimi.it) Francesco Sheiban (francescojamal.sheiban@mail.polimi.it) |
Collaborations | Prof. Egidio D’Angelo and Claudia Casellato, Università di Pavia |
Description | Aim: IN PROGRESS Project phases:
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Thesis | Neural Networks for the Decoding of Neural Signals from Behaving Monkeys |
Supervisor | Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it) Silvestro Micera (silvestro.micera@santannapisa.it) |
Collaborations | Patrizia 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:
This thesis foresees a development part at Scuola Superiore Sant’Anna in Pisa. |
Thesis | Wavelet-based Analysis of Neural Population Dynamics in Reaching Tasks |
Supervisor | Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it) Silvestro Micera (silvestro.micera@santannapisa.it) |
Collaborations | Patrizia 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 This thesis foresees a development part at Scuola Superiore Sant’Anna in Pisa. |