Exoskeletons and active prostheses promise to enhance human mobility, but few have succeeded. Optimizing device characteristics on the basis of measured human performance could lead to improved designs. We have developed a method for identifying the exoskeleton assistance that minimizes human energy cost during walking. Optimized torque patterns from an exoskeleton worn on one ankle reduced metabolic energy consumption by 24.2 ± 7.4% compared to no torque. The approach was effective with exoskeletons worn on one or both ankles, during a variety of walking conditions, during running, and when optimizing muscle activity. Finding a good generic assistance pattern, customizing it to individual needs, and helping users learn to take advantage of the device all contributed to improved economy. Optimization methods with these features can substantially improve performance.

Human-in-the-loop optimization of exoskeleton assistance during walking.

Wearable robotic devices have been shown to substantially reduce the energy expenditure of human walking. However, response variance between participants for fixed control strategies can be high, leading to the hypothesis that individualized controllers could further improve walking economy. Recent studies on human-in-the-loop (HIL) control optimization have elucidated several practical challenges, such as long experimental protocols and low signal-to-noise ratios. Here, we used Bayesian optimization-an algorithm well suited to optimizing noisy performance signals with very limited data-to identify the peak and offset timing of hip extension assistance that minimizes the energy expenditure of walking with a textile-based wearable device. Optimal peak and offset timing were found over an average of 21.4 ± 1.0 min and reduced metabolic cost by 17.4 ± 3.2% compared with walking without the device (mean ± SEM), which represents an improvement of more than 60% on metabolic reduction compared with state-of-the-art devices that only assist hip extension. In addition, our results provide evidence for participant-specific metabolic distributions with respect to peak and offset timing and metabolic landscapes, lending support to the hypothesis that individualized control strategies can offer substantial benefits over fixed control strategies. These results also suggest that this method could have practical impact on improving the performance of wearable robotic devices.

Human-in-the-loop optimization of hip assistance with a soft exosuit during walking

Walking and running have fundamentally different biomechanics, which makes developing devices that assist both gaits challenging. We show that a portable exosuit that assists hip extension can reduce the metabolic rate of treadmill walking at 1.5 meters per second by 9.3% and that of running at 2.5 meters per second by 4.0% compared with locomotion without the exosuit. These reduction magnitudes are comparable to the effects of taking off 7.4 and 5.7 kilograms during walking and running, respectively, and are in a range that has shown meaningful athletic performance changes. The exosuit automatically switches between actuation profiles for both gaits, on the basis of estimated potential energy fluctuations of the wearer's center of mass. Single-participant experiments show that it is possible to reduce metabolic rates of different running speeds and uphill walking, further demonstrating the exosuit's versatility.

Reducing the metabolic rate of walking and running with a versatile, portable exosuit.

Carrying load alters normal walking, imposes additional stress to the musculoskeletal system, and results in an increase in energy consumption and a consequent earlier onset of fatigue. This phenomenon is largely due to increased work requirements in lower extremity joints, in turn requiring higher muscle activation. The aim of this work was to assess the biomechanical and physiological effects of a multi-joint soft exosuit that applies assistive torques to the biological hip and ankle joints during loaded walking. The exosuit was evaluated under three conditions: powered (EXO_ON), unpowered (EXO_OFF) and unpowered removing the equivalent mass of the device (EXO_OFF_EMR). Seven participants walked on an instrumented split-belt treadmill and carried a load equivalent to 30 % their body mass. We assessed their metabolic cost of walking, kinetics, kinematics, and lower limb muscle activation using a portable gas analysis system, motion capture system, and surface electromyography. Our results showed that the exosuit could deliver controlled forces to a wearer. Net metabolic power in the EXO_ON condition (7.5 ± 0.6 W kg−1) was 7.3 ± 5.0 % and 14.2 ± 6.1 % lower than in the EXO_OFF_EMR condition (7.9 ± 0.8 W kg−1; p = 0.027) and in the EXO_OFF condition (8.5 ± 0.9 W kg−1; p = 0.005), respectively. The exosuit also reduced the total joint positive biological work (sum of hip, knee and ankle) when comparing the EXO_ON condition (1.06 ± 0.16 J kg−1) with respect to the EXO_OFF condition (1.28 ± 0.26 J kg−1; p = 0.020) and to the EXO_OFF_EMR condition (1.22 ± 0.21 J kg−1; p = 0.007). The results of the present work demonstrate for the first time that a soft wearable robot can improve walking economy. These findings pave the way for future assistive devices that may enhance or restore gait in other applications.

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A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking

Since the early 2000s, researchers have been trying to develop lower-limb exoskeletons that augment human mobility by reducing the metabolic cost of walking and running versus without a device. In 2013, researchers finally broke this ‘metabolic cost barrier’. We analyzed the literature through December 2019, and identified 23 studies that demonstrate exoskeleton designs that improved human walking and running economy beyond capable without a device. Here, we reviewed these studies and highlighted key innovations and techniques that enabled these devices to surpass the metabolic cost barrier and steadily improve user walking and running economy from 2013 to nearly 2020. These studies include, physiologically-informed targeting of lower-limb joints; use of off-board actuators to rapidly prototype exoskeleton controllers; mechatronic designs of both active and passive systems; and a renewed focus on human-exoskeleton interface design. Lastly, we highlight emerging trends that we anticipate will further augment wearable-device performance and pose the next grand challenges facing exoskeleton technology for augmenting human mobility.

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The exoskeleton expansion: improving walking and running economy

Lower-limb exoskeletons capable of comfortably applying high torques at high bandwidth can be used to probe the human neuromuscular system and assist gait. We designed and built two tethered ankle exoskeletons with strong lightweight frames, comfortable three-point contact with the leg, and series elastic elements for improved torque control. Both devices have low mass (< 0.88 kg), are modular, structurally compliant in selected directions, and instrumented to measure joint angle and torque. The exoskeletons are actuated by an off-board motor, and torque is controlled using a combination of proportional feedback and damping injection with iterative learning during walking tests. We tested closed-loop torque control by commanding 50 N·m and 20 N·m linear chirps in desired torque while the exoskeletons were worn by human users, and measured bandwidths greater than 16 Hz and 21 Hz, respectively. During walking trials, we demonstrated 120 N·m peak torque and 2.0 N·m RMS torque tracking error. These performance measures compare favorably with existing devices and with human ankle musculature, and show that these exoskeletons can be used to rapidly explore a wide range of control techniques and robotic assistance paradigms as elements of versatile, high-performance testbeds. Our results also provide insights into desirable properties of lower-limb exoskeleton hardware, which we expect to inform future designs.

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Design of two lightweight, high-bandwidth torque-controlled ankle exoskeletons

Few comparisons have been performed across torque controllers for exoskeletons, and differences among devices have made interpretation difficult. In this study, we designed, developed and compared the torque-tracking performance of nine control methods, including variations on classical feedback control, model-based control, adaptive control and iterative learning. Each was tested with four high-level controllers that determined desired torque based on time, joint angle, a neuromuscular model, or electromyography. Controllers were implemented on a tethered ankle exoskeleton with series elastic actuation. Measurements were taken while one human subject walked on a treadmill at 1.25 m·s−1 for one hundred steady-state steps. The combination of proportional control with damping injection and iterative learning resulted in the lowest errors for all high-level controllers. With time-based desired torque, root-mean-squared errors were 0.6 N·m (1.3% of peak desired torque) step by step, and 0.1 N·m (0.2%) on average. These results indicate that model-free, integration-free feedback control is suited to the uncertain dynamics of the human-robot system, while iterative learning is effective in the cyclic task of walking.

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Experimental comparison of torque control methods on an ankle exoskeleton during human walking

Each year, stroke and traumatic brain injury leave millions of survivors with motion control loss, which results in great demand for recovery training. The great labor intensity in traditional human-based therapies has recently boosted the research on rehabilitation robotics. Existing controllers for rehabilitative robotics cannot solve the closed-loop system stability with uncertain nonlinear dynamics and conflicting human–robot interactions. This paper presents a theoretical framework that establishes the passivity of the closed-loop upper-limb rehabilitative robotic systems and allows rigorous stability analysis of human–robot interaction. Position-dependent stiffness and position-dependent desired trajectory are employed to resolve the possible conflicts in motions between patient and robot. The proposed method also realizes the “assist-as-needed” strategy. In addition, it handles human–robot interactions in such a way that correct movements are encouraged and incorrect ones are suppressed to make the training process more effective. While guaranteeing these properties, the proposed controller allows parameter adjustment to provide flexibility for therapists to adjust and fine tune depending on the conditions of the patients and the progress of their recovery. Simulation and experiment results are presented to illustrate the performance of the method.

Passivity and Stability of Human–Robot Interaction Control for Upper-Limb Rehabilitation Robots

Many torque control approaches have been proposed for robotic devices used in legged locomotion, but few comparisons have been performed across controllers in the same system. In this study, we compared the torque-tracking performance of nine control strategies, including variations on classical feedback control, model-based control, adaptive control, and iterative learning. To account for interactions between patterns in desired torque and tracking performance, we tested each in combination with four high-level controllers that determined desired torque based on time, joint angle, a neuromuscular model, or electromyographic measurements. Controllers were implemented on an ankle exoskeleton with series elastic actuation driven by an off-board motor through a uni-directional Bowden cable. The exoskeleton was worn by one human subject walking on a treadmill at 1.25 m⋅ s −1 for one hundred steady-state steps under each condition. We found that the combination of proportional control, damping injection and iterative learning resulted in substantially lower root-mean-squared error than other torque control approaches for all high-level controllers. With this low level torque controller, RMS errors can be as low as 1.3% of peak torque for real-time tracking, and 0.2% for the average stride. Model-free, integration-free feedback control seems to be well suited to the uncertain, changing dynamics of the human–robot system, while iterative learning is advantageous in the cyclic task of walking.

Juanjuan Zhang

Papers

Human-in-the-loop optimization of exoskeleton assistance during walking.

Design of two lightweight, high-bandwidth torque-controlled ankle exoskeletons

Experimental comparison of torque control methods on an ankle exoskeleton during human walking

Passivity and Stability of Human–Robot Interaction Control for Upper-Limb Rehabilitation Robots

Torque Control in Legged Locomotion