Bionics engineering

Università degli Studi di Pisa
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  • Pisa
Descrizione

Bionics engineering is a new frontier of biomedical engineering. Indeed, "bionics" is increasingly used at international level to indicate the research area which integrates the most advanced robotics and bioengineering technologies with life sciences, such as medicine and neuroscience, materials science, etc., with the ultimate goal of inventing and deploying a new generation of biomimetic machines, human-centred healthcare and (more generally) assistive technologies. Although some of them will replace traditional biomedical technologies, bionic solutions will be in general complementary and synergic to state-of-the-art biomedical engineering efforts.

One of the primary goals of this master degree course is to challenge a selected core of very highly qualified students that, besides acquiring high-level professional skills, will also foster the progress of the research activities in the bionics field. They will be able to close the innovation loop by both translating the knowledge across application scenarios and transferring scientific insights into market opportunities. In the progress of their studies, graduate students of the Master of Science in Bionics Engineering will gather fundamentals of science and technology of biorobotics and neural engineering. They will be also progressively trained to a multi-disciplinary research attitude by means of a fruitful dialogue with scientists from different research fields, such as medicine, biology, neuroscience, as well as with clinicians in the field of rehabilitation and surgery, pioneers of emerging industrials sectors and social scientists. Eventually, students of Bionics Engineering will enrich their background with specific skills in the following engineering domains: mechatronics, robotics, biomedical robotics, telerobotics, biomimetics and bio-inspired design of robotic platforms, neuroprostheses, wearable and...

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Inizio Luogo
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Pisa
Lungarno Antonio Pacinotti, 43 , 56122, Pisa, Italia
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Programma

  • Neural Engineering
  • Biorobotics
Neural Engineering
  • Materials and instrumentation for bionics engineering (12 cfu)

    • The course “Materials and instrumentation for bionics engineering” is composed of two modules: “Instrumentation and measurement for bionic systems” and “Soft and smart materials”.
      Instrumentation and measurement for bionic systems introduces to the methods and technologies involved in the development of equipment for measuring physical and electrical variables during monitoring and control of bionic systems. The students will be exposed to a system-oriented approach to the theory and practice of bionic measurement systems, cutting across several disciplines, including electronics, systems theory, digital signal processing, statistics and artificial intelligence.
      Soft and smart materials aims at providing an advanced knowledge on novel soft and smart materials for bionics. Different technologies will be analysed from the basic principles to their exploitation as smart sensors or actuators. The course will enable the student to implement a comparative analysis for the choice of the most suitable technologies for specific engineering problems. The student will be asked to use advanced design principles and tools (like CAD and FEM) as well as to carry out hands-on lab activities.
  • Biomechanics of human motion (6 cfu)

    • The objectives of this course are to provide an introduction to the biomechanics of the human movements and then to understand the main role underlying the control of spatial multiple degree-of-freedom human motion. These objectives will be reached by means of both theoretical lessons and practical activities in a lab of human movement analysis.
  • Bioinspired computational methods (12 cfu)

    • The course aims to introduce the main concepts and techniques used in bioinspired computational methods. The course is divided in two modules “Neural and fuzzy computation” and “Biological data mining”. The first module intends to offer students the opportunity to learn the basic concepts and models of computational intelligence, to have a thorough understanding of the associated computational techniques, such as artificial neural networks, fuzzy systems and genetic algorithms, and to know how to apply them to a wide variety of application areas. The second module will focus on the basic aspects of biological data mining: data pre-processing, frequent pattern mining, classification, prediction, clustering and outlier detection.
  • Applied brain science (12 cfu)

    • This course is divided in two modules “Behavioral and cognitive Neuroscience” and “Computational neuroscience”
      In the class “Behavioral and cognitive neuroscience” the student will learn the following topics: the neurobiological correlates of human behavior and cognition; genetic factors that affect behaviour; brain mechanisms that modulate social behaviour such as emotion and aggression; the neural bases of free will; abnormal expressions of aggressive behaviour;the mental representation of the external world; the functional neuroanatomy of perception; mental representation in the absence of visual experience; pharmacological and non-pharmacological modulation of brain activity; brain enhancement; the in vivo examination of the cerebral correlates of mental function in humans; decoding neural activity: implications for the development of brain-computer interfaces.
      The objectives of “Computational neuroscience” class include architectures and learning methods for dynamical/recurrent neural networks and their properties analysis, bio-inspired neural modelling, spiking neural networks, the role of synaptic delays in a computational brain, the role of astrocytes in a computational brain, neuron-astrocyte networks, the role of computational neuroscience in robotics applications.
  • Advanced signal processing (6 cfu)

    • The course will cover advanced signal processing methods, with application to bioengineering field. The students will become familiar with discrete time signals representation, as well as time-frequency analysis, uncovering topics as basis functions and model dimensionality choices. Multivariate analysis topics as PCA and ICA will be introduced and discussed. Estimation methods in signal processing will be addressed, as maximum likelihood and least squares, with emphasis on prediction and filtering. An introduction to Bayesian framework for signal analysis will be given. Advanced topics in parametric and non parametric method for spectral estimation will be introduced gaining examples from biomedical applications. Finally, the course will cover main issues in adaptive filtering, with a special focus on Kalman and extended Kalman filters as well as recursive least squares approaches.
  • 12 cfu a scelta nel gruppo Free choice

    • List of classes that the student chooses freely. These classes will be automatically approved by the board of the Master Degree Course
    • Economic assessment of medical technologies and robotics for healthcare (6 cfu)

      • The course will provide the rationale and the technical tools for assessing the economic, social, usability, and acceptability dimensions of a new medical technology. The methodologies gained will enable students to assess a new medical technology both during the R&D process and in the pre-marketing phase, increasing the probability of its successful transfer and adoption in the market. A special focus will be devoted to robotics for healthcare.
    • Electronics for neural activity monitoring (6 cfu)

      • The student who successfully completes the course will be able to design and analyse analog electronic devices and circuits for neural activity monitoring. He/she will be able to demonstrate a solid knowledge of both basic and advanced integrated devices and electronic circuits used for neuronal activity monitoring, such as field effect transistors, differential, instrumentation and isolation amplifiers, passive and active analog filters. The course covers physics and operation of field effect transistors, as well as design and analysis of differential, instrumentation and isolation amplifiers, passive and active analog filters.
    • Neuromorphic engineering (6 cfu)

      • The course will explore computational and physical models that emulate the neural dynamics of biological neurons of peripheral and central nervous system. A particular focus will be dedicated to real-time implementation of spiking artefacts that could be integrated in neurophysiological studies and in closed loop hybrid-bionic systems to restore missing sensorimotor functions.
    • Principles of bionics engineering (6 cfu)

      • The course will introduce attendants to biological methods and systems found in nature which can be used as source of inspiration to study and design advanced engineering systems, technologies and algorithms. Examples will include: human and animal locomotion, biomechanics, sensorimotor control hypotheses and systems. The course will devote special attention to biomechatronic and biorobotic components and systems.
  • Social robotics and affective computing (12 cfu)

    • The course is conposed of two modules “Social robotics” and “Affective computing”
      Social robotics is a fairly recent branch of robotics; it addresses the need for robots to correctly interpret people’s action and respond appropriately. A cross point of several disciplines, such us psychology, engineering, sociology, social robotics development will be addressed in this course, with arguments mostly based on engineering reasoning and design.
      The course of Affective computing aims at showing how computational technology can be used to understand and interpret human emotions. Specifically, modelling of human emotional expression will be addressed, including software and hardware solutions to acquire, communicate, and express affective information. Understanding how emotions can be experienced can be also helpful to quantify correlated patterns of central and autonomic nervous activity in order to investigate on mood and consciousness disorders.
  • Integrative cerebral function and image processing (12 cfu)

    • The course is divided in two modules:
      Integrative cerebral functions – All cognitive and emotional functions are the by-product of the activity of anatomo-functional distributed and, at the same time, integrated networks. The didactic module entitled "Integrative cerebral functions" will address the following main topics: 1) Node and rich-clubs in the human connectome; 2) Sleep, mentation and dreaming; 3) Biological bases of consciousness; 4) Theory of mind and mirror neuron system; 4) Empathy in the emotional context; 5) Stress in the context of body and mind integration
      Advanced image processing - This module will cover advanced image processing methods that can be applied to biomedical images of the brain. In particular, the methods used to study structural and functional connectivity, as well as brain metabolism, will be deeply covered. The students will be trained to process images acquired using different neuroimaging techniques, as those based on MRI, PET and NIRS. The course will also introduce the main approaches for the integration of biomedical images and electrophysiological recordings.
  • Bionic senses (6 cfu)

    • The course “Bionic senses” refers to engineering artificial sensing and perceptual systems through biological principles to implement neuroprostheses to restore lost functions, for human augmentation and bioinspired perceptional machines. The basis of needed methodology and technology will be given tutorially.
  • Neural prostheses (12 cfu)

    • The course on “Neural prostheses” is composed of two modules: “Neural interfaces and bioelectronic medicine” and “Neural tissue engineering”.

      During the course on “Neural interfaces and bioelectronic medicine” the students will acquire the basic principles underlying the design and development of implantable neural interfaces for different parts of the nervous system. They will also develop a broad view on existing neuroprosthetic systems to restore motor functions and on novel solutions based on the stimulation of the autonomic nervous system, and will be able to identify current limitations and challenges for future applications. Finally, the students will learn the conceptual and practical bases for the development of a novel neuroprosthesis (group project).

      During the course on “Neural tissue engineering” the students will acquire the strategies to develop grafts and scaffolds that can be implanted to promote nerve regeneration and to repair damage caused to nerves of both the central nervous system and peripheral nervous system by an injury and to eliminate inflammation and fibrosis upon implantation. Specifically, the technological processes and the materials necessary to realise these grafts and also their interaction with physiological neural tissue.
  • Lab Training (3 cfu)

    • This activity will consist of Lab training that the student will perform in dedicated facilities and laboratories, with the aim to increase his/her experience in laboratory practice.
  • Final examination (15 cfu)

    • The final examination involves the preparation of a report led to a design or research activity, and in its presentation and discussion.

  • Biorobotics
  • Materials and instrumentation for bionics engineering (12 cfu)

    • The course “Materials and instrumentation for bionics engineering” is composed of two modules: “Instrumentation and measurement for bionic systems” and “Soft and smart materials”.
      Instrumentation and measurement for bionic systems introduces to the methods and technologies involved in the development of equipment for measuring physical and electrical variables during monitoring and control of bionic systems. The students will be exposed to a system-oriented approach to the theory and practice of bionic measurement systems, cutting across several disciplines, including electronics, systems theory, digital signal processing, statistics and artificial intelligence.
      Soft and smart materials aims at providing an advanced knowledge on novel soft and smart materials for bionics. Different technologies will be analysed from the basic principles to their exploitation as smart sensors or actuators. The course will enable the student to implement a comparative analysis for the choice of the most suitable technologies for specific engineering problems. The student will be asked to use advanced design principles and tools (like CAD and FEM) as well as to carry out hands-on lab activities.
  • Advanced signal processing (6 cfu)

    • The course will cover advanced signal processing methods, with application to bioengineering field. The students will become familiar with discrete time signals representation, as well as time-frequency analysis, uncovering topics as basis functions and model dimensionality choices. Multivariate analysis topics as PCA and ICA will be introduced and discussed. Estimation methods in signal processing will be addressed, as maximum likelihood and least squares, with emphasis on prediction and filtering. An introduction to Bayesian framework for signal analysis will be given. Advanced topics in parametric and non parametric method for spectral estimation will be introduced gaining examples from biomedical applications. Finally, the course will cover main issues in adaptive filtering, with a special focus on Kalman and extended Kalman filters as well as recursive least squares approaches.
  • Bioinspired computational methods (12 cfu)

    • The course aims to introduce the main concepts and techniques used in bioinspired computational methods. The course is divided in two modules “Neural and fuzzy computation” and “Biological data mining”. The first module intends to offer students the opportunity to learn the basic concepts and models of computational intelligence, to have a thorough understanding of the associated computational techniques, such as artificial neural networks, fuzzy systems and genetic algorithms, and to know how to apply them to a wide variety of application areas. The second module will focus on the basic aspects of biological data mining: data pre-processing, frequent pattern mining, classification, prediction, clustering and outlier detection.
  • Applied brain science (12 cfu)

    • This course is divided in two modules “Behavioral and cognitive Neuroscience” and “Computational neuroscience”
      In the class “Behavioral and cognitive neuroscience” the student will learn the following topics: the neurobiological correlates of human behavior and cognition; genetic factors that affect behaviour; brain mechanisms that modulate social behaviour such as emotion and aggression; the neural bases of free will; abnormal expressions of aggressive behaviour;the mental representation of the external world; the functional neuroanatomy of perception; mental representation in the absence of visual experience; pharmacological and non-pharmacological modulation of brain activity; brain enhancement; the in vivo examination of the cerebral correlates of mental function in humans; decoding neural activity: implications for the development of brain-computer interfaces.
      The objectives of “Computational neuroscience” class include architectures and learning methods for dynamical/recurrent neural networks and their properties analysis, bio-inspired neural modelling, spiking neural networks, the role of synaptic delays in a computational brain, the role of astrocytes in a computational brain, neuron-astrocyte networks, the role of computational neuroscience in robotics applications.
  • Biomechanics of human motion (6 cfu)

    • The objectives of this course are to provide an introduction to the biomechanics of the human movements and then to understand the main role underlying the control of spatial multiple degree-of-freedom human motion. These objectives will be reached by means of both theoretical lessons and practical activities in a lab of human movement analysis.
  • 12 cfu a scelta nel gruppo Free choice

    • List of classes that the student chooses freely. These classes will be automatically approved by the board of the Master Degree Course
    • Economic assessment of medical technologies and robotics for healthcare (6 cfu)

      • The course will provide the rationale and the technical tools for assessing the economic, social, usability, and acceptability dimensions of a new medical technology. The methodologies gained will enable students to assess a new medical technology both during the R&D process and in the pre-marketing phase, increasing the probability of its successful transfer and adoption in the market. A special focus will be devoted to robotics for healthcare.
    • Electronics for neural activity monitoring (6 cfu)

      • The student who successfully completes the course will be able to design and analyse analog electronic devices and circuits for neural activity monitoring. He/she will be able to demonstrate a solid knowledge of both basic and advanced integrated devices and electronic circuits used for neuronal activity monitoring, such as field effect transistors, differential, instrumentation and isolation amplifiers, passive and active analog filters. The course covers physics and operation of field effect transistors, as well as design and analysis of differential, instrumentation and isolation amplifiers, passive and active analog filters.
    • Neuromorphic engineering (6 cfu)

      • The course will explore computational and physical models that emulate the neural dynamics of biological neurons of peripheral and central nervous system. A particular focus will be dedicated to real-time implementation of spiking artefacts that could be integrated in neurophysiological studies and in closed loop hybrid-bionic systems to restore missing sensorimotor functions.
    • Principles of bionics engineering (6 cfu)

      • The course will introduce attendants to biological methods and systems found in nature which can be used as source of inspiration to study and design advanced engineering systems, technologies and algorithms. Examples will include: human and animal locomotion, biomechanics, sensorimotor control hypotheses and systems. The course will devote special attention to biomechatronic and biorobotic components and systems.
  • Human and animal models in biorobotics (6 cfu)

    • The course focuses on bioinspired robotics and biorobotic platforms for neuroscience and biology. The course provides the knowledge about the models of the human brain, of human intelligence, of...