Control and Optimization of Soft Exosuit to Improve

Control and Optimization of Soft Exosuit to Improve the Efficiency of Human Walking A dissertation presented by

Ye Ding to

The School of Engineering and Applied Science

in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the subject of Engineering Sciences

Harvard University Cambridge, Massachusetts Jan 2018

c 2018 Ye Ding

All rights reserved.

Dissertation Advisor: Professor Conor J. Walsh

Author: Ye Ding

Control and Optimization of Soft Exosuit to Improve the Efficiency of Human Walking

Abstract Researchers have strived to develop lower limb wearable devices to improve the efficiency of human walking, however significant device inertia, kinematic constraints and the lack of optimal control strategies has limited performance when testing with human subjects. To alleviate these challenges, we developed the soft exosuit, a textile based wearable device that provides a conformal, unobtrusive and compliant means for interface with the human body. Unlike most rigid exoskeletons, the components of the soft exosuit on the lower extremity can be worn like light clothing. Combined with proximal mounted actuators, soft exosuit can provide forces that are in parallel to the lower limb muscles to improve the efficiency of walking. This thesis details the developments of soft exosuits, actuation platforms, real-time controllers and optimization methods. The working principle of the soft exosuits and the development of multi-joint actuation platforms that can provide real-time controlled assistance are presented. Two types of exosuit controllers, including iterative force-based position control and switching admittance-position control are designed and evaluated. The iterative force-based position control can accurately detect the hip extension onset timing and track peak timing and peak magnitude of the assistive profile within 1% of error. The switching admittance-position controller can track the whole shape of assistive profile with a root-mean-square error of 3.4%. For the exploration of human-robot interaction, human subject studies evaluating the effects of multi-joint assistance and timing of hip extension assistance are conducted. In the

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multi-joint assistance study with subjects carrying a 23.8 kg backpack on a treadmill of 1.25 m s−1 , the single joint condition solely assisting hip extension with a peak force of 95 N achieves a metabolic reduction of 4.6%, while the multi-joint condition assisting both hip extension and ankle plantarflexion with peak forces of 95 N and 196 N achieves a metabolic reduction of 14.6% compared to unpowered condition. The result suggests the metabolic benefits can be added up by assisting multiple joints simultaneously. In the effect of the timing of hip extension study, four different assistive profiles with two sets of onset and peak timings are evaluated with subjects carrying a 23 kg backpack on a treadmill of 1.5 m s−1 . The result demonstrates the actuation timing can affect the delivered mechanical power, biological joint power and metabolic performance. The assistive profile with an early onset and late peak timing delivers the most of mechanical power and achieves the highest metabolic reduction of 8.5% compared to the unpowered condition. Finally, the development and evaluation of human-in-the-loop Bayesian optimization for identifying the optimal control parameters is presented. The optimization method is first evaluated by finding subject’s optimal step frequency. It is found that the single-parameter optimization converges to the optimal step frequency in half the time of the established gradient descent method and significantly reduces the required total energy expenditure for the overall experiment protocol. Then, the multi-parameter Bayesian optimization is evaluated by optimizing the peak and offset timing of hip extension assistance with a soft exosuit. In this study, optimal timings are found over an average of 21.4 min and achieved 17.4% metabolic reduction compared to no-suit condition, which represented an improvement of more than 60% on metabolic reduction compared with state-of-the-art devices that only assist hip extension. The result provides 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. Overall, this thesis presents how to design and control soft exosuit system and lends insight into the human-robot interaction including the use of human-in-the-loop optimization to

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maximize the efficiency of human walking. I hope this work contributes to a shift towards individualization of future wearable devices.

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Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiv 1

Thesis Summary 1.1 Specific aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Thesis contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Introduction 2.1 Wearable assistive device definition . . . . . . . . . . . . 2.2 Wearable assistive device applications . . . . . . . . . . . 2.2.1 Metabolic reduction . . . . . . . . . . . . . . . . . 2.2.2 Strength augmentation . . . . . . . . . . . . . . . . 2.2.3 Orthosis and rehabilitation training . . . . . . . . 2.2.4 Energy harvesting . . . . . . . . . . . . . . . . . . 2.3 Contributing factors to metabolic cost of human walking 2.4 Wearable assistive device challenges . . . . . . . . . . . . 2.4.1 Device mass and inertia . . . . . . . . . . . . . . . 2.4.2 Kinematic constraints . . . . . . . . . . . . . . . . 2.4.3 Controls . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Motivations for soft exosuit approach . . . . . . . . . . .

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Biomechanical gait analysis 3.1 Human Gait . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Human walking biomechanics in the sagittal plane . . . . . . . . . . . . . . . 3.3 Electromyography of muscle contraction during walking . . . . . . . . . . . .

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Soft exosuit design 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Structure of ankle exosuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Structure of hip exosuit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Electro-mechanical design of multi-joint actuation platforms 5.1 Motivation of the multi-joint actuation platform . . . . . . 5.2 Functional requirements . . . . . . . . . . . . . . . . . . . . 5.3 Multi-joint actuation platform V1 and V2 . . . . . . . . . . 5.3.1 System structure . . . . . . . . . . . . . . . . . . . . 5.3.2 Actuator design . . . . . . . . . . . . . . . . . . . . . 5.3.3 Sensing and instrumentation . . . . . . . . . . . . . 5.4 Multi-joint actuation platform V3 . . . . . . . . . . . . . . . 5.4.1 System structure . . . . . . . . . . . . . . . . . . . . 5.4.2 Actuator design . . . . . . . . . . . . . . . . . . . . . 5.4.3 Control system architecture . . . . . . . . . . . . . . 5.5 Lesson learned . . . . . . . . . . . . . . . . . . . . . . . . . 5.5.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . 5.5.2 Software . . . . . . . . . . . . . . . . . . . . . . . . . Controller design and Implementation 6.1 Iterative force-based position control . 6.1.1 Motivation . . . . . . . . . . . . 6.1.2 System modeling . . . . . . . . 6.1.3 Simulation . . . . . . . . . . . . 6.1.4 Gait detection . . . . . . . . . . 6.1.5 Iterative profile generation . . 6.1.6 Experiment design and result . 6.1.7 Limitations . . . . . . . . . . . 6.1.8 Conclusion . . . . . . . . . . . 6.2 Switching admittance-position control 6.2.1 Motivation . . . . . . . . . . . . 6.2.2 System modeling . . . . . . . . 6.2.3 Control module . . . . . . . . . 6.2.4 Experiment design and result . 6.2.5 Conclusion . . . . . . . . . . .

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Biomechanical and physiological evaluation of multi-joint assistance 7.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 System and Controller . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Soft exosuit and multi-joint actuation platform . . . . . . . 7.2.2 Assistive Controller . . . . . . . . . . . . . . . . . . . . . . . 7.3 Experimental procedures . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Testing protocol . . . . . . . . . . . . . . . . . . . . . . . . . . vii

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7.3.2 Data analysis . . . 7.3.3 Statistical analysis Results . . . . . . . . . . . 7.4.1 Metabolic analysis 7.4.2 Kinematic analysis 7.4.3 Kinetic analysis . . 7.4.4 EMG analysis . . . 7.4.5 Discussion . . . . . Conclusion . . . . . . . . .

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Effect of timing of hip extension assistance 8.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . 8.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Soft exosuit and multi-actuation platform 8.2.2 Sensing and control . . . . . . . . . . . . . 8.2.3 Assistive force profiles . . . . . . . . . . . . 8.2.4 Testing protocol . . . . . . . . . . . . . . . . 8.2.5 Data analysis . . . . . . . . . . . . . . . . . 8.2.6 Statistical analysis . . . . . . . . . . . . . . 8.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . 8.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . Human-in-the-loop 1-D step frequency optimization 9.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . 9.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Experimental protocol . . . . . . . . . . . . 9.2.2 Experimental system . . . . . . . . . . . . . 9.2.3 Optimization methods . . . . . . . . . . . . 9.2.4 Data analysis . . . . . . . . . . . . . . . . . 9.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . 9.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . .

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10 Human-in-the-loop 2-D hip extension assistance optimization 10.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2.1 Experimental design . . . . . . . . . . . . . . . . . . 10.2.2 Participants . . . . . . . . . . . . . . . . . . . . . . . viii

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10.2.3 Soft exosuit . . . . . . . . . . . . . . . 10.2.4 Actuation platform . . . . . . . . . . . 10.2.5 Sensing and control . . . . . . . . . . 10.2.6 Instantaneous metabolic estimation . 10.2.7 Bayesian Optimization . . . . . . . . . 10.2.8 Metabolic measurement and analysis 10.2.9 Convergence analysis . . . . . . . . . 10.2.10 Ground reaction force . . . . . . . . . 10.2.11 Statistics . . . . . . . . . . . . . . . . . 10.3 Results . . . . . . . . . . . . . . . . . . . . . . 10.3.1 Metabolic rate . . . . . . . . . . . . . . 10.3.2 Convergence time . . . . . . . . . . . 10.3.3 Optimal timing . . . . . . . . . . . . . 10.3.4 Optimal assistive force profile . . . . 10.3.5 Metabolic landscapes . . . . . . . . . 10.4 Discussion . . . . . . . . . . . . . . . . . . . . 10.5 Conclusion . . . . . . . . . . . . . . . . . . . . 11 Conclusion

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12 Future work 154 12.1 Force control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 12.2 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 12.3 Other human subject studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 References

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List of Tables

Table 5.1

Design requirements of actuation system from biological data. . . . . .

Table 6.1 Peak force and peak timing across for ten measured steps for each subject in the human subject test. Peak timing is the ratio of the duration between the moment of the onset timing of maximum hip flexion and the moment of reaching the peak force to average stride time. The target peak force and peak timing are 200 N and 23% shown in our simulation. . . . . . Table 6.2 Desired and measured peak force of different magnitudes of walking experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 6.3 Measured RMS-E of different shapes of walking experiments . . . . . . Table 6.4 Desired and measured peak timings of the shifted profiles of walking experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 6.5 Desired and measured peak force of different magnitudes of jogging experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Table 7.1 Metabolic power and metabolic reduction on hip and multi-joint assistance for eight subjects. Values are mean ± s.e.m., N = 8. P value is acquired from repeated measures ANOVA, P