Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)

Prof. Marta Gandolla (marta.gandolla@polimi.it)

Valeria Longatelli (valeria.longatelli@polimi.it)

Villa Beretta Rehabilitation Institute

Aim 

Robotic exoskeletons are nowadays widely used in rehabilitation settings to enhance beneficial neuroplasticity that may accelerate functional recovery and restore healthy gait after stroke. However, the available scientific evidence demonstrates their effectiveness comparing standard clinical scales before and after the rehabilitative intervention (i.e., training effect). This project aims to investigate the immediate effect (i.e., orthotic effect) in walking abilities of 15 subacute post-stroke patients that used Ekso Bionics through the analysis of the EMG signal of lower limb muscles.

Expected Project Development 

• Literature review
• Algorithm development
• Data analysis
• Discussion of obtained data 

Required skills 

• Knowledge in MATLAB

Development of a benchmark framework for upper limb rehabilitation technologies

Villa Beretta Rehabilitation Institute

Over the past few decades, robotic technologies have played a crucial role in basic research, expanding our knowledge of sensory and motor function, and have shown potential for rehabilitation therapy. These successes are a result of this technology’s ability to objectively, repeatedly, and reliably measure behavior.

Aim 

The purpose of this work is to use robotic technology to examine motor functionality of the upper limbs in a 3D space in healthy individuals. In particular, a standardized and replicable protocol will be defined, and proper sensors set (e.g., XSENS, EMG reader) will be used to measure the performed movement. Then, the work will involve the development of an algorithm to characterize the upper limb normative profile (e.g., arm coordination, energetic index, normative trajectories, and strategies). A virtual environment to drive the user during the execution of the protocol could be developed in order to increase the user’s engagement. This benchmarking framework could then be translated to the clinical setting to evaluate patients’ performances.

Expected Project Development 

• Literature review
• Experimental protocol and set-up definition
• Data acquisition and Algorithm development
• Data analysis
• Discussion of obtained data towards benchmarking framework

Required skills 

• Knowledge in MATLAB
• Interest in development of software/firmware 

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

Prof. Alessandra Pedrocchi (alessandra.pedrocchi@polimi.it)

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.

Aim:

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.

Aim:

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

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

Project phases:

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

Requirements:

• Knowledge of Matlab
• Interest in HW and SW development

Aim:

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

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

Project phases:

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

Requirements:

• Knowledge of Matlab
• Experience in programming (C++)

Aim:

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

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

Project phases:

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

Requirements:

• Knowledge of Matlab
• Experience in programming

Aim:

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

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

Project phases:

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

Requirements:

• Knowledge of Matlab

Aim:

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

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

Project phases:

• Literature review
• Data analysis
• Statistical analysis

Requirements:

• Basic knowledge of Matlab

Aim:

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

Project phases:

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

Requirements:

• Basic knowledge on robotics and motion tracking systems.
• Good programming skills in Python and MATLAB are a plus
• Motivation to take part in clinical acquisitions and interact with therapists – italian language mother tongue/proficiency

Aim:

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

Project phases:

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

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

Requirements:

• Knowledge of Matlab
• Availability to participate to data collection

Aim:

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

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

Project phases:

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

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

Requirements:

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

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: 

1. Understanding how the Galvanic Skin Response (GSR) signal is affected during some daily-life tasks because of the level of stress, attention, and overall mental state. 
2. Monitor such changes during the interaction between human subjects and a robotic device. 
3. Develop an algorithm able to compute a quantitative score of attention. This algorithm may be embedded into a robotic exoskeleton for rehabilitation. 

Expected Project Development 

After an intensive study of the state of the art, we plan to tackle each objective described above sequentially. 

Required skills 

• Proficiency in MATLAB and embedded C/C++ 
• Basic knowledge of statistics 

Basic knowledge (or willingness to learn) of ROS and Git/GitHub are welcome 

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 

• Proficiency in MATLAB and C/C++ 
• Knowledge of machine learning 
• Interest in development of software/firmware 

Basic knowledge (or willingness to learn) of Python and Git/GitHub are welcome