Neuromuscular Controller Embedded in a Powered Ankle Exoskeleton: Effects on Gait, Clinical Features and Subjective Perspective of Incomplete Spinal Cord Injured Subjects

Powered exoskeletons are among the emerging technologies claiming to assist functional ambulation. The potential to adapt robotic assistance based on specific motor abilities of incomplete spinal cord injury (iSCI) subjects, is crucial to optimize Human-Robot Interaction (HRI). Achilles, an autonomous wearable robot able to assist ankle during walking, was developed for iSCI subjects and utilizes a NeuroMuscular Controller (NMC). NMC can be used to adapt robotic assistance based on specific residual functional abilities of subjects. The main aim of this pilot study was to analyze the effects of the NMC-controlled Achilles, used as an assistive device, on chronic iSCI participants' performance, by assessing gait speed during 10-session training of robot-aided walking. Secondary aims were to assess training impact on participants' motion, clinical and functional features and to evaluate subjective perspective in terms of attitude towards technology, workload, usability and satisfaction. Results showed that 5 training sessions were necessary to significantly improve robot-aided gait speed on short paths and consequently to optimize HRI. Moreover, the training allowed participants who initially were not able to walk for 6 minutes, to improve gait endurance during Achilles-aided walking and to reduce perceived fatigue. Improvements were obtained also in gait speed during free walking, thus suggesting a potential rehabilitative impact, even if Achilles-aided walking was not faster than free walking. Participants' subjective evaluations indicated a positive experience.

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

Safety Issues in Human-Robot Interactions

Aude Billard, Milos Vasic

Safety is an important consideration in human-robot interactions. Robots can perform powerful movements that can cause hazards to humans surrounding them. To prevent accidents, it is important to identify sources of potential harm, to determine which of the persons in the robot’s vicinity may be in greatest peril and to assess the type of injuries the robot may cause to this person. This survey starts with a review of the safety issues in industrial settings, where robots manipulate dangerous tools and move with extreme rapidity and force. We then move to covering issues related to the growing numbers of autonomous mobile robots that operate in crowded (human-inhabited) environments. We discuss the potential benefits of fully autonomous cars on safety on roads and for pedestrians. Lastly, we cover safety issues related to assistive robots.

2013

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

Florin Dzeladini

EPFL2019