Exoskeletons can make walking easier, but extensive training is needed to fully benefit from them, potentially hindering device adoption. Coaching new exoskeleton users to walk with energetically beneficial gait patterns might accelerate motor learning, but these target patterns have yet to be established. In this study, we characterized changes in gait biomechanics between healthy young adults first exposure to ankle exoskeletons and after 7+ hours of training when they demonstrated full motor adaptation. We observed consistent markers of adaptation in ankle kinematics and spatial gait features; expert exoskeleton users exhibited greater ankle dorsiflexion before a powerful and concentrated push-off while walking with narrower and longer steps. Adaptations in muscle coordination patterns were more varied, but generally involved reducing co-contraction throughout the leg. By the end of training, non-propulsive exoskeleton-assisted gait features approached those of unassisted walking. Our results suggest that effective exoskeleton use involves selectively avoiding most disruptions to typical gait while accommodating energetically beneficial changes around push-off. All reported gait features can be monitored and targeted in real-time, facilitating the application of these results to reduce the time burden for novice users to maximally benefit from ankle exoskeletons.

For individuals with knee osteoarthritis, increased knee loading is linked to disease progression and pain. Some approaches to treating osteoarthritis, such as specialized footwear, braces, and powered exoskeletons, also increase the mass of the lower limbs, which could lead to increases in knee loads. Prior studies have investigated the effect of changes in torso mass and total body mass on peak knee contact forces, but the effects of increased leg mass remain unclear. In this study, we created musculoskeletal simulations informed by experimental data to estimate tibiofemoral knee contact force under different lower-limb segment mass conditions. The mass of the foot, shank, and thigh were varied by adding weights to each segment, separately and concurrently, as healthy young adults (N = 10) walked on a treadmill. Kinematics, kinetics, and muscle activity were recorded. Our simulations used an optimal control framework that enforced experimental kinematics while minimizing a combination of net joint moment errors and mismatch between measured and estimated muscle activity. The simulations revealed that adding mass to the lower-limb segments linearly increased early- and late-stance peaks in knee contact force, but that the slope of this relationship was different for each peak and each mass placement location. For each 1% of body weight (BW) added per limb (2% BW total) at the thigh, shank, and foot, early-stance peak knee contact force increased by 1.5%, 2.1%, and 5.9% (r = 0.71), while late-stance peak contact force increased by 1.6%, 0.9% and 3.0% (r = 0.67), respectively. Adding mass to the thigh and shank increases peak contact force at or below the rate of increase in body mass, while adding mass to the foot disproportionately increases peak knee contact force. These detrimental effects should be considered when designing interventions for osteoarthritis.

Exoskeletons that make running easier could increase users’ physical activity levels and provide related health benefits. In this paper, we present the design of a portable, powered ankle exoskeleton that assists running and uses lightweight and compact twisted string actuators. It has limited durability at this stage of development, but preliminary results of its power to mass density and potential for reducing the metabolic cost of running are promising. The exoskeleton can provide high peak power of 700 W per leg, 7 times more than prior twisted-string devices, and high peak torques of 43 Nm. Kinetostatic and dynamic models were used to select mass-optimal components, producing a device that weighs 1.8 kg per leg and 2.0 kg in a backpack. We performed preliminary tests on a single participant to evaluate the exoskeleton performance during both treadmill running and outdoor running. The exoskeleton reduced metabolic energy use by 10.8% during treadmill running tests and reduced cost of transport by 7.7% during outdoor running tests compared to running without the device. Unfortunately, the twisted string wore out quickly, lasting an average of 4 min 50 s before breaking. This exoskeleton shows promise for making running easier if string life challenges can be addressed.

Lower-limb exoskeletons could benefit individuals with knee osteoarthritis by reducing knee loading. Real-time estimation of knee loads could accelerate the development of load-reducing exoskeletons. However, measuring or estimating internal knee forces remains challenging due to the rarity of force-sensing knee implants and complexity of simulation-based methods. We developed two data-driven models to separately estimate the peaks in knee contact force during early and late stance using a limited set of features from electromyography (EMG), ground reaction force (GRF), and knee angle recordings. These models were trained on experimental data from healthy young adults (N = 6) walking with a wide range of knee-ankle exoskeleton torque assistance conditions. Peak knee contact forces were obtained from EMG-informed musculoskeletal simulations in OpenSim Moco. The data-driven models were evaluated using leave-one-subject-out cross validation on their ability to accurately compare exoskeleton assistance conditions. The data-driven models identified directional changes in peak knee contact force larger than 0.1 body weights (BW) with 90% accuracy for early-stance peak and 79% accuracy for late-stance peak. Both models included GRF and knee angle features, but EMG features reflected phase-specific muscle activity: quadriceps appeared in the early-stance model, plantar flexors in late stance, and hamstrings in both. We developed a simple method to rapidly estimate changes in peak knee contact force. This approach is suitable for systematic interventions that aim to reduce knee load, such as human-in-the-loop optimization of exoskeleton assistance.

Measuring balance is important for detecting impairments and developing interventions to prevent falls, but there is no consensus on which method is most effective. Many balance metrics derived from steady-state walking data have been proposed, such as step-width variability, step-time variability, foot placement predictability, maximum Lyapunov exponent and margin of stability. Recently, perturbation-based metrics such as center of mass displacement have also been explored. Perturbations typically involve unexpected disturbances applied to the subject. In this study we collected walking data from 10 healthy human subjects while walking normally and while impairing balance with ankle braces, eye-blocking masks and pneumatic jets on their legs. In some walking trials we also applied mechanical perturbations to the pelvis. We obtained a comprehensive biomechanics dataset and compared the ability of various metrics to detect impaired balance using steady-state walking and perturbation recovery data. We also compared metric performance using thresholds informed by data from multiple subjects versus subject-specific thresholds. We found that step-width variability, step-time variability and foot placement predictability, using steady-state data and subject-specific thresholds, detected impaired balance with the highest accuracy (≥86%), whereas other metrics were less effective (≤68%). Incorporating perturbation data did not improve accuracy of these metrics, although this comparison was limited by the small amount of perturbation data included and analyzed. Subject-specific baseline measurements improved the detection of changes in balance ability. Thus, in clinical practice, taking baseline measurements might improve the detection of impairment due to aging or disease progression.

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 population. Methods 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 trials. Results 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 = 8 e -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 adaptation. Conclusions 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.

From industrial exoskeletons to implantable medical devices, robots that interact closely with people are poised to improve every aspect of our lives. Yet designing these systems is very challenging; humans are incredibly complex and, in many cases, we respond to robotic devices in ways that cannot be modelled or predicted with sufficient accuracy. A new approach, human-in-the-loop optimization, can overcome these challenges by systematically and empirically identifying the device characteristics that result in the best objective performance for a specific user and application. This approach has enabled substantial improvements in human-robot performance in research settings and has the potential to speed development and enhance products. In this Perspective, we describe methods for applying human-in-the-loop optimization to new human-robot interaction problems, addressing each key decision in a variety of contexts. We also identify opportunities to develop new optimization techniques and answer underlying scientific questions. We anticipate that our readers will advance human-in-the-loop optimization and use it to design robotic devices that truly enhance the human experience.

Optimized assistance patterns have produced the greatest exoskeleton benefits to energy expenditure of any strategy to date. This strategy may be effective due to the customization of the applied torque profiles to the user as well as the locomotion condition; however, it is currently unclear how sensitive participants are to their unique torque profile. To investigate, we applied previously optimized hip-knee-ankle torque profiles to expert users (N=3; 1.25 m/s; 0 deg incline). The participants walked with the profile optimized to them, the two profiles optimized to the other two participants, and the average of the three torque profiles while we measured their energy expenditure. Relative to walking with the device turned off, on average, participants experienced a 47.5% (range 12%) metabolic reduction when walking with the torque profile optimized to them and a 46% (range 15%) reduction when walking with the other profiles. Interestingly, within-subject performance was more consistent than across subjects (P1: 52% range 5%, P2: 49% range 6%, P3: 39% range 3%) suggesting that, for expert users of some devices, there may be a range of nearly equally effective torque profiles to reduce the metabolic cost of walking. The torque timing was remarkably similar across the four torque profiles while the torque magnitude varied; participants may be much more sensitive to torque timing than torque magnitude, and there may be a set of torque timing parameters that are generally effective.

Electric motors are widely used in robots but waste energy in many applications. We introduce an elastic energy-recycling actuator that maintains the versatility of motors while improving energy efficiency in cyclic tasks. The actuator comprises a motor in parallel with an array of springs that can be individually engaged and disengaged, while retaining stored energy, by pairs of low-power electroadhesive clutches. We developed a prototype actuator and tested it in five repetitive tasks with features common in robotic applications but difficult to perform efficiently. The actuator reduced power consumption by at least 50% in all cases and by 97% in the best case. Elastic energy recovery, controlled by low-power clutches, can improve the efficiency of mobile robots, assistive devices, and other engineered systems.

Exoskeletons that assist in ankle plantarflexion can improve energy economy in locomotion. Characterizing the joint-level mechanisms behind these reductions in energy cost can lead to a better understanding of how people interact with these devices, as well as to improved device design and training protocols. We examined the biomechanical responses to exoskeleton assistance in exoskeleton users trained with a lengthened protocol. Kinematics at unassisted joints were generally unchanged by assistance, which has been observed in other ankle exoskeleton studies. Peak plantarflexion angle increased with plantarflexion assistance, which led to increased total and biological mechanical power despite decreases in biological joint torque and whole-body net metabolic energy cost. Ankle plantarflexor activity also decreased with assistance. Muscles that act about unassisted joints also increased activity for large levels of assistance, and this response should be investigated over long-term use to prevent overuse injuries.

Exoskeletons are a burgeoning technology with many possible applications to improve human life; focusing the effort of exoskeleton research and development on the most important features is essential for facilitating adoption and maximizing positive societal impact. To identify important focus areas for exoskeleton research and development, we conducted a survey with 154 potential users (older adults) and another survey with 152 clinicians. The surveys were conducted online and to ensure a consistent concept of an exoskeleton across respondents, an image of a hip exoskeleton was shown during exoskeleton-related prompts. The survey responses indicate that both older adults and clinicians are open to using exoskeletons, fall prevention and joint pain reduction are especially important features, and users are likely to wear an exoskeleton in the scenarios when it has the greatest opportunity to help prevent a fall. These findings can help inform future exoskeleton research and guide the development of devices that are accepted, used, and provide meaningful benefit to users.

Falls are the leading cause of fatal and non-fatal injuries, particularly for older persons. Imbalance can result from the body's internal causes (illness), or external causes (active or passive perturbation). Active perturbation results from applying an external force to a person, while passive perturbation results from human motion interacting with a static obstacle. This work proposes a metric that allows for the monitoring of the person's torso and its correlation to active and passive perturbations. We show that large changes in the torso sway can be strongly correlated to active perturbations. We also show that we can reasonably predict the future path and expected change in torso sway by conditioning the expected path and torso sway on the past trajectory, torso motion, and the surrounding scene. This could have direct future applications to fall prevention. Results demonstrate that the torso sway is strongly correlated with perturbations. And our model is able to make use of the visual cues presented in the panorama and condition the prediction accordingly.

Walking balance is central to independent mobility, and falls due to loss of balance are a leading cause of death for people 65 years of age and older. Bipedal gait is typically unstable, but healthy humans use corrective torques to counteract perturbations and stabilize gait. Exoskeleton assistance could benefit people with neuromuscular deficits by providing stabilizing torques at lower-limb joints to replace lost muscle strength and sensorimotor control. However, it is unclear how applied exoskeleton torques translate to changes in walking kinematics. This study used musculoskeletal simulation to investigate how exoskeleton torques applied to the ankle and subtalar joints alter center of mass kinematics during walking. We first created muscle-driven walking simulations using OpenSim Moco by tracking experimental kinematics and ground reaction forces recorded from five healthy adults. We then used forward integration to simulate the effect of exoskeleton torques applied to the ankle and subtalar joints while keeping muscle excitations fixed based on our previous tracking simulation results. Exoskeleton torque lasted for 15% of the gait cycle and was applied between foot-flat and toe-off during the stance phase, and changes in center of mass kinematics were recorded when the torque application ended. We found that changes in center of mass kinematics were dependent on both the type and timing of exoskeleton torques. Plantarflexion torques produced upward and backward changes in velocity of the center of mass in mid-stance and upward and smaller forward velocity changes near toe-off. Eversion and inversion torques primarily produced lateral and medial changes in velocity in mid-stance, respectively. Intrinsic muscle properties reduced kinematic changes from exoskeleton torques. Our results provide mappings between ankle plantarflexion and inversion-eversion torques and changes in center of mass kinematics which can inform designers building exoskeletons aimed at stabilizing balance during walking. Our simulations and software are freely available and allow researchers to explore the effects of applied torques on balance and gait.

Background During walking, humans exert a substantial hip abduction moment to maintain balance and prevent pelvic drop. This significant torque requirement suggests that assisting the frontal hip muscles could be a promising strategy to reduce the energy expenditure associated with walking. A previous musculoskeletal simulation study also predicted that providing hip abduction assistance through an exoskeleton could potentially result in a large reduction in whole-body metabolic rate. However, to date, no study has experimentally assessed the metabolic cost of walking with frontal hip assistance. Methods In this case study involving a single subject (N = 1), a tethered hip exoskeleton emulator was used to assess the feasibility of reducing metabolic expenditure through frontal-plane hip assistance. Human-in-the-loop optimization was conducted separately under torque and position control to determine energetically optimal assistance parameters for each control scheme. Results The optimized profiles in both control schemes did not reduce metabolic rate compared to walking with assistance turned off. The optimal peak torque magnitude was found to be close to zero, suggesting that any hip abduction torque would increase metabolic rate. Both bio-inspired and simulation-inspired profiles substantially increased metabolic cost. Conclusion Frontal hip assistance does not appear to be promising in reducing the metabolic rate of walking. This could be attributed to the need for maintaining balance, as humans may refrain from relaxing certain muscles as a precaution against unexpected disturbances during walking. An investigation of different control architectures is needed to determine if frontal-plane hip assistance can yield successful results.

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subject's skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

Personalized exoskeleton assistance provides users with the largest improvements in walking speed 1 and energy economy 2–4 but requires lengthy tests under unnatural laboratory conditions. Here we show that exoskeleton optimization can be performed rapidly and under real-world conditions. We designed a portable ankle exoskeleton based on insights from tests with a versatile laboratory testbed. We developed a data-driven method for optimizing exoskeleton assistance outdoors using wearable sensors and found that it was equally effective as laboratory methods, but identified optimal parameters four times faster. We performed real-world optimization using data collected during many short bouts of walking at varying speeds. Assistance optimized during one hour of naturalistic walking in a public setting increased self-selected speed by 9 ± 4% and reduced the energy used to travel a given distance by 17 ± 5% compared with normal shoes. This assistance reduced metabolic energy consumption by 23 ± 8% when participants walked on a treadmill at a standard speed of 1.5 m s −1 . Human movements encode information that can be used to personalize assistive devices and enhance performance.

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method to automate and standardize motion capture data processing: bilevel optimization that is able to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. The optimization requires less than five minutes of computation to process a subject’s motion capture data, compared with about one day of manual work for a human expert. On a sample of 34 trials of experimental data, the root-mean-square marker reconstruction error (RMSE) was 1.38 cm, approximately 40% lower than the 2.58 cm achieved manually by 3 experts. Optimization solutions reconstructed known joint angle trajectories from four diverse motion trials of synthetic data to an average of 0.79 degrees RMSE. We have published an open source cloud service at AddBiomechanics.org to process experimental motion capture data, which is available at no cost and asks that users agree to share processed and de-identified data with the community. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to engage in state-of-the-art biomechanical analysis in their work, do so at lower cost, and share larger and more accurate datasets. Author summary Creating large-scale public datasets of human motion could unlock data-driven breakthroughs in our understanding of neuromuscular diseases, assistive devices, and human motion more broadly. The manual effort currently required to process these motion datasets is costly and limits the collection and sharing of large-scale datasets. Our cloud-based software tool, called AddBiomechanics, uses state-of-the-art optimization techniques to automatically scale the body segments of a musculoskeletal model to match the subject of interest, and then compute body segment kinematics during a motion. The optimization requires less than five minutes of computation to process a subject’s motion capture data, compared with about one day of manual work for a human expert. The accuracy of the approach in quantifying the body segment kinematics is as good or better than the results achieved manually by experts. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to engage in state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

Our nervous systems can learn optimal control policies in response to changes to our bodies, tasks, and movement contexts. For example, humans can learn to adapt their control policy in walking contexts where the energy-optimal policy is shifted along variables such as step frequency or step width. However, it is unclear how the nervous system determines which ways to adapt its control policy. Here, we asked how human participants explore through variations in their control policy to identify more optimal policies in new contexts. We created new contexts using exoskeletons that apply assistive torques to each ankle at each walking step. We analyzed four variables that spanned the levels of the whole movement, the joint, and the muscle: step frequency, ankle angle range, total soleus activity, and total medial gastrocnemius activity. We found that, across all of these analyzed variables, variability increased upon initial exposure to new contexts and then decreased with experience. This led to adaptive changes in the magnitude of specific variables, and these changes were correlated with reduced energetic cost. The timescales by which adaptive changes progressed and variability decreased were faster for some variables than others, suggesting a reduced search space within which the nervous system continues to optimize its policy. These collective findings support the principle that exploration through general variability leads to specific adaptation toward optimal movement policies.

Real-world application of haptic feedback from kinesthetic devices is implemented while the user is in motion, but human wrist torque magnitude discrimination has previously only been characterized while users are stationary. In this study, we measured wrist torque discrimination in conditions relevant to activities of daily living, using a previously developed backdrivable wrist exoskeleton capable of applying wrist flexion and extension torque. We implemented a torque comparison test using a two-alternative forced-choice paradigm while participants were both seated and walking on a treadmill, with both a stationary and a moving wrist. Like most kinesthetic haptic devices, the wrist exoskeleton output torque is commanded in an open-loop manner. Thus, the study design was informed by Monte Carlo simulations to verify that the errors in the wrist exoskeleton output torque would not significantly affect the results. Results from ten participants show that although both walking and moving wrist conditions result in higher Weber Fractions (worse perception), participants were able to detect relatively small changes in torque of 12-19% on average in all grouped conditions. The results provide insight regarding the torque magnitudes necessary to make wrist-worn kinesthetic haptic devices noticeable and meaningful to the user in various conditions relevant to activities of daily living.

Split-belt treadmill walking, in which the two belts move at different speeds, reveals a mechanism through which energy can be extracted from the environment. When a person walks with positive step length asymmetry on a split-belt treadmill, the treadmill can perform net positive work on the person. Here we use a split-belt rimless wheel model to explore how people could take advantage of the treadmill. We show that a split-belt rimless wheel can passively walk steadily by capturing energy from the treadmill to overcome collision losses, whereas it loses energy on each step with no way to recover the losses when walking on tied belts. Our simulated split-belt rimless wheel can walk steadily for a variety of leg angle and belt speed combinations, tolerating both speed disturbances and ground height variability. The wheel can even capture enough energy to walk uphill. We also built a physical split-belt rimless wheel robot and demonstrated that it can walk continuously without additional energy input. In comparing the wheel solutions to human split-belt gait, we found that humans do not maximize positive work performed by the treadmill. Other aspects of walking, such as costs associated with swing, balance, and free vertical moments, likely limit people’s ability to benefit from the treadmill. This study uses a simple walking model to characterize the mechanics and energetics of split-belt walking, demonstrating that energy capture through intermittent contact with two belts is possible and providing a simple model framework for understanding human adaptation during split-belt walking.