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human-in-the-loop optimization of wearable device parameters using an EMG-based objective function

WEARABLE TECHNOLOGIES 2024年 5卷 e15-e15页

作者： Diaz, Maria Alejandra De Bock, Sander Beckerle, Philipp Babic, Jan Verstraten, Tom De Pauw, Kevin Vrije Univ Brussel BruBot B-1050 Brussels Belgium Vrije Univ Brussel Human Physiol & Sports Physiotherapy Res Grp B-1050 Brussels Belgium Friedrich Alexander Univ Erlangen Nurnberg Inst Autonomous Syst & Mechatron Dept Elect Engn D-91052 Erlangen Germany Friedrich Alexander Univ Erlangen Nurnberg Dept Artificial Intelligence Biomed Engn D-91052 Erlangen Germany Jozef Stefan Inst Dept Automat Biocybernet & Robot Lab Neuromech & Biorobot Ljubljana 1000 Slovenia Univ Ljubljana Fac Elect Engn Ljubljana 1000 Slovenia Vrije Univ Brussel Robot & Multibody Mech Res Grp B-1050 Brussels Belgium Flanders Make B-1050 Brussels Belgium

Advancements in wearable robots aim to improve user motion, motor control, and overall experience by minimizing energetic cost (EC). However, EC is challenging to measure and it is typically indirectly estimated through respiratory gas analysis. This study introduces a novel EMG-based objective function that captures individuals' natural energetic expenditure during walking. The objective function combines information from electromyography (EMG) variables such as intensity and muscle synergies. First, we demonstrate the similarity of the proposed objective function, calculated offline, to the EC during walking. Second, we minimize and validate the EMG-based objective function using an online Bayesian optimization algorithm. The walking step frequency is chosen as the parameter to optimize in both offline and online approaches in order to simplify experiments and facilitate comparisons with related research. Compared to existing studies that use EC as the objective function, results demonstrated that the optimization of the presented objective function reduced the number of iterations and, when compared with gradient descent optimization strategies, also reduced convergence time. Moreover, the algorithm effectively converges toward an optimal step frequency near the user's preferred frequency, positively influencing EC reduction. The good correlation between the estimated objective function and measured EC highlights its consistency and reliability. Thus, the proposed objective function could potentially optimize lower limb exoskeleton assistance and improve user performance and human-robot interaction without the need for challenging respiratory gas measurements.

关键词： human-robot interaction optimization performance augmentation human-in-the-loop optimization

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human-in-the-loop optimization of Exoskeleton Assistance Via Online Simulation of Metabolic Cost

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IEEE TRANSACTIONS ON ROBOTICS 2022年第3期38卷 1410-1429页

作者： Gordon, Daniel F. N. McGreavy, Christopher Christou, Andreas Vijayakumar, Sethu Univ Edinburgh Sch Informat Edinburgh EH8 9AB Midlothian Scotland Alan Turing Inst London NW1 2DB England

Many assistive robotic devices have been developed to augment or assist human locomotion. Despite advancements in design and control algorithms, this task remains challenging. human walking strategies are unique and complex, and assistance strategies based on the dynamics of unassisted locomotion typically offer only modest reductions to the metabolic cost of walking. Recently, human-in-the-loop (HIL) methodologies have been used to identify subject-specific assistive strategies, which offer significant improvements to energy savings. However, current implementations suffer from long measurement times, necessitating the use of low-dimensional control parameterizations, and possibly requiring multiday collection protocols to avoid subject fatigue. We present a HIL methodology, which optimizes the assistive torques provided by a powered hip exoskeleton. Using musculoskeletal modeling, we are able to evaluate simulated metabolic rate online. We applied our methodology to identify assistive torque profiles for seven subjects walking on a treadmill, and found greater reductions to metabolic cost when compared to generic or off-the-shelf controllers. In a secondary investigation, we directly compare simulated and measured metabolic rate for three subjects experiencing a range of assistance levels. The time investment required to identify assistance strategies via our protocol is significantly lower when compared to existing protocols relying on calorimetry. In the future, frameworks such as these could be used to enable shorter HIL protocols or exploit more complex control parameterizations for greater energy savings.

关键词： Solid modeling Exoskeletons Torque Computational modeling optimization Legged locomotion Pelvis Assistive robotics exoskeletons human-in-the-loop optimization musculoskeletal modeling

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A Relationship Model Between Optimized Exoskeleton Assistance and Gait Conditions Improves Multi-Gait human-in-the-loop optimization Performance

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2024年 32卷 4304-4313页

作者： Jing, Zhibo Han, Hong Han, Jianda Zhang, Juanjuan Nankai Univ Coll Artificial Intelligence Smart Sensing Interdisciplinary Sci Ctr Tianjin 300350 Peoples R China Nankai Univ Smart Sensing Interdisciplinary Sci Ctr Sch Mat Sci & Engn Tianjin 300350 Peoples R China

Exoskeletons have been shown be able to reduce metabolic cost of human walking. Determining the suitable assistance is challenging due to individual variability in response and the need for tailored assistance across different gait conditions. human-in-the-loop (HIL) optimization has been proposed to address this issue, but current implementations often suffer from prolonged optimization cycles. In this study, we establish a model capturing the relationship between optimized assistance parameters and gait conditions (speed and slope) through a series of HIL optimization experiments spanning various gait conditions. The validation results showed that the desired assistance torque calculated by the model closely aligns with the assistance torque obtained through HIL optimization, and the calculated assistance reduced metabolic cost by 11.95% (p<0.001) and RMS soleus activity by 22.28% (p=0.049) compared to the case without assistance. The optimized assistance using model values for initialization after two generations significantly reduced metabolic cost by 12.1% (p<0.001) and RMS soleus activity by 24.8% (p=0.033), and produced larger benefits than using the empirical values after four generations, with a 50% increase in efficiency. Results suggested that the relationship model can help to improve multi-gait exoskeleton assistance customization efficiency and effectiveness both by optimization parameter initialization and direct parameter assertion. These advancements expand the applicability of HIL optimization and improve the effectiveness of exoskeleton assistance.

关键词： human-in-the-loop optimization exoske-leton assistance relationship model multi-gait conditions human-in-the-loop optimization exoske-leton assistance relationship model multi-gait conditions

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human-in-the-loop optimization for Artificial Intelligence Algorithms

Human-in-the-Loop Optimization for Artificial Intelligence A...

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19th International Conference on Service-Oriented Computing (ICSOC)

作者： Farhood, Helia Saberi, Morteza Najafi, Mohammad Macquarie Univ Sch Comp Sydney NSW Australia Univ Technol Sydney Sch Informat Syst & Modelling Sydney NSW Australia Univ Technol Sydney Sch Elect & Data Engn Sydney NSW Australia

ISBN: (纸本)9783031141355;9783031141348

Numerous organisations use artificial intelligence algorithm-based products in their different activities. These solutions help with a wide range of jobs, from operational task automation to augmentation-based strategic decision making. The users' trust in the truth and fairness of a product's outputs must be built before it can be completely integrated and embedded in an organization's daily functioning. They would be burdened with more work if Artificial Intelligence (AI) products did not have this feature. A human-in-loop decision-making process is important for building confidence and producing a successful AI-powered solution. In this research, a novel interactive system was created to explore the behaviour of AI-powered products. When designing our framework, we considered the necessity of integrating a human-in-the-loop technique in the design stage, something that had been missed in prior research of a comparable scale. The proposed software can optimise and monitor the AI-powered product process and outputs to involve people directly in the optimisation loop to identify and avoid likely and diverse failures. The Local Interpretable Model-agnostic Explanations (LIME) heatmap was utilised to illustrate decision-making features and mistake details more effectively throughout the improvement phase. The literature highlights the need of taking these issues into account throughout the design stage of an AI-powered product. This article describes how a human-in-loop AI-powered product is created by combining technologies from the AI, risk management, and human-computation domains. The designed system is based on deep learning as its decision-making engine, LIME as its approach explanation module, and the human aspect of knowledge workers. For real-world applications, we show how the created system improves product dependability and understandability by using real data and benchmark datasets.

关键词： human-in-the-loop optimization Artificial intelligence improvement LIME Knowledge workers

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Preference-Based human-in-the-loop optimization for Perceived Realism of Haptic Rendering

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IEEE TRANSACTIONS ON HAPTICS 2023年第4期16卷 470-476页

作者： Catkin, Bilal Patoglu, Volkan Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkiye

The fidelity of haptic rendering is characterized by the perceived realism capturing the level of similarity to a corresponding tangible object. Perceived realism depends on the musculoskeletal, mental, and perceptual properties of the individuals that manipulate the system. human-in-the-loop (HiL) studies provide a feasible means for the concurrent optimization of the performance of the overall haptic rendering process, as the physical limitations of the hardware, the factors affecting the fidelity of the rendering algorithm, and the limitations of human action and perception can all be considered simultaneously. In this study, we propose the use of preference-based HiL optimization techniques based on sample-efficient Bayesian optimization algorithms and qualitative pairwise comparisons to maximize the perceived realism of haptic rendering tasks. We present two HiL optimization studies that maximize the perceived realism of spring and friction rendering and validate our results by comparing the HiL-optimized rendering models with expert-tuned nominal models. We show that the system parameters can effectively be optimized within a reasonable amount of time using a preference-based HiL optimization approach. Furthermore, we demonstrate that the approach provides an efficient means of studying the effect of haptic rendering parameters on perceived realism by capturing the interactions among the parameters, even for relatively high dimensional parameter spaces.

关键词： human-in-the-loop optimization preference-based Bayesian optimization perceived realism haptic rendering

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Continual human-in-the-loop optimization 25

Continual Human-in-the-Loop Optimization

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Liao, Yi-Chi Streli, Paul Li, Zhipeng Gebhardt, Christoph Holz, Christian Department of Computer Science Eth Zurich Zurich Switzerland

ISBN: (纸本)9798400713941

Optimal input settings vary across users due to differences in motor abilities and personal preferences, which are typically addressed by manual tuning or calibration. Although human-in-the-loop optimization has the potential to identify optimal settings during use, it is rarely applied due to its long optimization process. A more efficient approach would continually leverage data from previous users to accelerate optimization, exploiting shared traits while adapting to individual characteristics. We introduce the concept of Continual human-in-the-loop optimization and a Bayesian optimization-based method that leverages a Bayesian-neural-network surrogate model to capture population-level characteristics while adapting to new users. We propose a generative replay strategy to mitigate catastrophic forgetting. We demonstrate our method by optimizing virtual reality keyboard parameters for text entry using direct touch, showing reduced adaptation times with a growing user base. Our method opens the door for next-generation personalized input systems that improve with accumulated experience. © 2025 Copyright held by the owner/author(s).

关键词： Bayesian optimization Continual learning continual optimization human-in-the-loop optimization lifelong learning meta-learning mid-air keyboard typing virtual reality

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Reducing the muscle activity of walking using a portable hip exoskeleton based on human-in-the-loop optimization

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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2023年 11卷 1006326页

作者： Xu, Linghui Liu, Xiaoguang Chen, Yuting Yu, Linfan Yan, Zehao Yang, Canjun Zhou, Congcong Yang, Wei Zhejiang Univ Ningbo Innovat Ctr Ningbo Peoples R China Zhejiang Univ Sch Mech Engn Hangzhou Peoples R China Ningbo Rehabil Hosp Spinal Cord Injury Rehabil Dept Ningbo Peoples R China Yanshan Univ Hebei Heavy Machinery Fluid Power Transmiss & Cont Qinhuangdao Peoples R China Zhejiang Univ Sir Run Run Shaw Hosp Sch Med Hangzhou Peoples R China

Introduction: human-in-the-loop optimization has made great progress to improve the performance of wearable robotic devices and become an effective customized assistance strategy. However, a lengthy period (several hours) of continuous walking for iterative optimization for each individual makes it less practical, especially for disabled people, who may not endure this ***: In this paper, we provide a muscle-activity-based human-in-the-loop optimization strategy that can reduce the time spent on collecting biosignals during each iteration from around 120 s to 25 s. Both Bayesian and Covariance Matrix Adaptive Evolution Strategy (CMA-ES) optimization algorithms were adopted on a portable hip exoskeleton to generate optimal assist torque patterns, optimizing rectus femoris muscle activity. Four volunteers were recruited for exoskeleton-assisted walking *** and Discussion: As a result, using human-in-the-loop optimization led to muscle activity reduction of 33.56% and 41.81% at most when compared to walking without and with the hip exoskeleton, respectively. Furthermore, the results of human-in-the-loop optimization indicate that three out of four participants achieved superior outcomes compared to the predefined assistance patterns. Interestingly, during the optimization stage, the order of the two typical optimizers, i.e., Bayesian and CMA-ES, did not affect the optimization results. The results of the experiment have confirmed that the assistance pattern generated by muscle-activity-based human-in-the-loop strategy is superior to predefined assistance patterns, and this strategy can be achieved more rapidly than the one based on metabolic cost.

关键词： muscle activity portable hip exoskeleton human-in-the-loop optimization walking assist surface electromyography (SEMG)

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Improving Walking Economy With an Ankle Exoskeleton Prior to human-in-the-loop optimization

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FRONTIERS IN NEUROROBOTICS 2022年 15卷 797147页

作者： Wang, Wei Chen, Jianyu Ding, Jianquan Zhang, Juanjuan Liu, Jingtai Nankai Univ Inst Robot & Automat Informat Syst Coll Artificial Intelligence Tianjin Peoples R China Nankai Univ Tianjin Key Lab Intelligent Robot Tianjin Peoples R China

Lower limb robotic exoskeletons have shown the capability to enhance human locomotion for healthy individuals or to assist motion rehabilitation and daily activities for patients. Recent advances in human-in-the-loop optimization that allowed for assistance customization have demonstrated great potential for performance improvement of exoskeletons. In the optimization process, subjects need to experience multiple types of assistance patterns, thus, leading to a long evaluation time. Besides, some patterns may be uncomfortable for the wearers, thereby resulting in unpleasant optimization experiences and inaccurate outcomes. In this study, we investigated the effectiveness of a series of ankle exoskeleton assistance patterns on improving walking economy prior to optimization. We conducted experiments to systematically evaluate the wearers' biomechanical and physiological responses to different assistance patterns on a lightweight cable-driven ankle exoskeleton during walking. We designed nine patterns in the optimization parameters range which varied peak torque magnitude and peak torque timing independently. Results showed that metabolic cost of walking was reduced by 17.1 +/- 7.6% under one assistance pattern. Meanwhile, soleus (SOL) muscle activity was reduced by 40.9 +/- 19.8% with that pattern. Exoskeleton assistance changed maximum ankle dorsiflexion and plantarflexion angle and reduced biological ankle moment. Assistance pattern with 48% peak torque timing and 0.75 N center dot m center dot kg(-1) peak torque magnitude was effective in improving walking economy and can be selected as an initial pattern in the optimization procedure. Our results provided a preliminary understanding of how humans respond to different assistances and can be used to guide the initial assistance pattern selection in the optimization.

关键词： wearable device ankle exoskeleton walking assistance pattern biomechanics human-in-the-loop optimization walking economy

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Engagement Enhancement Based on human-in-the-loop optimization for Neural Rehabilitation

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FRONTIERS IN NEUROROBOTICS 2020年 14卷 596019页

作者： Wang, Jiaxing Wang, Weiqun Ren, Shixin Shi, Weiguo Hou, Zeng-Guang Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Beijing Peoples R China

Enhancing patients' engagement is of great benefit for neural rehabilitation. However, physiological and neurological differences among individuals can cause divergent responses to the same task, and the responses can further change considerably during training;both of these factors make engagement enhancement a challenge. This challenge can be overcome by training task optimization based on subjects' responses. To this end, an engagement enhancement method based on human-in-the-loop optimization is proposed in this paper. Firstly, an interactive speed-tracking riding game is designed as the training task in which four reference speed curves (RSCs) are designed to construct the reference trajectory in each generation. Each RSC is modeled using a piecewise function, which is determined by the starting velocity, transient time, and end velocity. Based on the parameterized model, the difficulty of the training task, which is a key factor affecting the engagement, can be optimized. Then, the objective function is designed with consideration to the tracking accuracy and the surface electromyogram (sEMG)-based muscle activation, and the physical and physiological responses of the subjects can consequently be evaluated simultaneously. Moreover, a covariance matrix adaption evolution strategy, which is relatively tolerant of both measurement noises and human adaptation, is used to generate the optimal parameters of the RSCs periodically. By optimization of the RSCs persistently, the objective function can be maximized, and the subjects' engagement can be enhanced. Finally, the performance of the proposed method is demonstrated by the validation and comparison experiments. The results show that both subjects' sEMG-based motor engagement and electroencephalography based neural engagement can be improved significantly and maintained at a high level.

关键词： human-in-the-loop optimization EEG based neural engagement sEMG based muscle activation tracking accuracy neural rehabilitation

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Biomechanics-Based User-Adaptive Variable Impedance Control for Enhanced Physical human-Robot Interaction Using Bayesian optimization

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2025年第2期7卷

作者： Zahedi, Fatemeh Lee, Hyunglae Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA

This paper presents a biomechanics-based, user-adaptive variable impedance controller designed to enhance the performance of coupled human-robot systems during motion. The controller integrates the biomechanical characteristics of human limbs and dynamically adjusts the robotic impedance parameters-specifically damping, stiffness, and equilibrium trajectory-based on real-time estimations of the user's intent and direction of motion. The primary goal is to minimize the energy expenditure of the coupled human-robot system while maintaining system passivity. To address uncertainties in human behavior and noisy observations, the controller employs Bayesian optimization combined with a Gaussian process. To validate the proposed approach, human experiments are conducted using a standard robotic arm manipulator. The results demonstrate that the controller eliminates the need for manual parameter tuning, a process that is typically time-consuming. A comparative analysis against two variable impedance controllers without user-adaptive parameter adjustments reveal significant benefits, with the controller improving combined performance metrics-such as accuracy, speed, user effort, and smoothness-by over 13%. Notably, all participants in the study preferred the optimized controller over the alternatives. These findings highlight the effectiveness of the biomechanics-based, user-adaptive variable impedance control approach and its potential to enhance physical human-robot interaction in various applications that involve repetitive or continuous motion. This article introduces a biomechanics-based, user-adaptive variable impedance controller aimed at enhancing the overall performance of coupled human-robot systems during motion. The improvements span accuracy, speed, user effort, smoothness, and user preference during physical human-robot interaction (pHRI). To address the uncertainties of human behavior and noisy observations, the controller employs Bayesian optimization with a

关键词： assistive robots human-in-the-loop optimization interaction control physical human-robot interaction variable impedance control

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