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35th Annual ACM Symposium on User Interface Software and Technology (UIST)

作者： Yamamoto, Kenta Koyama, Yuki Ochiai, Yoichi Univ Tsukuba Tsukuba Japan Natl Inst Adv Ind Sci & Technol AIST Tokyo Japan Univ Tsukuba R&D Ctr Digital Nat Pixie Dust Technol Inc Tsukuba Japan

ISBN: (纸本)9781450393201

It is important for photographers to have the best possible lighting confguration at the time of shooting;otherwise, they need post-processing on images, which may cause artifacts and deterioration. Thus, photographers often struggle to fnd the best possible lighting confguration by manipulating lighting devices, including light sources and modifers, in a trial-and-error manner. In this paper, we propose a novel computational framework to support photographers. This framework assumes that every lighting device is programmable;that is, its adjustable parameters (e.g., orientation, intensity, and color temperature) can be set using a program. Using our framework, photographers do not need to learn how the parameter values afect the resulting lighting, and even do not need to determine the strategy of the trial-and-error process;instead, photographers need only concentrate on evaluating which lighting confguration is more desirable among options suggested by the system. The framework is enabled by our novel photographer-in-the-loop Bayesian optimization, which is sample-efcient (i.e., the number of required evaluation steps is small) and which can also be guided by providing a rough painting of the desired lighting confguration if any. We demonstrate how the framework works in both simulated virtual environments and a physical environment, suggesting that it could fnd pleasing lighting confgurations quickly in around 10 iterations. Our user study suggests that the framework enables the photographer to concentrate on the look of captured images rather than the parameters, compared with the traditional manual lighting workfow.

关键词： lighting design human-in-the-loop optimization Bayesian optimization photography

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Optimizing Exoskeleton Assistance for Faster Self-Selected Walking

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2021年 29卷 786-795页

作者： Song, Seungmoon Collins, Steven H. Stanford Univ Dept Mech Engn Stanford CA 94305 USA

Self-selected walking speed is an important aspect of mobility. Exoskeletons can increase walking speed, but the mechanisms behind these changes and the upper limits on performance are unknown. human-in-the-loop optimization is a technique for identifying exoskeleton characteristics that maximize the benefits of assistance, which has been critical to achieving large improvements in energy economy. In this study, we used human-in-the-loop optimization to test whether large improvements in self-selected walking speed are possible through ankle exoskeleton assistance. Healthy participants (N =10) were instructed to walk at a comfortable speed on a self-paced treadmill while wearing tethered ankle exoskeletons. An algorithm sequentially applied different patterns of exoskeleton torque and estimated the speed-optimal pattern, which was then evaluated in separate trials. With torque optimized for speed, participants walked 42% faster than in normal shoes (1.83 ms(-1) vs. 1.31 ms(-1);Tukey HSD, p = 4 x 10(-8)), with speed increases ranging from 6% to 91%. Participants walked faster with speed-optimized torque than with torque optimized for energy consumption (1.55 ms(-1)) or torque chosen to induce slow walking (1.18 ms(-1)). Gait characteristics with speed-optimized torque were highly variable across participants, and changes in metabolic cost of transport ranged from a 31% decrease to a 78% increase, with a decrease of 2% on average. These results demonstrate that ankle exoskeletons can facilitate large increases in self-selected walking speed, which could benefit older adults and others with reduced walking speed.

关键词： Legged locomotion Exoskeletons Torque optimization Particle measurements Atmospheric measurements Real-time systems Exoskeleton gait assistance human-in-the-loop optimization walking speed

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Optimizing exoskeleton assistance to improve walking speed and energy economy for older adults

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2024年第1期21卷 1页

作者： Lakmazaheri, Ava Song, Seungmoon Vuong, Brian B. Biskner, Blake Kado, Deborah M. Collins, Steven H. Stanford Univ Dept Mech Engn Stanford CA 94305 USA Northeastern Univ Dept Mech & Ind Engn Boston MA USA Vet Affairs Geriatr Res Educ & Clin Ctr Palo Alto CA USA Stanford Univ Dept Med Stanford CA USA

Background Walking speed and energy economy tend to decline with age. Lower-limb exoskeletons have demonstrated potential to improve either measure, but primarily in studies conducted on healthy younger adults. Promising techniques like optimization of exoskeleton assistance have yet to be tested with older populations, while speed and energy consumption have yet to be simultaneously optimized for any *** We investigated the effectiveness of human-in-the-loop optimization of ankle exoskeletons with older adults. Ten healthy adults > 65 years of age (5 females;mean age: 72 +/- 3 yrs) participated in approximately 240 min of training and optimization with tethered ankle exoskeletons on a self-paced treadmill. Multi-objective human-in-the-loop optimization was used to identify assistive ankle plantarflexion torque patterns that simultaneously improved self-selected walking speed and metabolic rate. The effects of optimized exoskeleton assistance were evaluated in separate *** Optimized exoskeleton assistance improved walking performance for older adults. Both objectives were simultaneously improved;self-selected walking speed increased by 8% (0.10 m/s;p = 0.001) and metabolic rate decreased by 19% (p = 0.007), resulting in a 25% decrease in energetic cost of transport (p = 8e-4) compared to walking with exoskeletons applying zero torque. Compared to younger participants in studies optimizing a single objective, our participants required lower exoskeleton torques, experienced smaller improvements in energy use, and required more time for motor *** Our results confirm that exoskeleton assistance can improve walking performance for older adults and show that multiple objectives can be simultaneously addressed through human-in-the-loop optimization.

关键词： Exoskeletons Aging Gait speed Energy cost human-in-the-loop optimization Motor learning

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human-prosthesis cooperation: combining adaptive prosthesis control with visual feedback guided gait

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2022年第1期19卷 140-140页

作者： Fylstra, Bretta L. Lee, I-Chieh Li, Minhan Lewek, Michael D. Huang, He North Carolina State Univ Joint Dept Biomed Engn Raleigh NC 27695 USA Univ North Carolina Chapel Hill Joint Dept Biomed Engn Chapel Hill NC 27599 USA Univ N Carolina Div Phys Therapy Chapel Hill NC 27599 USA

BackgroundPersonalizing prosthesis control is often structured as human-in-the-loop optimization. However, gait performance is influenced by both human control and intelligent prosthesis control. Hence, we need to consider both human and prosthesis control, and their cooperation, to achieve desired gait patterns. In this study, we developed a novel paradigm that engages human gait control via user-fed visual feedback (FB) of stance time to cooperate with automatic prosthesis control tuning. Three initial questions were studied: (1) does user control of gait timing (via visual FB) help the prosthesis tuning algorithm to converge faster? (2) in turn, does the prosthesis control influence the user's ability to reach and maintain the target stance time defined by the feedback? and (3) does the prosthesis control parameters tuned with extended stance time on prosthesis side allow the user to maintain this potentially beneficial behavior even after feedback is removed (short- and long-term retention)? MethodsA reinforcement learning algorithm was used to achieve prosthesis control to meet normative knee kinematics in walking. A visual FB system cued the user to control prosthesis-side stance time to facilitate the prosthesis tuning goal. Seven individuals without amputation (AB) and four individuals with transfemoral amputation (TFA) walked with a powered knee prosthesis on a treadmill. Participants completed prosthesis auto-tuning with three visual feedback conditions: no FB, self-selected stance time FB (SS FB), and increased stance time FB (Inc FB). The retention of FB effects was studied by comparing the gait performance across three different prosthesis controls, tuned with different visual FB. Results(1) human control of gait timing reduced the tuning duration in individuals without amputation, but not for individuals with TFA. (2) The change of prosthesis control did not influence users' ability to reach and maintain the visual FB goal. (3) All participants increas

关键词： human-in-the-loop optimization Transfemoral amputee Powered prosthesis tuning Visual feedback Gait

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Characterizing the relationship between peak assistance torque and metabolic cost reduction during running with ankle exoskeletons

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2022年第1期19卷 46-46页

作者： Miller, Delaney E. Tan, Guan Rong Farina, Emily M. Sheets-Singer, Alison L. Collins, Steven H. Stanford Univ Dept Mech Engn Stanford CA 94305 USA Nike Inc Sports Res Lab Beaverton OR USA

Background Reducing the energy cost of running with exoskeletons could improve enjoyment, reduce fatigue, and encourage participation among novice and ageing runners. Previously, tethered ankle exoskeleton emulators with offboard motors were used to greatly reduce the energy cost of running with powered ankle plantarflexion assistance. Through a process known as "human-in-the-loop optimization", the timing and magnitude of assistance torque was optimized to maximally reduce metabolic cost. However, to achieve the maximum net benefit in energy cost outside of the laboratory environment, it is also necessary to consider the tradeoff between the magnitude of device assistance and the metabolic penalty of carrying a heavier, more powerful exoskeleton. Methods In this study, tethered ankle exoskeleton emulators were used to characterize the effect of peak assistance torque on metabolic cost during running. Three recreational runners participated in human-in-the-loop optimization at four fixed peak assistance torque levels to obtain their energetically optimal assistance timing parameters at each level. Results We found that the relationship between metabolic rate and peak assistance torque was nearly linear but with diminishing returns at higher torque magnitudes, which is well-approximated by an asymptotic exponential function. At the highest assistance torque magnitude of 0.8 Nm/kg, participants' net metabolic rate was 24.8 +/- 2.3% (p = 4e-6) lower than running in the unpowered devices. Optimized timing of peak assistance torque was as late as allowed during stance (80% of stance) and optimized timing of torque removal was at toe-off (100% of stance);similar assistance timing was preferred across participants and torque magnitudes. Conclusions These results allow exoskeleton designers to predict the energy cost savings for candidate devices with different assistance torque capabilities, thus informing the design of portable ankle exoskeletons that maximize net metabol

关键词： Exoskeleton Augmentation Running human-in-the-loop optimization Metabolic cost

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human-in-the-loop Differential Subspace Search in High-Dimensional Latent Space

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ACM TRANSACTIONS ON GRAPHICS 2020年第4期39卷 85:1-85:15页

作者： Chiu, Chia-Hsing Koyama, Yuki Lai, Yu-Chi Igarashi, Takeo Yue, Yonghao Natl Taiwan Univ Sci & Technol Taipei Taiwan Natl Inst Adv Ind Sci & Technol Tokyo Japan Univ Tokyo Tokyo Japan Aoyama Gakuin Univ AGU Tokyo Japan T4-305-1 43Sec 4Keelung Rd Taipei 106 Taiwan Cent 2 1-1-1 Umezono Tsukuba Ibaraki 3058568 Japan Sci Bldg 7-303 7-3-1 HongoBunkyo Ku Tokyo 1130033 Japan O-525 Fuchinobe 5-10-1Chuo Ku Sagamihara Kanagawa 2525258 Japan

Generative models based on deep neural networks often have a high-dimensional latent space, ranging sometimes to a few hundred dimensions or even higher, which typically makes them hard for a user to explore directly. We propose differential subspace search to allow efficient iterative user exploration in such a space, without relying on domain- or data-specific assumptions. We develop a general framework to extract low-dimensional subspaces based on a local differential analysis of the generative model, such that a small change in such a subspace would provide enough change in the resulting data. We do so by applying singular value decomposition to the Jacobian of the generative model and forming a subspace with the desired dimensionality spanned by a given number of singular vectors stochastically selected on the basis of their singular values, to maintain ergodicity. We use our framework to present ID subspaces to the user via a ID slider interface. Starting from an initial location, the user finds a new candidate in the presented 1D subspace, which is in turn updated at the new candidate location. This process is repeated until no further improvement can he made. Numerical simulations show that our method can better optimize synthetic black-box objective functions than the alternatives that we tested. Furthermore, we conducted a user study using complex generative models and the results show that our method enables more efficient exploration of high-dimensional latent spaces than the alternatives.

关键词： human-in-the-loop optimization dimensionality reduction generative models

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Optimized hip-knee-ankle exoskeleton assistance reduces the metabolic cost of walking with worn loads

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2021年第1期18卷 161-161页

作者： Bryan, Gwendolyn M. Franks, Patrick W. Song, Seungmoon Reyes, Ricardo O'Donovan, Meghan P. Gregorczyk, Karen N. Collins, Steven H. Stanford Univ Dept Mech Engn Stanford CA 94305 USA US Army Natick Soldier Res Dev & Engn Ctr Natick MA USA

Background Load carriage is common in a wide range of professions, but prolonged load carriage is associated with increased fatigue and overuse injuries. Exoskeletons could improve the quality of life of these professionals by reducing metabolic cost to combat fatigue and reducing muscle activity to prevent injuries. Current exoskeletons have reduced the metabolic cost of loaded walking by up to 22% relative to walking in the device with no assistance when assisting one or two joints. Greater metabolic reductions may be possible with optimized assistance of the entire leg. Methods We used human-in the-loop optimization to optimize hip-knee-ankle exoskeleton assistance with no additional load, a light load (15% of body weight), and a heavy load (30% of body weight) for three participants. All loads were applied through a weight vest with an attached waist belt. We measured metabolic cost, exoskeleton assistance, kinematics, and muscle activity. We performed Friedman's tests to analyze trends across worn loads and paired t-tests to determine whether changes from the unassisted conditions to the assisted conditions were significant. Results Exoskeleton assistance reduced the metabolic cost of walking relative to walking in the device without assistance for all tested conditions. Exoskeleton assistance reduced the metabolic cost of walking by 48% with no load (p = 0.05), 41% with the light load (p = 0.01), and 43% with the heavy load (p = 0.04). The smaller metabolic reduction with the light load may be due to insufficient participant training or lack of optimizer convergence. The total applied positive power was similar for all tested conditions, and the positive knee power decreased slightly as load increased. Optimized torque timing parameters were consistent across participants and load conditions while optimized magnitude parameters varied. Conclusions Whole-leg exoskeleton assistance can reduce the metabolic cost of walking while carrying a range of loads. The con

关键词： Exoskeleton Augmentation Load-carriage human-in-the-loop optimization

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Comparing optimized exoskeleton assistance of the hip, knee, and ankle in single and multi-joint configurations

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WEARABLE TECHNOLOGIES 2021年 2卷 e16页

作者： Franks, Patrick W. Bryan, Gwendolyn M. Martin, Russell M. Reyes, Ricardo Lakmazaheri, Ava C. Collins, Steven H. Stanford Univ Dept Mech Engn Stanford CA 94305 USA

Exoskeletons that assist the hip, knee, and ankle joints have begun to improve human mobility, particularly by reducing the metabolic cost of walking. However, direct comparisons of optimal assistance of these joints, or their combinations, have not yet been possible. Assisting multiple joints may be more beneficial than the sum of individual effects, because muscles often span multiple joints, or less effective, because single-joint assistance can indirectly aid other joints. In this study, we used a hip-knee-ankle exoskeleton emulator paired with human-in-the-loop optimization to find single-joint, two-joint, and whole-leg assistance that maximally reduced the metabolic cost of walking. Hip-only and ankle-only assistance reduced the metabolic cost of walking by 26 and 30% relative to walking in the device unassisted, confirming that both joints are good targets for assistance (N = 3). Knee-only assistance reduced the metabolic cost of walking by 13%, demonstrating that effective knee assistance is possible (N = 3). Two-joint assistance reduced the metabolic cost of walking by between 33 and 42%, with the largest improvements coming from hip-ankle assistance (N = 3). Assisting all three joints reduced the metabolic cost of walking by 50%, showing that at least half of the metabolic energy expended during walking can be saved through exoskeleton assistance (N = 4). Changes in kinematics and muscle activity indicate that single-joint assistance indirectly assisted muscles at other joints, such that the improvement from whole-leg assistance was smaller than the sum of its single-joint parts. Exoskeletons can assist the entire limb for maximum effect, but a single well-chosen joint can be more efficient when considering additional factors such as weight and cost.

关键词： biomechanics exoskeleton human-in-the-loop optimization

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Optimized hip-knee-ankle exoskeleton assistance at a range of walking speeds

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JOURNAL OF NEUROENGINEERING AND REHABILITATION 2021年第1期18卷 152-152页

作者： Bryan, Gwendolyn M. Franks, Patrick W. Song, Seungmoon Voloshina, Alexandra S. Reyes, Ricardo O'Donovan, Meghan P. Gregorczyk, Karen N. Collins, Steven H. Stanford Univ Dept Mech Engn Stanford CA 94305 USA Univ Calif Irvine Mech & Aerosp Engn Irvine CA USA US Army Natick Soldier Res Dev & Engn Ctr Natick MA 01760 USA

Background Autonomous exoskeletons will need to be useful at a variety of walking speeds, but it is unclear how optimal hip-knee-ankle exoskeleton assistance should change with speed. Biological joint moments tend to increase with speed, and in some cases, optimized ankle exoskeleton torques follow a similar trend. Ideal hip-knee-ankle exoskeleton torque may also increase with speed. The purpose of this study was to characterize the relationship between walking speed, optimal hip-knee-ankle exoskeleton assistance, and the benefits to metabolic energy cost. Methods We optimized hip-knee-ankle exoskeleton assistance to reduce metabolic cost for three able-bodied participants walking at 1.0 m/s, 1.25 m/s and 1.5 m/s. We measured metabolic cost, muscle activity, exoskeleton assistance and kinematics. We performed Friedman's tests to analyze trends across walking speeds and paired t-tests to determine if changes from the unassisted conditions to the assisted conditions were significant. Results Exoskeleton assistance reduced the metabolic cost of walking compared to wearing the exoskeleton with no torque applied by 26%, 47% and 50% at 1.0, 1.25 and 1.5 m/s, respectively. For all three participants, optimized exoskeleton ankle torque was the smallest for slow walking, while hip and knee torque changed slightly with speed in ways that varied across participants. Total applied positive power increased with speed for all three participants, largely due to increased joint velocities, which consistently increased with speed. Conclusions Exoskeleton assistance is effective at a range of speeds and is most effective at medium and fast walking speeds. Exoskeleton assistance was less effective for slow walking, which may explain the limited success in reducing metabolic cost for patient populations through exoskeleton assistance. Exoskeleton designers may have more success when targeting activities and groups with faster walking speeds. Speed-related changes in optimized exoskelet

关键词： Exoskeleton Augmentation Walking speed human-in-the-loop optimization

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Practical approaches to group-level multi-objective Bayesian optimization in interaction technique design

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Collective Intelligence 2024年第1期3卷

作者： Yi-Chi Liao George B Mo John J Dudley Chun-Lien Cheng Liwei Chan Per Ola Kristensson Antti Oulasvirta Department of Information and Communications Engineering Aalto University Aalto Finland Department of Engineering University of Cambridge Cambridge UK Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan

Designing interaction techniques for end-users often involves exploring vast design spaces while balancing many objectives. Bayesian optimization offers a principled human-in-the-loop method for selecting designs for evaluation to efficiently explore such design spaces. To date, the application of Bayesian optimization in a human-in-the-loop setting has largely been restricted to optimization, or customization, of interaction techniques for individual user needs. In practice, interaction techniques are typically designed for a target population or group of users, with the goal is to produce a design that works well for most users. To accommodate this common use case in interaction technique design, we introduce two practical approaches that facilitate multi-objective Bayesian optimization at the group level. Specifically, our approaches streamline the process of (1) deriving designs suitable for a group of users from data collected in individual user evaluations; and (2) deriving an initialization from group data to improve the efficiency of design optimization for new users. We demonstrate the advantages of these practical approaches in two multi-phase user studies involving the design of non-trivial interaction techniques.

关键词： human-in-the-loop optimization interaction technique interface design optimization design optimization Bayesian optimization pointing haptics input touch

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