Virtual reality Exergames for an Upper limb rehabilitation robot.

Beatrice Luciani (beatrice.luciani@polimi.it)

Aim 

Development and test of virtual-reality exercises and games (Exergames) to be integrated into the rehabilitation sessions of a robotic exoskeleton for the upper limb. This thesis aims to create some interactive exercises in a virtual reality environment, taking into account he kinematical model of an already- existing exoskeleton for upper limb rehabilitation.

Upper-limb exoskeletons are nowadays widely used in the rehabilitation process of people after neurological injuries to restore arm functionality. It has been proved that rehabilitation treatments need to challenge and engage the patients to be effective. Virtual reality games can be useful to involve the patient during the execution of therapy exercises, making his/her upper limb move in a virtual environment and interact with virtual objects, keeping his/her level of attention high.

This work aims to develop virtual reality exercises using the cross-platform game engine Unity and to integrate them with the kinematic model of the AGREE exoskeleton, an upper limb 4 Degrees-of-freedom exoskeleton developed by the Politecnico di Milano. Some Oculus system could be integrated too, to improve the interaction between the games in virtual reality and the motion of the exoskeleton.

All models, methods, and algorithms will be implemented within the Robot Operating System (ROS) middleware (https://www.ros.org/) and the Unity system (https://unity.com/ ).

Requirements:

• Basic knowledge of Robotics
• Good knowledge of C/C++ and MATLAB
• Interest in learning ROS/Unity

Optimal path planning generation for rehabilitation upper-limb exoskeletons

Valeria Longatelli (valeria.longatelli@polimi.it) 

Aim 

Development and test of path planning strategies for upper-limb exoskeletons during rehabilitation exercises. This thesis aims to develop a framework to obtain an automatically generated safe, physiological, joint-space trajectory for upper-limb exoskeletons during rehabilitation exercises.

Upper-limb exoskeletons are nowadays widely used in the rehabilitation process of people after neurological injuries to restore arm functionality. A fundamental step towards an effective rehabilitation treatment is the definition of physiological trajectories. 

Inter-joint coordination, range of motion exploration, collision avoidance, uncomfortable configuration avoidance, and minimum-jerk motion are key features for generating such physiological trajectories during rehabilitation treatment. Furthermore, since most of the tasks require the fulfilment of a large variety of concurring constraints, the kinematic redundancy of the robot can be exploited to develop path planning algorithms able to solve high-complexity trajectories for high-dimensional configuration spaces. 

This work aims to develop a path planning algorithm and integrate and validate it in the AGREE exoskeleton, an upper limb 4 Degrees-of-freedom exoskeleton developed by the Politecnico di Milano. All models, methods, and algorithms will be implemented within the Robot Operating System (ROS) middleware (https://www.ros.org/) and the MoveIt framework (https://moveit.ros.org/).

Requirements:

• Basic knowledge of Robotics
• Good knowledge of C/C++ and MATLAB
• Interest in learning ROS/MoveIt

Aim 

Wearable sensors are becoming increasingly popular for complementing classical clinical assessments of gait deficits. They provide several gait features continuously during walking. The 6-minute walk test (6MWT) is a simple, non-invasive, standardized and reproducible test to assess exercise capacity. Monitoring gait during the 6MWT offers a unique opportunity to investigate the dynamic changes that occur over the entire walk. The aim of this work is to assess gait features and evaluate fatigability in patients with neuromuscular disorder performing 6MWT.

Expected Project Development 

• Development of algorithms to derive gait features from Gsensor data
• Identification of methods to assess gait fatigability
• Data analysis

Required skills 

• Knowledge of Matlab
• Interest in machine learning

Aim 

Spoken language has a very strong sequential and rhythmic component. Several studies show that there is a neurobiological link between the ability to keep pace and that of coding the sounds of spoken language, with significant consequences on linguistic understanding and reading skills. Simple rhythmic-motor imitation tasks can provide a reliable assessment of rhythmic skills, highlighting potential deficits in auditory information processing in preschool children. The aim of this work is to develop and validate a device that allows the assessment (and improvement) of motor imitation skills and processing of rhythmic sequences.

Expected Project Development 

• Improvement of hardware for data collection
• Development of software to assess motor imitation skills
• Development of game(s) to train rhythmic and musical abilities
• Pilot test of the device

Required skills 

• Knowledge of Matlab
• Knowledge of hardware design and development
• Interest in app development

Aim 

Wearable sensors can monitor patients performance continuously during daily life activities. Simple gait assessment can support the evaluation of recovery after rehabilitation or surgical intervention or the definition of personalized treatment. The aim of this work is to develop and validate active insoles that can provide an online evaluation of  gait features and provide alarms when asymmetric gait is detected.

Expected Project Development 

• Standardization of insoles production
• Development of hardware for data collection
• Development of an app for real time monitoring
• Pilot test of the device

Required skills 

• Knowledge of hardware design and development
• Interest in 3D printing and app development

Aim 

Robot-assisted treadmill training is an established intervention used to improve walking ability in patients with neurological disorders. Usually, studies investigate biomechanical engagement during training and assess the efficacy of the intervention. On the other hand, the mental state and wellbeing is rarely considered but is a key factor in successful rehabilitation. New wearable devices, such as smartwatches, are able to continuously record physiological data during activities. The aim of this work is to assess the mental states of children during robot-based rehabilitation.

Expected Project Development 

• Data collection from E4 (Empatica)
• Development of algorithms to derive mental states from available sensors and correlate with psychological questionnaires
• Data analysis

Required skills 

• Knowledge of Matlab
• Interest in machine learning

Aim 

Task-oriented training, mainly when combined with Functional Electrical Stimulation (FES), induces motor relearning after neurological disorders. Currently, wearable devices are becoming available. The main advantages of these devices are the customization on each single user and the easiness of wear for a non-expert user, therefore they are good candidate for home rehabilitation. In this project, the feasibility to use TESLASUIT for task-oriented training of neurological patients will be evaluated. TESLASUIT is a a smart full-body suit, integrating 14 inertial sensors for motion capture and 80 electrodes for muscle stimulation. Exercises for walking, cycling and upper limb training with the TESLASUIT will be developed and preliminary tested on healthy subjects.

Expected Project Development 

• Literature review
• definition of the exercises
• definition and implementation of the stimulation strategy to perform the exercise
• feasibility tests

Required skills 

• experience in programming (Unity 3D)
• Matlab

Aim 

Robot-assisted therapy and Functional Electrical Stimulation (FES) are often combined to enhance the outcomes of motor rehabilitation following neurological disorders. Hybrid devices are characterized by three interacting processes involving: (i) the action of the rehabilitation robot, (ii) the FES-induced muscular activity and (iii) the subject voluntary activity. The aim of this project is to optimize an already existing test-bench hybrid robotic platform with one degree of freedom (elbow flex-extension). In particular, the project foresees the integration of the EMG measurements within the platform and the development of a filter to estimate in real-time the voluntary contribution from the same stimulated muscle.

Expected Project Development 

• Literature review
• integration of the EMG measurements in the C++ code
• development of a filter to estimate the volitional contribution during hybrid muscle contractions
• extension of the existing control system
• feasibility tests with healthy subjects

Required skills 

• experience in programming (C++)

Aim 

Robot-assisted therapy and Functional Electrical Stimulation (FES) are often combined to enhance the outcomes of motor rehabilitation following neurological disorders. Within this framework, the actual challenge is to design cooperative control systems which can, on one side, maximize the therapeutic outcomes of FES and, on the other side, minimize the motor torque. The aim of this work is to integrate FES with a torque-based impedance controller during knee flex-extension movements and to adapt the controller parameters in order to optimize the trajectory tracking while minimizing the motor torque. Tests will be carried out in a test-bench and will preliminarily involve healthy subjects.

Expected Project Development 

• Literature review
• design of the FES control system
• integration in the test-bench
• feasibility tests with healthy subjects

Required skills 

• experience in programming (C++)

Aim 

Within the project FESleg, we are developing two systems based on Functional Electrical Stimulation (FES) in order to promote training and sport activities in people with Spinal Cord Injury (SCI). In this project, we would like to evaluate the usability of these systems in a real scenario. In particular, we would like to compare the usability of the POLIMI recumbent trike with respect to a commercial system (BerkelBike) with similar functionalities. Furthermore, we would like to evaluate the usability of an exoskeleton (TWIN-IIT) combined with FES, both from the user and the informal care-giver prospective. Usability tests will be carried out on a group of 5-10 people with SCI.

Expected Project Development 

• Literature review
• definition of the protocol and selection of the outcome measures
• preparation of the document for the ethical committee
• usability tests with patients

Required skills 

• Knowledge of Matlab

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 Injured (SCI) people. The crucial element of these systems is their control strategy: this should be cooperative, meaning that each system can “sense” the other one. Within the project FESleg, in collaboration with INAIL, this work aims to design a cooperative control strategy of the TWIN exoskeleton for walking 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. The possibility to use sensorized insoles to detect the gait phases and assess the weight distribution will be also investigated.

Expected Project Development 

• Literature review
• Acquisition of gait data in combination with the exoskeleton
• Design of the control system
• Validation tests of healthy subjects and people with SCI

Required skills 

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

Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it)

Elena Bardi(elena.bardi@polimi.it)

Aim 

Soft exoskeletons, being made of fabric, are intrinsically compliant, lightweight and their cost is reduced with respect to traditional stiff exoskeletons. Thanks to these unique characteristics, soft exoskeletons can augment the user’s capabilities without constraining the physiological movements and are good candidates to be used in daily life activities.

The aim of the project is to develop and validate an active gravity compensation control strategy for an upper limb exosuit. Thanks to the use of IMUs, the torque profiles to be applied at the joints to compensate the effect of gravity will be generated. These profiles will be then exploited in an admittance control to implement active gravity compensation.

Expected Project Development 

• Literature review
• IMUs integration in test bench
• Active gravity compensation strategy development and validation 
• Payload estimation from IMUs

Required skills 

• C++ language

Prof. Emilia Ambrosini (emilia.ambrosini@polimi.it)

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

Elena Bardi (elena.bardi@polimi.it)

Dipartimento di Meccanica, Politecnico di Milano (DMECC)

Aim 

Soft exoskeletons, being made of fabric, are intrinsically compliant, lightweight and their cost is reduced with respect to traditional stiff exoskeletons. Thanks to these unique characteristics, soft exoskeletons can augment the user’s capabilities without constraining the physiological movements and are good candidates to be used in daily life activities.  
The aim of this project is that of improving the performance of an admittance controller for gravity compensation by implementing feedforward control. The student will develop and validate adaptive models (friction, viscoelasticity…) which will be integrated in the control strategy.

Expected Project Development 

• Literature review
• Models development and validation
• Integration in the control strategy 

Required skills 

• C++ language

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:

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:

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:

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