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Physical Disturbance Rejection Methods in Myokinetic Control of Prosthetic Limbs

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Paggetti, F. Gherardini, M. Ianniciello, V. Cirelli, S. Cipriani, C. Scuola Superiore Sant'Anna Italy Scuola Superiore Sant'Anna BioRobotics Institute Italy Scuola Superiore Sant'Anna Department of Excellence in Robotics and AI Italy

Objective: The myokinetic control interface for limb protheses involves implanting small permanent magnets in the residual muscles of the stump, retrieving their displacement induced by muscle contraction using external magnetic sensors, and mapping such displacements to motor commands. We have previously shown the feasibility of tracking several magnets implanted in an anatomically relevant workspace. However, to clinically translate the interface, strategies to address different kinds of external disturbances compromising its functioning are mandatory. Among these, physical disturbances, viz. relative displacement between the sensors and the magnets not due to voluntary contraction, could significantly hinder the interface usability. Methods: Here we propose three rejection methods sought to mitigate this problem: one aims to realign the sensors and the magnets when a disturbance is detected, through numerical approximation methods;the others exploit differential measurements. We applied these concepts to upper limb prostheses, by mimicking the presence of physical disturbances in simulations and through a physical setup, reproducing four target muscles of a human forearm to be potentially implanted. Finally, we evaluated the rejection ability of one of those methods during the first-in-human implementation of the myokinetic interface. Results: All methods proved capable of rejecting the disturbances, showing median localization errors below 10% the displacement undergone during contraction. Conclusion: Results suggest that the optimal rejection method is application-specific, and provide hints for assessing different factors influencing the best choice. Significance: Other than being crucial for the myokinetic interface development, these outcomes also provide interesting insights for many biomedical applications exploiting remote magnetic tracking. © 1964-2012 IEEE.

关键词： Disturbance rejection

H-PhIEx α A Compact SEA-Based Hand Exoskeleton with Active Metacarpophalangeal Joints 10

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H-PhIEx α A Compact SEA-Based Hand Exoskeleton with Active ...

10th IEEE RAS/EMBS International Conference for Biomedical robotics and Biomechatronics, BioRob 2024

作者： Capitani, Stefano Laszlo Peperoni, Emanuele Kuang, Lisheng Fiumalbi, Tommaso Baldoni, Andrea Dell'agnello, Filippo Creatini, Ilaria Taglione, Elisa Vitiello, Nicola Trigili, Emilio Crea, Simona Motor Rehabilitation Center Pisa56048 Italy BioRobotics Institute Scuola Superiore Sant' Anna Pontedera56025 Italy Scuola Superiore Sant'Anna Department of Excellence in Robotics and AI Pisa56127 Italy IRCCS Fondazione Don Gnocchi Florence50143 Italy

ISBN: (纸本)9798350386523

Hand injuries have a considerable impact on daily life, requiring extensive rehabilitation efforts in both clinical and home environments. Portable wearable devices show promise in addressing this need., rendering rehabilitation repeatable., measurable., and challenging at the same time. This paper presents a novel hand exoskeleton designed to assist the thumb and index metacarpophalangeal (MCP) joints in flexion- extension movements while keeping the thumb free or locked in adduction-abduction and circumduction movements. The actuation units are based on a series-elastic architecture (SEA). Transmission chains include self-aligning mechanisms to avoid undesired parasitic forces on the user's musculoskeletal system. The device has a total weight of 190 g and can exert a maximum torque at the MCP joint up to 0.9 Nm. The lockable thumb chain enables users to lock the thumb in various configurations while assisting the flexion/extension motion. The device has been tested on the bench in position and admittance control., and with a human subject using electromyography-triggered flexion/extension movements. Results show a root mean square error in position control below 0.5 deg in all experimental conditions. The results prove that the developed hand exoskeleton can be used in rehabilitation applications. © 2024 IEEE.

关键词： Exoskeleton (robotics)

Pulsed electromagnetic field stimulation enhances neurite outgrowth in neural cells and modulates inflammation in macrophages

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Engineered Regeneration 2024年第1期5卷 80-91页

作者： Francesco Fontana Andrea Cafarelli Francesco Iacoponi Soria Gasparini Tiziano Pratellesi Abigail NKoppes Leonardo Ricotti The BioRobotics Institute Scuola Superiore Sant’Anna56127 PisaItaly Department of Excellence in Robotics&AI Scuola Superiore Sant’Anna56127 PisaItaly BAC Technology s.r.l. 50063 FlorenceItaly Department of Chemical Engineering Northeastern UniversityBostonMA 02115USA Department of Bioengineering Northeastern UniversityBostonMA 02115USA Department of Biology Northeastern UniversityBostonMA 02115USA

Nerve regeneration following traumas remains an unmet *** application of pulsed electromagnetic field(PEMF)stimulation has gained traction for a minimally invasive regeneration of ***,a sys-tematic exploration of different PEMF parameters influencing neuron function at a cellular level is not *** this study,we exposed neuroblastoma F11 cells to PEMF to trigger beneficial effects on neurite *** carrier frequencies,pulse repetition frequencies,and duty cycles were screened with a custom ad hoc setup to find the most influential parameters values.A carrier frequency of 13.5 MHz,a pulse repetition frequency of 20 Hz,and a duty cycle of 10%allowed maximal neurite outgrowth,with unaltered viability with respect to non-stimulated ***,in a longer-term analysis,such optimal conditions were also able to in-crease the gene expression of neuronal expression markers NeuN and Tuj-1 and transcription factor ***,the same optimal stimulation conditions were also applied to THP-1 macrophages,and both pro-inflammatory(TNF-α,IL-1β,IL-6,IL-8)and anti-inflammatory cytokines(IL-10,CD206)were *** optimal PEMF stimulation parameters did not induce differentiation towards an M1 macrophage phenotype,decreased IL-1β and IL-8 gene expression,decreased TNF-α and IL-8 cytokine release in M1-differentiated cells,increased IL-10 and CD206 gene expression,as well as IL-10 cytokine release in M0 *** specific PEMF stimulation regime,which is optimal in vitro,might have a high potential for a future in vivo translation targeting neural regeneration and anti-inflammatory action for treating peripheral nerve injuries.

关键词： Nerve regeneration Biophysical stimulation Pulsed electromagnetic fields(PEMF) Neurite outgrowth Anti-inflammatory Peripheral nerve injuries

Adaptive Drift Compensation for Soft Sensorized Finger Using Continual Learning

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Adaptive Drift Compensation for Soft Sensorized Finger Using...

IEEE International Conference on Soft robotics (RoboSoft)

作者： Nilay Kushawaha Radan Pathan Niccolò Pagliarani Matteo Cianchetti Egidio Falotico The BioRobotics Institute Scuola Superiore Sant’Anna Pontedera Italy Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Italy

ISBN: (数字)9798331520205

ISBN: (纸本)9798331520212

Strain sensors are gaining popularity in soft robotics for acquiring tactile data due to their flexibility and ease of integration. Tactile sensing plays a critical role in soft grippers, enabling them to safely interact with unstructured environments and precisely detect object properties. However, a significant challenge with these systems is their high non-linearity, time-varying behavior, and long-term signal drift. In this paper, we introduce a continual learning (CL) approach to model a soft finger equipped with piezoelectric-based strain sensors for proprioception. To tackle the aforementioned challenges, we propose an adaptive CL algorithm that integrates a Long Short-Term Memory (LSTM) network with a memory buffer for rehearsal and includes a regularization term to keep the model’s decision boundary close to the base signal while adapting to time-varying drift. We conduct nine different experiments, resetting the entire setup each time to demonstrate signal drift. We also benchmark our algorithm against two other methods and conduct an ablation study to assess the impact of different components on the overall performance.

关键词： Training Continuing education Adaptation models Adaptive systems Piezoelectric transducers Soft robotics Robot sensing systems Capacitive sensors Sensors Long short term memory

Adaptive Drift Compensation for Soft Sensorized Finger Using Continual Learning

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arXiv 2025年

作者： Kushawaha, Nilay Pathan, Radan Pagliarani, Niccolò Cianchetti, Matteo Falotico, Egidio The BioRobotics Institute Scuola Superiore Sant’Anna Pontedera Italy Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa Italy

关键词： Grippers

A Hybrid Adaptive Controller for Soft Robot Interchangeability

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arXiv 2023年

作者： Chen, Zixi Ren, Xuyang Bernabei, Matteo Mainardi, Vanessa Ciuti, Gastone Stefanini, Cesare The Biorobotics Institute The Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa56127 Italy

Soft robots have been leveraged in considerable areas like surgery, rehabilitation, and bionics due to their softness, flexibility, and safety. However, it is challenging to produce two same soft robots even with the same mold and manufacturing process owing to the complexity of soft materials. Meanwhile, widespread usage of a system requires the ability to replace inner components without highly affecting system performance, which is interchangeability. Due to the necessity of this property, a hybrid adaptive controller is introduced to achieve interchangeability from the perspective of control approaches. This method utilizes an offline-trained recurrent neural network controller to cope with the nonlinear and delayed response from soft robots. Furthermore, an online optimizing kinematics controller is applied to decrease the error caused by the above neural network controller. Soft pneumatic robots with different deformation properties but the same mold have been included for validation experiments. In the experiments, the systems with different actuation configurations and the different robots follow the desired trajectory with errors of 3.3 ± 2.9% and 4.3 ± 4.1% compared with the working space length, respectively. Such an adaptive controller also shows good performance on different control frequencies and desired velocities. This controller is also compared with a model-based controller in simulation. This controller endows soft robots with the potential for wide application, and future work may include different offline and online controllers. A weight parameter adjusting strategy may also be proposed in the future. Copyright © 2023, The Authors. All rights reserved.

关键词： Controllers

S2C2A: A Flexible Task Space Planning and Control Strategy for Modular Soft Robot Arms

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arXiv 2024年

作者： Chen, Zixi Guan, Qinghua Hughes, Josie Menciassi, Arianna Stefanini, Cesare Biorobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa56127 Italy CREATE Lab EPFL Lausanne Switzerland

Modular soft robot arms (MSRAs) are composed of multiple independent modules connected in a sequence. Due to their modular structure and high degrees of freedom (DOFs), these modules can simultaneously bend at different angles in various directions, enabling complex deformation. This capability allows MSRAs to perform more intricate tasks than singlemodule robots. However, the modular structure also induces challenges in accurate planning, modeling, and control. Nonlinearity, hysteresis, and gravity complicate the physical model, while the modular structure and increased DOFs further lead to accumulative errors along the sequence. To address these challenges, we propose a flexible task space planning and control strategy for MSRAs, named S2C2A (State to Configuration to Action). Our approach formulates an optimization problem, S2C (State to Configuration planning), which integrates various loss functions and a forward MSRA model to generate configuration trajectories based on target MSRA states. Given the model complexity, we leverage a biLSTM network as the forward model. Subsequently, a configuration controller C2A (Configuration to Action control) is implemented to follow the planned configuration trajectories, leveraging only inaccurate internal sensing feedback. Both a biLSTM network and a physical model are utilized for configuration *** validated our strategy using a cabledriven MSRA, demonstrating its ability to perform diverse offline tasks such as position control, orientation control, and obstacle avoidance. Furthermore, our strategy endows MSRA with online interaction capability with targets and obstacles. Future work will focus on addressing MSRA challenges, such as developing more accurate physical models and reducing configuration estimation errors along the module sequence. Copyright © 2024, The Authors. All rights reserved.

关键词： Recurrent neural networks

EEG-based self-paced decoding of upper limb movement intention in healthy subjects 2

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EEG-based self-paced decoding of upper limb movement intenti...

2nd Edition IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering, MetroXRaiNE 2023

作者： Ceradini, Matteo Tortora, Stefano Tonin, Luca Micera, Silvestro Scuola Superiore Sant 'Anna The BioRobotics Institute Department of Excellence in Robotics and Ai Pisa Italy University of Padova Department of Information Engineering Padova Italy Padua Neuroscience Center University of Padova Padova Italy Bertarelli Foundation in Translational Neuroengineering Epfl Geneva Switzerland

ISBN: (纸本)9798350300802

EEG-based brain-machine interfaces (BMIs) offer an intuitive approach for individuals with motor impairments to control prosthetic or rehabilitation devices. Decoding movement intentions plays a vital role in accurately translating the motor execution plans of subjects, such as identifying the desired grasp type or target position. In this study, EEG signals were recorded from seven healthy subjects during self-paced reaching and grasping tasks. Power Spectral Density (PSD) and Entropy were extracted as features to assess their efficacy in discrimination between different brain states related to movement planning. Classification between anticipation of movement and resting-state periods were evaluated using machine-learning methods (Linear Discriminant Analysis, Quadratic Discriminant Analysis, and Support Vector Machines). The achieved results provide strong evidence for the feasibility of decoding movement intention, laying the foundation for future applications and advancements in the field. Attaining high accuracy in decoding movement intention holds significant potential for the translational applications of BMIs in the fields of biomedical engineering and rehabilitation. © 2023 IEEE.

关键词： brain signal processing brain-machine interface (BMI) electroencephalography movement intention decoding

Continual Learning for Multimodal Data Fusion of a Soft Gripper

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arXiv 2024年

作者： Kushawaha, Nilay Falotico, Egidio The BioRobotics Institute Scuola Superiore Sant'Anna Pontedera Italy Department of Excellence in Robotics and AI Scuola Superiore Sant'Anna Pisa Italy

Continual learning (CL) refers to the ability of an algorithm to continuously and incrementally acquire new knowledge from its environment while retaining previously learned information. A model trained on one data modality often fails when tested with a different modality. A straightforward approach might be to fuse the two modalities by concatenating their features and training the model on the fused data. However, this requires retraining the model from scratch each time it encounters a new domain. In this paper, we introduce a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class-incremental and domain-incremental learning scenarios in an artificial environment where labeled data is scarce, yet non-iid (independent and identical distribution) unlabeled data from the environment is plentiful. The proposed algorithm is efficient and only requires storing prototypes for each class. We evaluate the algorithm's effectiveness on a challenging custom multimodal dataset comprising of tactile data from a soft pneumatic gripper, and visual data from non-stationary images of objects extracted from video sequences. Additionally, we conduct an ablation study on the custom dataset and the Core50 dataset to highlight the contributions of different components of the algorithm. To further demonstrate the robustness of the algorithm, we perform a real-time experiment for object classification using the soft gripper and an external independent camera setup, all synchronized with the Robot Operating System (ROS) framework. © 2024, CC BY.

关键词： Robot Operating System