Evolution of a beam dynamics model for the transport lines in a proton therapy facility

Despite the fact that the first-order beam dynamics models allow an approximated evaluation of the beam properties, their contribution is essential during the conceptual design of an accelerator or beamline. However, during the commissioning some of their limitations appear in the comparison against measurements. The extension of the linear model to higher order effects is, therefore, demanded. In this paper, the effects of particle-matter interaction have been included in the model of the transport lines in the proton therapy facility at the Paul Scherrer Institut (PSI) in Switzerland. To improve the performance of the facility, a more precise model was required and has been developed with the multi-particle open source beam dynamics code called OPAL (Object oriented Particle Accelerator Library). In OPAL, the Monte Carlo simulations of Coulomb scattering and energy loss are performed seamless with the particle tracking. Beside the linear optics, the influence of the passive elements (e.g. degrader, collimators, scattering foils and air gaps) on the beam emittance and energy spread can be analysed in the new model. This allows for a significantly improved precision in the prediction of beam transmission and beam properties. The accuracy of the OPAL model has been confirmed by numerous measurements.Comment: 17 pages, 19 figure

Full Scale Proton Beam Impact Testing of new CERN Collimators and Validation of a Numerical Approach for Future Operation

New collimators are being produced at CERN in the framework of a large particle accelerator upgrade project to protect beam lines against stray particles. Their movable jaws hold low density absorbers with tight geometric requirements, while being able to withstand direct proton beam impacts. Such events induce considerable thermo-mechanical loads, leading to complex structural responses, which make the numerical analysis challenging. Hence, an experiment has been developed to validate the jaw design under representative conditions and to acquire online results to enhance the numerical models. Two jaws have been impacted by high-intensity proton beams in a dedicated facility at CERN and have recreated the worst possible scenario in future operation. The analysis of online results coupled to post-irradiation examinations have demonstrated that the jaw response remains in the elastic domain. However, they have also highlighted how sensitive the jaw geometry is to its mounting support inside the collimator. Proton beam impacts, as well as handling activities, may alter the jaw flatness tolerance value by $\pm$ 70 ${\mu}$m, whereas the flatness tolerance requirement is 200 ${\mu}$m. In spite of having validated the jaw design for this application, the study points out numerical limitations caused by the difficulties in describing complex geometries and boundary conditions with such unprecedented requirements.Comment: 22 pages, 17 figures, Prepared for submission to JINS

Autoencoder-based myoelectric controller for prosthetic hands

In the past, linear dimensionality-reduction techniques, such as Principal Component Analysis, have been used to simplify the myoelectric control of high-dimensional prosthetic hands. Nonetheless, their nonlinear counterparts, such as Autoencoders, have been shown to be more effective at compressing and reconstructing complex hand kinematics data. As a result, they have a potential of being a more accurate tool for prosthetic hand control. Here, we present a novel Autoencoder-based controller, in which the user is able to control a high-dimensional (17D) virtual hand via a low-dimensional (2D) space. We assess the efficacy of the controller via a validation experiment with four unimpaired participants. All the participants were able to significantly decrease the time it took for them to match a target gesture with a virtual hand to an average of 6.9s and three out of four participants significantly improved path efficiency. Our results suggest that the Autoencoder-based controller has the potential to be used to manipulate high-dimensional hand systems via a myoelectric interface with a higher accuracy than PCA; however, more exploration needs to be done on the most effective ways of learning such a controller

Nonlinear dimensionality reduction for human movement analysis with application to body machine interfaces

For my PhD project, I set to explore how a nonlinear dimensionality reduction (DR) technique - autoencoder networks (AEs) - can identify low-dimensional latent manifolds of movement data. The thesis focuses on the application of this technique to body machine interfaces (BoMIs). I begin by comparing nonlinear AEs to Principal Component Analysis (PCA), a linear DR method, in capturing essential information of kinematic signals, including hand gestures and object manipulations, as well as electromyographic signals (EMG) obtained from unconstrained movements of shoulders and arms. AEs exhibited higher performance than PCA in the reconstruction of hand kinematic and EMG data from a latent manifold. Therefore, a non-linear DR method has the potential to provide a more effective coding platform for human-machine interfaces (HMIs). I therefore investigated how the choice of hyperparameters (e.g., type of activation function, number of hidden layers, etc.) affected the shape of the latent manifold, particularly its local curvature, and whether these potential effects were correlated with changes in reconstruction performance. To gain more consistent insights on the structure of the nonlinear latent manifold, I developed a visual tool based on classic concepts of cartography. This display offers a direct and intuitive assessment of the AE\u2019s nonlinear transformation. I was able to demonstrate that the cartographic approach makes the visible structure of the latent manifold stable and independent of the AE\u2019s training parameters. As such, the proposed approach is a step toward defining a unique latent manifold. After analyzing the properties of both linear (PCA) and nonlinear (AE) DR techniques, I focused on their applications within the control-scheme of a BoMI. First, I aimed at providing BoMI users with the possibility to switch seamlessly between movement and EMG control. Such approach is essential to utilize the BoMI as a therapeutic tool for promoting recovery of muscle control after neurological injury. In the clinical context it is essential to adapt the operation of the BoMI to the evolving state of its users. My guiding hypothesis for this purpose is that the operation of the interface is facilitated if the BoMI forward map is updated online to match the evolving latent manifold of the user\u2019s motions. Results show that this adaptive approach increased the representational efficiency of the interface and significantly improved users\u2019 task-related performance. As an extension of the application of AE-based BoMI, I developed a non-linear BoMI designed to control an assistive 4D virtual robotic manipulator and tested the interface on a cohort of unimpaired participants, who successfully acquired a high level of robot control. To conclude, I investigated whether AEs can represent and estimate motor learning during the operation of a BoMI. This final study demonstrates that in fact this nonlinear method is effective to accurately track users\u2019 learning process

A Non-Linear Body Machine Interface For Controlling Assistive Robotic Arms

Objective: Body machine interfaces (BoMIs) enable individuals with paralysis to achieve a greater measure of independence in daily activities by assisting the control of devices such as robotic manipulators. The first BoMIs relied on Principal Component Analysis (PCA) to extract a lower dimensional control space from information in voluntary movement signals. Despite its widespread use, PCA might not be suited for controlling devices with a large number of degrees of freedom, as because of PCs' orthonormality the variance explained by successive components drops sharply after the first. Methods: Here, we propose an alternative BoMI based on non-linear autoencoder (AE) networks that mapped arm kinematic signals into joint angles of a 4D virtual robotic manipulator. First, we performed a validation procedure that aimed at selecting an AE structure that would allow to distribute the input variance uniformly across the dimensions of the control space. Then, we assessed the users' proficiency practicing a 3D reaching task by operating the robot with the validated AE. Results: All participants managed to acquire an adequate level of skill when operating the 4D robot. Moreover, they retained the performance across two non-consecutive days of training. Conclusion: While providing users with a fully continuous control of the robot, the entirely unsupervised nature of our approach makes it ideal for applications in a clinical context since it can be tailored to each user's residual movements. Significance: We consider these findings as supporting a future implementation of our interface as an assistive tool for people with motor impairments

A video-based markerless body machine interface: a pilot study

Regaining functional independence plays a crucial role to improve the qualify of life of individuals with motor disabilities. Here, we address this problem within the framework of Body-Machine Interfaces (BoMIs). BoMIs enable individuals with restricted mobility to extend their capabilities by mapping their residual body movements into commands to control an external device. In this study, we propose a video-based marker-less interface that can track the position of the shoulders and the head using a state-of-the-art approach relying on the DeepLabCut (DLC) architecture. The high-dimensional body signal is then mapped into a lower dimensional space via non-linear variational autoencoder to obtain commands for a 2D computer cursor. First, we perform an offline test to evaluate the prediction power of the DLC fine tuned model. Then, we verify whether the proposed pipeline can be used to control a computer cursor in real-time. Results showed that the network can accurately predict the position of body landmarks. Moreover, an unimpaired participant was able to efficiently operate the computer cursor and gain a high-level of control skill after training with the interface. This enables performing experiments with video-based marker-less BoMIs for future implementation of an assistive device for people with motor disabilities

Building an adaptive interface via unsupervised tracking of latent manifolds

In human\u2013machine interfaces, decoder calibration is critical to enable an effective and seamless interaction with the machine. However, recalibration is often necessary as the decoder off-line predictive power does not generally imply ease-of-use, due to closed loop dynamics and user adaptation that cannot be accounted for during the calibration procedure. Here, we propose an adaptive interface that makes use of a non-linear autoencoder trained iteratively to perform online manifold identification and tracking, with the dual goal of reducing the need for interface recalibration and enhancing human\u2013machine joint performance. Importantly, the proposed approach avoids interrupting the operation of the device and it neither relies on information about the state of the task, nor on the existence of a stable neural or movement manifold, allowing it to be applied in the earliest stages of interface operation, when the formation of new neural strategies is still on-going. In order to more directly test the performance of our algorithm, we defined the autoencoder latent space as the control space of a body\u2013machine interface. After an initial offline parameter tuning, we evaluated the performance of the adaptive interface versus that of a static decoder in approximating the evolving low-dimensional manifold of users simultaneously learning to perform reaching movements within the latent space. Results show that the adaptive approach increased the representational efficiency of the interface decoder. Concurrently, it significantly improved users\u2019 task-related performance, indicating that the development of a more accurate internal model is encouraged by the online co-adaptation process