Rehabilitative Soft Exoskeleton for Rodents

Robotic exoskeletons provide programmable, consistent and controllable active therapeutic assistance to patients with neurological disorders. Here we introduce a prototype and preliminary experimental evaluation of a rehabilitative gait exoskeleton that enables compliant yet effective manipulation of the fragile limbs of rats. To assist the displacements of the lower limbs without impeding natural gait movements, we designed and fabricated soft pneumatic actuators (SPAs). The exoskeleton integrates two customizable SPAs that are attached to a limb. This configuration enables a 1 N force load, a range of motion exceeding 80 mm in the major axis, and speed of actuation reaching two gait cycles/s. Preliminary experiments in rats with spinal cord injury validated the basic features of the exoskeleton. We propose strategies to improve the performance of the robot and discuss the potential of SPAs for the design of other wearable interfaces.

An Adaptive Neuromuscular Controller for Assistive Lower-Limb Exoskeletons: A Preliminary Study on Subjects with Spinal Cord Injury

Tycho Jan Henricus Brug, Florin Dzeladini, Auke Ijspeert, Amy Roning Wu

Versatility is important for a wearable exoskeleton controller to be responsive to both the user and the environment. These characteristics are especially important for subjects with spinal cord injury (SCI), where active recruitment of their own neuromuscular system could promote motor recovery. Here we demonstrate the capability of a novel, biologically-inspired neuromuscular controller (NMC) which uses dynamical models of lower limb muscles to assist the gait of SCI subjects. Advantages of this controller include robustness, modularity, and adaptability. The controller requires very few inputs (i.e., joint angles, stance, and swing detection), can be decomposed into relevant control modules (e.g., only knee or hip control), and can generate walking at different speeds and terrains in simulation. We performed a preliminary evaluation of this controller on a lower-limb knee and hip robotic gait trainer with seven subjects (N = 7, four with complete paraplegia, two incomplete, one healthy) to determine if the NMC could enable normal-like walking. During the experiment, SCI subjects walked with body weight support on a treadmill and could use the handrails. With controller assistance, subjects were able to walk at fast walking speeds for ambulatory SCI subjects-from 0.6 to 1.4 m/s. Measured joint angles and NMC-provided joint torques agreed reasonably well with kinematics and biological joint torques of a healthy subject in shod walking. Some differences were found between the torques, such as the lack of knee flexion near mid-stance, but joint angle trajectories did not seem greatly affected. The NMC also adjusted its torque output to provide more joint work at faster speeds and thus greater joint angles and step length. We also found that the optimal speed-step length curve observed in healthy humans emerged for most of the subjects, albeit with relatively longer step length at faster speeds. Therefore, with very few sensors and no predefined settings for multiple walking speeds or adjustments for subjects of differing anthropometry and walking ability, NMC enabled SCI subjects to walk at several speeds, including near healthy speeds, in a healthy-like manner. These preliminary results are promising for future implementation of neuromuscular controllers on wearable prototypes for real-world walking conditions.

Frontiers Research Foundation2017

Neuroprosthetics to support impaired hand functions: A multidisciplinary approach combining brain-machine interfaces and wearable exoskeletons

Luca Randazzo

Neuroprosthetics, the discipline that aims at interfacing neural systems to artificially engineered devices, has witnessed in recent years important advancements towards the ultimate goal of augmenting and restoring human functions through technology and artificial systems. Research in this field covers a wide range of efforts, from advancing our basic understanding of the neural processes involved into human sensory, motor and cognitive functions, to developing artificial intelligence algorithms for their decoding, to the physical interfacing between artificial devices and the human nervous system and biomechanics. Ultimately, this discipline aims at fusing these systems in order to restore, replace and rehabilitate lost and impaired functions due to motor disabling conditions or traumatic accidents. In this thesis, I present a multidisciplinary approach that aimed at developing a neuroprosthesis for the assistance and restoration of hand functions impaired by neurological disorders or traumatic accidents, such as cerebrovascular accidents and spinal cord injuries. These efforts encompassed the conceptualization and development of a robotic hand exoskeleton and brain-machine interface (BMI) algorithms for its control. Specifically, this thesis focused on the design, development and testing of (i) a novel mechatronic hand exoskeleton to assist hand motions within domestic and clinical settings, (ii) non-invasive BMI approaches based on electroencephalography (EEG) to decode neural correlates of intended hand movements, and on (iii) the closed-loop integration of the proposed exoskeleton and BMI for the sake of providing continuous feedback about ongoing neural modulations through hand motions and to trigger sensorimotor rehabilitation within clinical scenarios. Results showed that the proposed mechatronic system can successfully control hand opening and closing within a fully wearable, portable and lightweight package. The system was tested with users who suffered from motor disabling impairments, showing that it could help them in performing several activities of daily living for the first time since their accidents. From a brain-machine interfacing perspective, this work shows how imagined hand movements can be decoded, through EEG, in parallel with exoskeleton-induced motions, with important implications for the development of more embodied human-machine interaction protocols. Finally, this work shows how the closed-loop control of exoskeleton motions by means of the decoded ongoing neural activity enhances the discriminability of sensorimotor neural patterns and improves the performance of the brain-machine control channel. Overall, the results presented here represent important advancements within the field of neuroprosthetics, with interesting implications for the development of assistive exoskeletal technologies and non-invasive brain-machine interfaces aimed at controlling such systems in clinical or domestic settings, for both assistive and neurorehabilitative purposes.

EPFL2018

From bio-inspired locomotion models To controllers for lower-limb exoskeletons

Florin Dzeladini

Human locomotion shows fascinating abilities which are the results of the interplay between the environment, the biomechanics, the spinal cord, and modulation from higher control centers. How the different structures interact to generate meaningful behavior is an active field of research, and understanding the key principles underlying bipedal locomotion could have a strong impact and important implications in several fields related to both medicine and robotics, such as improved rehabilitation procedures, predicting surgery outcome or facilitated human-robot interaction. In this context, the development of biologically relevant bipedal models that faithfully recapitulate human locomotion are urgently needed. Existing such bio-inspired models usually rely on one of the two following principles: the Central Pattern Generators (CPGs) and the reflexes. In the first part of the thesis, we present a method to introduce a CPGs as feedforward components in a feedback based (i.e. reflex) model of human walking, named neuromuscular model (NMM). The proposed strategy is based on the idea that, in a feedback driven system, the feedforward component can be viewed as a feedback predictor. We implement the feedback predictors using morph oscillators as abstract models of biological CPGs. Thanks to the intrinsic robustness inherited from the feedback pathways, modulation of CPGs network's frequency and amplitudes becomes possible, over a broad range, without affecting the overall walking stability. Furthermore, the modulation of the CPGs network's parameters allowed smooth and stable gait modulation (such as changes in speed and adaptation to increasing slope) suggesting that the idea of using feedback predictor as gait modulator can be extended to a large range of applications, highlighting the role biological CPGs could play on top of a reflex-based rhythmic movement. Building on the NMM, we present, in the second part of the thesis, the implementations of the models as controllers on different ortheses and exoskeletons. Wearable devices designed to assist abnormal gaits require controllers that interact with the user in an intuitive and unobtrusive manner. Here, we rationalized that such a neuromuscular controller could be implemented based on the NMM models. The implementation of NMM model on a controller (NMC) was demonstrated for human healthy subject and was confirmed with experiment on SCI subjects with different devices. Overall, the bio-inspired NMCs successfully demonstrated remarkable versatility in generating gait patterns tuned to the subjects' dynamics and producing near-physiological gait at near-normative speeds. The positive SCI subject-machine interaction stemmed from replacing the subject's impaired function with dynamical virtual muscles that require few sensors. These preliminary but auspicious results have important implications towards the exploitation of natural walking dynamics through understanding human biological behavior in the design of controllers for wearable devices that are amenable to various environmental conditions and promote intuitive and unobtrusive human-machine interaction.

EPFL2019