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Development and Adaptive Assistance Control of the Robotic Hip Exoskeleton to Improve Gait Symmetry and Restore Normal Gait

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第1期21卷 799-809页

作者： Zhang, Ting Li, Yang Ning, Chuanxin Zeng, Bo Soochow Univ Coll Mech & Elect Engn Microsyst Ctr Suzhou 215000 Peoples R China

Neurological disorders and aging induce abnormal gait kinematics such as asymmetric gait, reduced stride length, and walking speeds. Gait asymmetry increases musculoskeletal pain and joint impairment in the affected leg, increases the risk of patients falling, and induces higher energy expenditure. This paper presents a novel assistive control framework for the series elastic actuator-driven robotic hip exoskeleton that improves gait symmetry and restores normal gait. The proposed assistive control depends on virtual stiffness compensation with a series elastic actuator (SEA)-driven hip exoskeleton to assist without needing estimation, prediction of the gait phase, or human intent. In addition, adaptive adjustment of the virtual stiffness based on the approximate dynamic programming method is used to improve gait symmetry. The proposed control strategy is successfully implemented, and experiments are validated with human subjects' gait assistance using a SEA-driven wearable hip exoskeleton. Note to Practitioners-This paper was motivated by the problem of hip exoskeleton assistive control. The goal of hip exoskeleton assistance is to improve gait symmetry and restore the normal gait of the patient with neurological disorders and the elderly. Existing approaches to fixturing such parts generally need to estimate or predict the gait phase or human intent. And the existing approaches generally adopted fixed control parameters. The hip exoskeleton's assistance cannot adapt to various walking conditions and human gait differences. The proposed assistive control is based on virtual stiffness compensation with a SEA-driven hip exoskeleton to assist with walking under various walking conditions without estimating or predicting the gait phase or human intent. And the proposed method is online adjusted control parameters with human-in-the-loop to improve the gait symmetry. The human walking experiment results showed that the hip exoskeleton assistive controller not only reduce

关键词： Robotic hip exoskeleton assistive control gait symmetry human-in-the-loop optimization

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A Novel Framework to Facilitate User Preferred Tuning for a Robotic Knee Prosthesis

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2023年 31卷 895-903页

作者： Alili, Abbas Nalam, Varun Li, Minhan Liu, Ming Feng, Jing Si, Jennie Huang, He NC State Univ Dept Elect & Comp Engn Raleigh NC 27606 USA NC State Univ NC State UNC Joint Dept Biomed Engn Raleigh NC 27695 USA Univ North Carolina Chapel Hill NC State UNC Joint Dept Biomed Engn Chapel Hill NC 27599 USA NC State Univ Dept Psychol Raleigh NC 27606 USA Arizona State Univ Dept Elect Comp & Energy Engn Tempe AZ 85281 USA

The tuning of robotic prosthesis control is essential to provide personalized assistance to individual prosthesis users. Emerging automatic tuning algorithms have shown promise to ease the device personalization procedure. However, very few automatic tuning algorithms consider the user preference as the tuning goal, which may limit the adoptability of the robotic prosthesis. In this study, we propose and evaluate a novel prosthesis control tuning framework for a robotic knee prosthesis, which could enable user preferred robot behavior in the device tuning process. The framework consists of 1) a User-Controlled Interface that allows the user to select their preferred knee kinematics in gait and 2) a reinforcement learning-based algorithm for tuning high-dimension prosthesis control parameters to meet the desired knee kinematics. We evaluated the performance of the framework along with usability of the developed user interface. In addition, we used the developed framework to investigate whether amputee users can exhibit a preference between different profiles during walking and whether they can differentiate between their preferred profile and other profiles when blinded. The results showed effectiveness of our developed framework in tuning 12 robotic knee prosthesis control parameters while meeting the user-selected knee kinematics. A blinded comparative study showed that users can accurately and consistently identify their preferred prosthetic control knee profile. Further, we preliminarily examined gait biomechanics of the prosthesis users when walking with different prosthesis control and did not find clear difference between walking with preferred prosthesis control and when walking with normative gait control parameters. This study may inform future translation of this novel prosthesis tuning framework for home or clinical use.

关键词： Prosthetics Knee Robots Tuning Legged locomotion Impedance Wearable robots Robotic knee prosthesis human-in-the-loop optimization user preference reinforcement learning user-controlled interface

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Stiffness optimization based on muscle fatigue and muscle synergy for passive waist assistive exoskeleton

ROBOTIC INTELLIGENCE AND AUTOMATION

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ROBOTIC INTELLIGENCE AND AUTOMATION 2023年第3期43卷 209-224页

作者： Pang, Muye Li, Ruiqing Ding, Ying Tang, Biwei Luo, Jing Xiang, Kui Wuhan Univ Technol Sch Automat Wuhan Peoples R China

PurposeThis paper aims to optimize the stiffness coefficient of the elastic element for a passive waist assistive exoskeleton (WAE). There is a tradeoff between stiffness coefficient of elastic element of the exoskeleton and work efficiency of the wearer, because elastic element affects original bending motion of the wearer and the force requirement of erector spinae is compensated by the other muscles. However, there is no accepted conclusion on how to determine the proper stiffness coefficient, especially with respected to the effort of groups of muscles, not only erector spinae. Design/methodology/approachIn this study, a consumption indicator based on muscle fatigue of seven muscles is proposed and a Bayesian-based human-in-the-loop optimization approach is adopted to optimize the stiffness coefficient. Pneumatic artificial muscles are used to replace the mechanical elastic part to adjust the assistive force automatically. The proposed optimization method is verified by the way of load-lifting experiments with three different conditions: without exoskeleton, with fixed air pressure and with optimized air pressure. Six subjects participated in the experiment and each experiment is performed in different day. FindingsCompared with No-Exo condition and static assistance condition, the parameter-optimized waist exoskeleton averagely reduces muscle fatigue of the six subjects by 45.30 & PLUSMN;29.14% and 30.94 & PLUSMN;30.29%, respectively. The experimental results indicate that the proposed method is effective to reduce muscle fatigue during stoop lifting task. Originality/valueThis paper provides a novel cost function construction method based on muscle fatigue and muscle synergy for passive WAE stiffness optimization.

关键词： Passive waist assistive exoskeleton Muscle fatigue Muscle synergy Stoop lifting human-in-the-loop optimization Robotics Programming

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Framework for Personalizing Wearable Devices Using Real-Time Physiological Measures

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IEEE ACCESS 2023年 11卷 81389-81400页

作者： Kantharaju, Prakyath Vakacherla, Sai Siddarth Jacobson, Michael Jeong, Hyeongkeun Mevada, Meet Nikunj Zhou, Xingyuan Major, Matthew J. Kim, Myunghee Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA Univ Illinois Dept Elect & Comp Engn Chicago IL USA Jesse Brown VA Med Ctr Chicago IL USA

Personalizing wearable robots by incorporating user physiological feedback can improve energy efficiency and comfort. However, many current personalization methods are specific to a particular device and often require reprogramming, making them less accessible. In this study, we present an open-source, device-independent personalization framework that allows for human-in-the-loop optimization. This modular framework includes cost functions and optimization algorithms that use a physiological response to optimize wearable robot parameters. We tested this framework in three case studies involving diverse subjects and wearable robots. The first case study focused on personalizing an ankle-foot prosthesis using indirect calorimetry feedback. This resulted in a 5.3% and 18.1% reduction in metabolic cost for walking for two individuals with transtibial amputation, compared to the weight-based assistance. The second case study personalized a robotic ankle exoskeleton for three different walking speeds using indirect calorimetry feedback for two subjects. The metabolic cost was reduced by 1%, 2%, and 5.8% for one subject and by 20.8%, 1.9%, and 19% for the other subject, compared to a generic assistance condition for increasing speeds. The third case study personalized gait parameters, specifically step frequency, using an electrocardiogram (ECG)-based cost function along with an optimization algorithm variant, resulting in a 43% reduction in optimization time for one non-disabled subject. These case studies suggest that our personalization framework can effectively personalize wearable robot parameters and potentially enhance assistance benefits.

关键词： Wearable device personalization human-in-the-loop optimization exoskeleton prosthesis metabolic cost electrocardiogram

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A Meta-Bayesian Approach for Rapid Online Parametric optimization for Wrist-based Interactions 24

A Meta-Bayesian Approach for Rapid Online Parametric Optimiz...

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CHI Conference on human Factors in Computing Sytems (CHI)

作者： Liao, Yi-Chi Desai, Ruta Pierce, Alec M. Taylor, Krista E. Benko, Hrvoje Jonker, Tanya R. Gupta, Aakar Aalto Univ Helsinki Finland Saarland Univ Saarbrucken Germany Meta Inc Fundamental AI Res FAIR Redmond WA USA Meta Inc Real Labs Res Redmond WA USA Fujitsu Res Amer Pittsburgh PA USA

ISBN: (纸本)9798400703300

Wrist-based input often requires tuning parameter settings in correspondence to between-user and between-session diferences, such as variations in hand anatomy, wearing position, posture, etc. Traditionally, users either work with predefned parameter values not optimized for individuals or undergo time-consuming calibration processes. We propose an online Bayesian optimization (BO)-based method for rapidly determining the user-specifc optimal settings of wrist-based pointing. Specifcally, we develop a meta-Bayesian optimization (meta-BO) method, difering from traditional human-in-the-loop BO: By incorporating meta-learning of prior optimization data from a user population with BO, meta-BO enables rapid calibration of parameters for new users with a handful of trials. We evaluate our method with two representative and distinct wrist-based interactions: absolute and relative pointing. On a weighted-sum metric that consists of completion time, aiming error, and trajectory quality, meta-BO improves absolute pointing performance by 22.92% and 21.35% compared to BO and manual calibration, and improves relative pointing performance by 25.43% and 13.60%.

关键词： Bayesian optimization human-in-the-loop optimization meta-Bayesian optimization meta-learning wrist-based interaction calibration target selection adaptive interface

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SwipeGANSpace: Swipe-to-Compare Image Generation via Efficient Latent Space Exploration 24

SwipeGANSpace: Swipe-to-Compare Image Generation via Efficie...

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29th ACM Annual International Conference on Intelligent User Interfaces (IUI)

作者： Nakashima, Yuto Yang, Mingzhe Baba, Yukino Univ Tokyo Tokyo Japan

ISBN: (纸本)9798400705083

Generating preferred images using generative adversarial networks (GANs) is challenging owing to the high-dimensional nature of latent space. In this study, we propose a novel approach that uses simple user-swipe interactions to generate preferred images for users. To effectively explore the latent space with only swipe interactions, we apply principal component analysis to the latent space of the StyleGAN, creating meaningful subspaces. We use a multi-armed bandit algorithm to decide the dimensions to explore, focusing on the preferences of the user. Experiments show that our method is more efficient in generating preferred images than the baseline methods. Furthermore, changes in preferred images during image generation or the display of entirely different image styles were observed to provide new inspirations, subsequently altering user preferences. This highlights the dynamic nature of user preferences, which our proposed approach recognizes and enhances.

关键词： Personalized image generation human-in-the-loop optimization Preferential Bayesian optimization

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The Effects of Incline Level on Optimized Lower-Limb Exoskeleton Assistance: A Case Series

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2022年 30卷 2494-2505页

作者： Franks, Patrick W. Bryan, Gwendolyn M. Reyes, Ricardo O'Donovan, Meghan P. Gregorczyk, Karen N. Collins, Steven H. Stanford Univ Dept Mech Engn Stanford CA 94305 USA DEVCOM Soldier Ctr Natick MA 01760 USA

For exoskeletons to be successful in real-world settings, they will need to be effective across a variety of terrains, including on inclines. While some single-joint exoskeletons have assisted incline walking, recent successes in level-ground assistance suggest that greater improvements may be possible by optimizing assistance of the whole leg. To understand how exoskeleton assistance should change with incline, we used human-in-the-loop optimization to find whole-leg exoskeleton assistance torques that minimized metabolic cost on a range of grades. We optimized assistance for three non-disabled, expert participants on 5 degree, 10 degree, and 15 degree inclines using a hip-knee-ankle exoskeleton emulator. For all assisted conditions, the cost of transport was reduced by at least 50% relative to walking in the device with no assistance, which is a large improvement to walking comparable to the benefits of whole-leg assistance on level-ground (N = 3). Optimized extension torque magnitudes and exoskeleton power increased with incline. Hip extension, knee extension and ankle plantarflexion often grew as large as allowed by comfort-based limits. Applied powers on steep inclines were double the powers applied during level-ground walking, indicating that greater exoskeleton power may be optimal in scenarios where biological powers and costs are higher. Future exoskeleton devices could deliver large improvements in walking performance across a range of inclines if they have sufficient torque and power capabilities.

关键词： Legged locomotion Exoskeletons Torque Costs Knee optimization Hip Exoskeleton walking assistance human-in-the-loop optimization metabolic cost incline

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Sample-efficient gear-ratio optimization for biomechanical energy harvester

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INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2022年第1期6卷 10-22页

作者： Kobayashi, Taisuke Ikawa, Yutaro Matsubara, Takamitsu Nara Inst Sci & Technol Div Informat Sci Grad Sch Sci & Technol Nara 6300192 Japan

The biomechanical energy harvester is expected to harvest the electric energies from human motions. A tradeoff between harvesting energy and keeping the user's natural movements should be balanced via optimization techniques. In previous studies, the hardware itself has been specialized in advance for a single task like walking with constant speed on a flat. A key ingredient is Continuous Variable Transmission (CVT) to extend it applicable for multiple tasks. CVT could continuously adjust its gear ratio to balance the tradeoff for each task;however, such gear-ratio optimization problem remains open yet since its optimal solution may depend on the user, motion, and environment. Therefore, this paper focuses on a framework for data-driven optimization of a gear ratio in a CVT-equipped biomechanical energy harvester. Since the data collection requires a heavy burden on the user, we have to optimize the gear ratio for each task in the shortest possible time. To this end, our framework is designed sample-efficiently based on the fact that the user encounters multiple tasks, which are with similarities with each other. Specifically, our framework employs multi-task Bayesian optimization to reuse the optimization results of the similar tasks previously optimized by finding their similarities. Through experiments, we confirmed that, for each task, the proposed framework could achieve the optimal gear ratio of around 50 % faster than one by random search, and that takes only around 20 min. Experimental results also suggested that the optimization can be accelerated by actively exploiting similarities with previously optimized tasks.

关键词： Energy harvesting Continuous variable transmission human-in-the-loop optimization

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Intuitive and versatile bionic legs: a perspective on volitional control

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FRONTIERS IN NEUROROBOTICS 2024年 18卷 1410760页

作者： Voss, Matthias Koelewijn, Anne D. Beckerle, Philipp Friedrich Alexander Univ Erlangen Nurnberg Chair Autonomous Syst & Mechatron Dept Elect Engn Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn Erlangen Germany

Active lower limb prostheses show large potential to offer energetic, balance, and versatility improvements to users when compared to passive and semi-active devices. Still, their control remains a major development challenge, with many different approaches existing. This perspective aims at illustrating a future leg prosthesis control approach to improve the everyday life of prosthesis users, while providing a research road map for getting there. Reviewing research on the needs and challenges faced by prosthesis users, we argue for the development of versatile control architectures for lower limb prosthetic devices that grant the wearer full volitional control at all times. To this end, existing control approaches for active lower limb prostheses are divided based on their consideration of volitional user input. The presented methods are discussed in regard to their suitability for universal everyday control involving user volition. Novel combinations of established methods are proposed. This involves the combination of feed-forward motor control signals with simulated feedback loops in prosthesis control, as well as online optimization techniques to individualize the system parameters. To provide more context, developments related to volitional control design are touched on.

关键词： lower limb prostheses bionic legs voluntary control electromyography human-in-the-loop optimization

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"Rather Solve the Problem from Scratch": Gamesploring human-Machine Collaboration for Optimizing the Debris Collection Problem 22

"Rather Solve the Problem from Scratch": Gamesploring Human-...

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27th Annual International Conference on Intelligent User Interfaces (ACM IUI)

作者： Ulusan, Aybike Narayan, Uttkarsh Snodgrass, Sam Ergun, Ozlem Harteveld, Casper Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781450391443

Optimizing operations on critical infrastructure networks is key to reducing the impact of disruptive events. In this paper, we explore the potential of having humans and algorithms work together to address this difficult task. For this purpose, we use a gamified experiment to build and assess this potential in the context of the debris collection problem (i.e., "gamesploring"). We developed a digital game where players can request the help of the computer while facing a multi-objective problem of assigning contractors to road segments for clearing debris in a disaster area. Through a within-subjects experimental study, we assessed how players optimized under various circumstances (e.g., initial solution vs. from scratch) compared to the computer on its own. The results are both surprising as well as insightful: they suggest that human-machine collaboration is indeed beneficial but also that more work is needed on how to appropriately guide this form of collaboration.

关键词： human-machine collaboration human-in-the-loop optimization gamified experiments serious games debris collection problem gamesploring

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