Towards Variable Assistance for Lower Body Exoskeletons

This letter presents and experimentally demonstrates a novel framework for variable assistance on lower body exoskeletons, based upon safety-critical control methods. Existing work has shown that providing some freedom of movement around a nominal gait, instead of rigidly following it, accelerates the spinal learning process of people with a walking impediment when using a lower body exoskeleton. With this as motivation, we present a method to accurately control how much a subject is allowed to deviate from a given gait while ensuring robustness to patient perturbation. This method leverages control barrier functions to force certain joints to remain inside predefined trajectory tubes in a minimally invasive way. The effectiveness of the method is demonstrated experimentally with able-bodied subjects and the Atalante lower body exoskeleton

Enhancing performance during inclined loaded walking with a powered ankle-foot exoskeleton

A simple ankle-foot exoskeleton that assists plantarflexion during push-off can reduce the metabolic power during walking. This suggests that walking performance during a maximal incremental exercise could be improved with an exoskeleton if the exoskeleton is still efficient during maximal exercise intensities. Therefore, we quantified the walking performance during a maximal incremental exercise test with a powered and unpowered exoskeleton: uphill walking with progressively higher weights. Nine female subjects performed two incremental exercise tests with an exoskeleton: 1 day with (powered condition) and another day without (unpowered condition) plantarflexion assistance. Subjects walked on an inclined treadmill (15 %) at 5 km h(-1) and 5 % of body weight was added every 3 min until exhaustion. At volitional termination no significant differences were found between the powered and unpowered condition for blood lactate concentration (respectively, 7.93 +/- A 2.49; 8.14 +/- A 2.24 mmol L-1), heart rate (respectively, 190.00 +/- A 6.50; 191.78 +/- A 6.50 bpm), Borg score (respectively, 18.57 +/- A 0.79; 18.93 +/- A 0.73) and peak (respectively, 40.55 +/- A 2.78; 40.55 +/- A 3.05 ml min(-1) kg(-1)). Thus, subjects were able to reach the same (near) maximal effort in both conditions. However, subjects continued the exercise test longer in the powered condition and carried 7.07 +/- A 3.34 kg more weight because of the assistance of the exoskeleton. Our results show that plantarflexion assistance during push-off can increase walking performance during a maximal exercise test as subjects were able to carry more weight. This emphasizes the importance of acting on the ankle joint in assistive devices and the potential of simple ankle-foot exoskeletons for reducing metabolic power and increasing weight carrying capability, even during maximal intensities

Feedback Control of an Exoskeleton for Paraplegics: Toward Robustly Stable Hands-free Dynamic Walking

This manuscript presents control of a high-DOF fully actuated lower-limb exoskeleton for paraplegic individuals. The key novelty is the ability for the user to walk without the use of crutches or other external means of stabilization. We harness the power of modern optimization techniques and supervised machine learning to develop a smooth feedback control policy that provides robust velocity regulation and perturbation rejection. Preliminary evaluation of the stability and robustness of the proposed approach is demonstrated through the Gazebo simulation environment. In addition, preliminary experimental results with (complete) paraplegic individuals are included for the previous version of the controller.Comment: Submitted to IEEE Control System Magazine. This version addresses reviewers' concerns about the robustness of the algorithm and the motivation for using such exoskeleton

Preference-Based Learning for Exoskeleton Gait Optimization

This paper presents a personalized gait optimization framework for lower-body exoskeletons. Rather than optimizing numerical objectives such as the mechanical cost of transport, our approach directly learns from user preferences, e.g., for comfort. Building upon work in preference-based interactive learning, we present the CoSpar algorithm. CoSpar prompts the user to give pairwise preferences between trials and suggest improvements; as exoskeleton walking is a non-intuitive behavior, users can provide preferences more easily and reliably than numerical feedback. We show that CoSpar performs competitively in simulation and demonstrate a prototype implementation of CoSpar on a lower-body exoskeleton to optimize human walking trajectory features. In the experiments, CoSpar consistently found user-preferred parameters of the exoskeleton’s walking gait, which suggests that it is a promising starting point for adapting and personalizing exoskeletons (or other assistive devices) to individual users

Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits

Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users’ preferences over a high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LINECOSPAR, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LINECOSPAR is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamicity, while also highlighting differences in the utility functions underlying individual users’ gait preferences. This result has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation

Robotic design and modelling of medical lower extremity exoskeletons

This study aims to explain the development of the robotic Lower Extremity Exoskeleton (LEE) systems between 1960 and 2019 in chronological order. The scans performed in the exoskeleton system’s design have shown that a modeling program, such as AnyBody, and OpenSim, should be used first to observe the design and software animation, followed by the mechanical development of the system using sensors and motors. Also, the use of OpenSim and AnyBody musculoskeletal system software has been proven to play an essential role in designing the human-exoskeleton by eliminating the high costs and risks of the mechanical designs. Furthermore, these modeling systems can enable rapid optimization of the LEE design by detecting the forces and torques falling on the human muscles

Mechanisms of energy optimization in human walking

Humans can learn to move optimally. For many movements, we have a control strategy—or control policy—that optimizes some objective. In walking, we prefer the combination of step widths, step lengths, and speeds that optimizes the amount of energy we need. In familiar contexts, we have had many opportunities to establish this optimal control policy. But in new contexts, the nervous system must quickly learn new control policies in order to continue to move optimally. Our lab has recently demonstrated that humans can continuously optimize energetic cost during walking. This is an impressive feat given that the nervous system has tens of thousands of motor units at its disposal, and it can coordinate these motor units over millisecond timescales, which results in countless combinations of motor unit coordination. The goal of this thesis is to determine how the nervous system navigates this combinatorial problem to learn new energy optimal control policies in new walking contexts. I used three distinct studies to accomplish this goal. For the first two studies, I designed and implemented a simple mechatronic system that applies energetic penalties in the form of walking incline as a function of gait. This creates a new relationship between gait and energetic cost—or new cost landscape—that shifts the energy optimal gait. For the third study, I used exoskeletons that apply assistive torques to each ankle at each walking step to shift the energy optimal gait. The first study tested whether previous findings that people can learn to adapt their control policy when the energy optimum is shifted along step frequency generalize to a different gait parameter and to a different experimental setup. I found that, like step frequency, people can learn to adapt their control policy when the energy optimum is shifted along step width. The second study tested if and how energy optimization extends to multiple gait parameters at the same time. I found that, when the energy optimum is shifted along step width and step frequency, people are limited in their ability to optimize both gait parameters. The third study asked how people learn in which ways to optimize their policy. I found that general variability leads to specific adaptation toward optimal policies. Taken together, these findings provide insight into the mechanisms that underlie energy optimization in walking, as well as the limitations of this optimization

User-Centered Back-Support Exoskeleton: Design and Prototyping

Exhausting manual labor is still predominant in the industrial context. It typically consists in manipulating heavy parts or working in non-ergonomic conditions. The resulting work-related musculoskeletal disorders are a major problem to tackle. The most-affected body section is the the lumbar spine. Recently, exoskeletons have been identified as a possible non-invasive solution to reduce the impact of low-back pain. State-of-the-art prototypes have been optimized to: follow unconstrained human kinematics, (partially) relieve the load on assisted joints, and allow anthropometric adaptation. Yet, this technology still has limited adoption. Manufacturing optimization may address the following limitations: bulky/heavy resulting designs, complex assembly and maintenance, high manufacturing costs, long procedures for adaptation and wearing, and psychological effects (e.g., cognitive load and usability). In this contribution, the aforementioned issues are tackled improving a previous low-back exoskeleton prototype. In particular, kinematic analysis, Finite-Element-Method, and topological optimization have been combined to obtain a lightweight prototype, testing different materials (Nylon, carbon-fiber reinforced PC/ABS, etc.). We applied both Design for Assembly and Design for Manufacturability. The resulting exoskeleton prototype is described in the paper, ready for end-user field tests

Development and Biomechanical Analysis toward a Mechanically Passive Wearable Shoulder Exoskeleton

Shoulder disability is a prevalent health issue associated with various orthopedic and neurological conditions, like rotator cuff tear and peripheral nerve injury. Many individuals with shoulder disability experience mild to moderate impairment and struggle with elevating the shoulder or holding the arm against gravity. To address this clinical need, I have focused my research on developing wearable passive exoskeletons that provide continuous at-home movement assistance. Through a combination of experiments and computational tools, I aim to optimize the design of these exoskeletons. In pursuit of this goal, I have designed, fabricated, and preliminarily evaluated a wearable, passive, cam-driven shoulder exoskeleton prototype. Notably, the exoskeleton features a modular spring-cam-wheel module, allowing customizable assistive force to compensate for different proportions of the shoulder elevation moment due to gravity. The results of my research demonstrated that this exoskeleton, providing modest one-fourth gravity moment compensation at the shoulder, can effectively reduce muscle activity, including deltoid and rotator cuff muscles. One crucial aspect of passive shoulder exoskeleton design is determining the optimal anti-gravity assistance level. I have addressed this challenge using computational tools and found that an assistance level within the range of 20-30% of the maximum gravity torque at the shoulder joint yields superior performance for specific shoulder functional tasks. When facing a new task dynamic, such as wearing a passive shoulder exoskeleton, the human neuro-musculoskeletal system adapts and modulates limb impedance at the end-limb (i.e., hand) to enhance task stability. I have presented development and validation of a realistic neuromusculoskeletal model of the upper limb that can predict stiffness modulation and motor adaptation in response to newly introduced environments and force fields. Future studies will explore the model\u27s applicability in predicting stiffness modulation for 3D movements in novel environments, such as passive assistive devices\u27 force fields