Implementation of a Human Feedback-based Locomotion and its Control by means of a Feedforward Component inspired by Central Pattern Generators

Walking gaits of various animals have been modeled using this framework of differential equations, and more specifically, using network of coupled oscillators or CPG (Central Pattern Generators). In these models, oscillators are coupled among themselves and thus influence each other. Nerve signals generating swimming in the Lampreys have been modeled using such a system. These oscillatory networks were successfully used to model several swimming animals. However, results obtained with walking animals have been rather disappointing. This is not surprising, since CPGs does not take into account interaction with the environment for shaping the movement, which seems to be more important for walking animals. In this project we study the interaction between feedback and CPGs in humans using a bio-inspired musculoskeletal model of human walking. We start with a pure feedback based model of human walking and extend it by introducing a feedforward component inspired by CPGs. We then test the properties of such a hybrid feedback and feedforward system. We show that, not only those new models are stable with characteristics close to the original model, but with online control they showed a clear increase of the robustness compared to pure feedback model. Moreover, modifications of some general parameters of the feedforward component allow easy changes in gait characteristics, such as gait speed

A Neuromuscular Model for Symbiotic Man-Machine Exoskeleton Control Accounting for Patient Impairment Specificity

Millions of people worldwide live with impaired locomotion. The degree of impairment is highly variable and the causes are multiple. This variation necessitates the design of a new generation of exoskeleton controllers for personalised, symbiotic man-machine interaction. One of the characteristics of such a controller is the ability to realistically include the characteristics of both normal and neurologically impaired human locomotion. The information can be used to recover only the relevant missing features of locomotion. In this paper, we describe the main characterisation tools used to describe human movement and discuss possible ways to include the resulting information in a neuromuscular model in order to create a personalised controller for a wearable exoskeleton

Muscle activation variability is inversely correlated with walking speed

Individuals with motor impairments typically walk at much slower speeds than their unimpaired counter- parts, yet their gait data is still evaluated against the relatively faster gait of healthy subjects. Therefore a good understanding of unimpaired gait at extremely slow speeds is needed for comparison. Studies have shown that walking at very slow speeds is quantitatively different from self-selected walking speed. These modifications can be observed at different levels (kinetic, kinematic, electromyographic). In order to better understand the changes in walking at extremely slow speeds, we recorded seven subjects walking at their preferred speed and at speeds ranging from 0.11 m/s to 0.61 m/s. In this study, we analyzed changes in muscle activations and quantified their variability using the Pearson correlation coefficient. Confirming previous observation, we show that both the inter- and intra- subject variability of muscle activities increases with decreases in walking speed, with a more pronounced effect for proximal muscles. The inter-subject correlation of muscle activities also suggests a modular organization of muscle activities in three functional blocks at normal speed. This modular organization vanishes with decreasing walking speed following a proximo- distal gradient

Real-time full body motion imitation on the COMAN humanoid robot

On-line full body imitation with a humanoid robot standing on its own two feet requires simultaneously maintaining the balance and imitating the motion of the demonstrator. In this paper we present a method that allows real-time motion imitation while maintaining stability, based on prioritized task control. We also describe a method of modified prioritized kinematic control that constrains the imitated motion to preserve stability only when the robot would tip over, but does not alter the motions otherwise. To cope with the passive compliance of the robot, we show how to model the estimation of the center of mass of the robot using support vector machines. In the paper we give detailed description of all steps of the algorithm, essentially providing a tutorial on the implementation of kinematic stability control. We present the results on a child-sized humanoid robot called Compliant Humanoid Platform or COMAN. Our implementation shows reactive and stable on-line motion imitation of the humanoid robot

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

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

The contribution of a central pattern generator in a reflex-based neuromuscular model

Although the concept of central pattern generators (CPGs) controlling locomotion in vertebrates is widely accepted, the presence of specialized CPGs in human locomotion is still a matter of debate. An interesting numerical model developed in the 90s' demonstrated the important role CPGs could play in human locomotion, both in terms of stability against perturbations, and in terms of speed control. Recently, a reflex-based neuro-musculo-skeletal model has been proposed, showing a level of stability to perturbations similar to the previous model, without any CPG components. Although exhibiting striking similarities with human gaits, the lack of CPG makes the control of speed/step length in the model difficult. In this paper, we hypothesize that a CPG component will offer a meaningful way of controlling the locomotion speed. After introducing the CPG component in the reflex model, and taking advantage of the resulting properties, a simple model for gait modulation is presented. The results highlight the advantages of a CPG as feed forward component in terms of gait modulation

Stable real-time full body motion imitation on the COMAN humanoid robot.

On-line full body imitation with a humanoid robot standing on its own two feet requires simultaneously maintaining the balance and imitating the motion of the demonstrator. In this paper we present a method that allows real-time motion imitation while maintaining stability, based on prioritized task control. Additionally, we describe a method of modified prioritized kinematic control that constrains the imitated motion to preserve stability only when the robot would tip over, but does not alter the motions otherwise. In the paper we give detailed description of all the steps of the algorithm, essentially providing a tutorial on the implementation of kinematical stability control. We present the results on the child sized humanoid robot called Compliant Humanoid Platform or COMAN. Our implementation for the first time shows reactive, stable on-line motion imitation on a large humanoid robot

Development of a simulated transtibial amputee model

Currently, there are more than 30 million amputees in the world and each year thousands of people suffer from amputation and, therefore, the development of lower limb prostheses is crucial to improve the quality of millions of people's lives by restoring their mobility. This contribution proposes a simulated amputee model capable of reproducing a transtibial amputee subject wearing a passive prosthesis. The passive prosthesis behavior is simulated using a spring-damper system between shin and foot. This contribution provides a tool capable of reproducing an amputee subject wearing a passive prosthesis, as well as an adaptive framework where researchers can deploy controllers in the simulated transtibial prosthesis, transforming it in a powered transtibial prosthesis. Results show that the amputee model is good for the simulation of transtibial amputees wearing a passive device or an active transtibial prosthesis