User preference optimization for control of ankle exoskeletons using sample efficient active learning.

One challenge to achieving widespread success of augmentative exoskeletons is accurately adjusting the controller to provide cooperative assistance with their wearer. Often, the controller parameters are "tuned" to optimize a physiological or biomechanical objective. However, these approaches are resource intensive, while typically only enabling optimization of a single objective. In reality, the exoskeleton user experience is likely derived from many factors, including comfort, fatigue, and stability, among others. This work introduces an approach to conveniently tune the four parameters of an exoskeleton controller to maximize user preference. Our overarching strategy is to leverage the wearer to internally balance the experiential factors of wearing the system. We used an evolutionary algorithm to recommend potential parameters, which were ranked by a neural network that was pretrained with previously collected user preference data. The controller parameters that had the highest preference ranking were provided to the exoskeleton, and the wearer responded with real-time feedback as a forced-choice comparison. Our approach was able to converge on controller parameters preferred by the wearer with an accuracy of 88% on average when compared with randomly generated parameters. User-preferred settings stabilized in 43 ± 7 queries. This work demonstrates that user preference can be leveraged to tune a partial-assist ankle exoskeleton in real time using a simple, intuitive interface, highlighting the potential for translating lower-limb wearable technologies into our daily lives.

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

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.

Coupled exoskeleton assistance simplifies control and maintains metabolic benefits: A simulation study.

Assistive exoskeletons can reduce the metabolic cost of walking, and recent advances in exoskeleton device design and control have resulted in large metabolic savings. Most exoskeleton devices provide assistance at either the ankle or hip. Exoskeletons that assist multiple joints have the potential to provide greater metabolic savings, but can require many actuators and complicated controllers, making it difficult to design effective assistance. Coupled assistance, when two or more joints are assisted using one actuator or control signal, could reduce control dimensionality while retaining metabolic benefits. However, it is unknown which combinations of assisted joints are most promising and if there are negative consequences associated with coupled assistance. Since designing assistance with human experiments is expensive and time-consuming, we used musculoskeletal simulation to evaluate metabolic savings from multi-joint assistance and identify promising joint combinations. We generated 2D muscle-driven simulations of walking while simultaneously optimizing control strategies for simulated lower-limb exoskeleton assistive devices to minimize metabolic cost. Each device provided assistance either at a single joint or at multiple joints using massless, ideal actuators. To assess if control could be simplified for multi-joint exoskeletons, we simulated different control strategies in which the torque provided at each joint was either controlled independently or coupled between joints. We compared the predicted optimal torque profiles and changes in muscle and total metabolic power consumption across the single joint and multi-joint assistance strategies. We found multi-joint devices-whether independent or coupled-provided 50% greater metabolic savings than single joint devices. The coupled multi-joint devices were able to achieve most of the metabolic savings produced by independently-controlled multi-joint devices. Our results indicate that device designers could simplify multi-joint exoskeleton designs by reducing the number of torque control parameters through coupling, while still maintaining large reductions in metabolic cost.

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

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 consistent optimized timing parameters across participants and conditions suggest that metabolic cost reductions are sensitive to torque timing. The variable torque magnitude parameters could imply that torque magnitude should be customized to the individual, or that there is a range of useful torque magnitudes. Future work should test whether applying the load to the exoskeleton rather than the person's torso results in larger benefits.

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

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 exoskeleton assistance varied by participant, indicating either the benefit of participant-specific tuning or that a wide variety of torque profiles are similarly effective.

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

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.

Demonstration of extended field-of-view ultrasound's potential to increase the pool of muscles for which in vivo fascicle length is measurable.

Static, B-mode ultrasound is the most common method of measuring fascicle length in vivo. However, most forearm muscles have fascicles that are longer than the field-of-view of traditional ultrasound (T-US). As such, little work has been done to quantify in vivo forearm muscle architecture. The extended field-of-view ultrasound (EFOV-US) method, which fits together a sequence of B-mode images taken from a continuous ultrasound scan, facilitates direct measurements of longer, curved fascicles. Here, we test the validity and reliability of the EFOV-US method for obtaining fascicle lengths in the extensor carpi ulnaris (ECU). Fascicle lengths from images of the ECU captured in vivo with EFOV-US were compared to lengths from a well-established method, T-US. Images were collected in a joint posture that shortens the ECU such that entire fascicle lengths were captured within a single T-US image. Resulting measurements were not significantly different (p=0.18); a Bland-Altman test demonstrated their agreement. A novice sonographer implemented EFOV-US in a phantom and in vivo on the ECU. The novice sonographer's measurements from the ultrasound phantom indicate that the combined imaging and analysis method is valid (average error=2.2±1.3mm) and the in vivo fascicle length measurements demonstrate excellent reliability (ICC=0.97). To our knowledge, this is the first study to quantify in vivo fascicle lengths of the ECU using any method. The ability to define a muscle's architecture in vivo using EFOV-US could lead to improvements in diagnosis, model development, surgery guidance, and rehabilitation techniques.