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Objective: Semi-active exoskeletons combining lightweight, low powered actuators and passive-elastic elements are a promising approach to portable robotic assistance during locomotion. Here, we introduce a novel semi-active hip exoskeleton concept and evaluate human walking performance across a range of parameters using a tethered robotic testbed. Methods : We emulated semi-active hip exoskeleton (exo) assistance by applying a virtual torsional spring with a fixed rotational stiffness and an equilibrium angle established in terminal swing phase (i.e., via pre-tension into stance). We performed a 2-D sweep of spring stiffness x equilibrium position parameters (30 combinations) across walking speed (1.0, 1.3, and 1.6 m/s) and measured metabolic rate to identify device parameters for optimal metabolic benefit. Results : At each speed, optimal exoskeleton spring settings provided a ∼10% metabolic benefit compared to zero-impedance (ZI). Higher walking speeds required higher exoskeleton stiffness and lower equilibrium angle for maximal metabolic benefit. Optimal parameters tuned to each individual (user-dependent) provided significantly larger metabolic benefit than the average-best settings (user-independent) at all speeds except the fastest (p = 0.021, p = 0.001, and p = 0.098 at 1.0, 1.3, and 1.6 m/s, respectively). We found significant correlation between changes in user's muscle activity and changes in metabolic rate due to exoskeleton assistance, especially for muscles crossing the hip joint. Conclusion : A semi-active hip exoskeleton with spring-parameters personalized to each user could provide metabolic benefit across functional walking speeds. Minimizing muscle activity local to the exoskeleton is a promising approach for tuning assistance on-line on a user-dependent basis.
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Extremum Seeking Control for Stiffness Auto-Tuning of a Quasi-Passive Ankle Exoskeleton
Recently, it has been shown that light-weight, passive, ankle exoskeletons with spring-based energy store-and-release mechanisms can reduce the muscular effort of human walking. The stiffness of the spring in such a device must be properly tuned in order to minimize the muscular effort. However, this muscular effort changes for different locomotion conditions (e.g., walking speed), causing the optimal spring stiffness to vary as well. Existing passive exoskeletons have a fixed stiffness during operation, preventing it from responding to changes in walking conditions. Thus, there is a need of a device and auto-tuning algorithm that minimizes the muscular effort across different walking conditions, while preserving the advantages of passive exoskeletons. In this letter, we developed a quasi-passive ankle exoskeleton with a variable stiffness mechanism capable of self-tuning. As the relationship between the muscular effort and the optimal spring stiffness across different walking speeds is not known a priori, a model-free, discrete-time extremum seeking control (ESC) algorithm was implemented for real-time optimization of spring stiffness. Experiments with an able-bodied subject demonstrate that as the walking speed of the user changes, ESC automatically tunes the torsional stiffness about the ankle joint. The average RMS EMG readings of tibialis anterior and soleus muscles at slow walking speed decreased by 26.48% and 7.42%, respectively.
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- PAR ID:
- 10159541
- Date Published:
- Journal Name:
- IEEE Robotics and Automation Letters
- Volume:
- 5
- Issue:
- 3
- ISSN:
- 2377-3774
- Page Range / eLocation ID:
- 4604 - 4611
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/LRA.2020.3001541
