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Manzoori AR, Malatesta D, Primavesi J, Ijspeert A, Bouri M. Evaluation of controllers for augmentative hip exoskeletons and their effects on metabolic cost of walking: explicit versus implicit synchronization. Front Bioeng Biotechnol 2024; 12:1324587. [PMID: 38532879 PMCID: PMC10963600 DOI: 10.3389/fbioe.2024.1324587] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/19/2024] [Indexed: 03/28/2024] Open
Abstract
Background: Efficient gait assistance by augmentative exoskeletons depends on reliable control strategies. While numerous control methods and their effects on the metabolic cost of walking have been explored in the literature, the use of different exoskeletons and dissimilar protocols limit direct comparisons. In this article, we present and compare two controllers for hip exoskeletons with different synchronization paradigms. Methods: The implicit-synchronization-based approach, termed the Simple Reflex Controller (SRC), determines the assistance as a function of the relative loading of the feet, resulting in an emerging torque profile continuously assisting extension during stance and flexion during swing. On the other hand, the Hip-Phase-based Torque profile controller (HPT) uses explicit synchronization and estimates the gait cycle percentage based on the hip angle, applying a predefined torque profile consisting of two shorter bursts of assistance during stance and swing. We tested the controllers with 23 naïve healthy participants walking on a treadmill at 4 km ⋅ h-1, without any substantial familiarization. Results: Both controllers significantly reduced the metabolic rate compared to walking with the exoskeleton in passive mode, by 18.0% (SRC, p < 0.001) and 11.6% (HPT, p < 0.001). However, only the SRC led to a significant reduction compared to walking without the exoskeleton (8.8%, p = 0.004). The SRC also provided more mechanical power and led to bigger changes in the hip joint kinematics and walking cadence. Our analysis of mechanical powers based on a whole-body analysis suggested a reduce in ankle push-off under this controller. There was a strong correlation (Pearson's r = 0.778, p < 0.001) between the metabolic savings achieved by each participant with the two controllers. Conclusion: The extended assistance duration provided by the implicitly synchronized SRC enabled greater metabolic reductions compared to the more targeted assistance of the explicitly synchronized HPT. Despite the different assistance profiles and metabolic outcomes, the correlation between the metabolic reductions with the two controllers suggests a difference in individual responsiveness to assistance, prompting more investigations to explore the person-specific factors affecting assistance receptivity.
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Affiliation(s)
| | - Davide Malatesta
- Institute of Sport Sciences, University of Lausanne (UNIL), Lausanne, Switzerland
| | - Julia Primavesi
- Institute of Sport Sciences, University of Lausanne (UNIL), Lausanne, Switzerland
| | | | - Mohamed Bouri
- Biorobotics Laboratory, EPFL, Lausanne, Switzerland
- Translational Neural Engineering Laboratory, EPFL, Lausanne, Switzerland
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Walters K, Thomas GC, Lin J, Gregg RD. An Energetic Approach to Task-Invariant Ankle Exoskeleton Control. PROCEEDINGS OF THE ... IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS. IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS 2023; 2023:6082-6089. [PMID: 38130334 PMCID: PMC10732252 DOI: 10.1109/iros55552.2023.10342136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2023]
Abstract
Robotic ankle exoskeletons have been shown to reduce human effort during walking. However, existing ankle exoskeleton control approaches are limited in their ability to apply biomimetic torque across diverse tasks outside of the controlled lab environment. Energy shaping control can provide task-invariant assistance without estimating the user's state, classifying task, or reproducing pre-defined torque trajectories. In previous work, we showed that an optimally task-invariant energy shaping controller implemented on a knee-ankle exoskeleton reduced the effort of certain muscles for a range of tasks. In this paper, we extend this approach to the sensor suite available at the ankle and present its implementation on a commercially-available, bilateral ankle exoskeleton. An experiment with three healthy subjects walking on a circuit and on a treadmill showed that the controller can approximate biomimetic profiles for varying terrains and task transitions without classifying tasks or switching control modes.
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Affiliation(s)
- Katharine Walters
- Katharine Walters, Gray Thomas, and Robert D. Gregg are with the Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Gray C. Thomas
- Katharine Walters, Gray Thomas, and Robert D. Gregg are with the Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jianping Lin
- Jianping Lin is with the State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Robert D. Gregg
- Katharine Walters, Gray Thomas, and Robert D. Gregg are with the Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA
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Manzoori AR, Ye T, Malatesta D, Lugaz C, Pajot O, Ijspeert A, Bouri M. Gait Phase Estimation in Steady Walking: A Comparative Study of Methods Based on the Phase Portrait of the Hip Angle. IEEE Int Conf Rehabil Robot 2023; 2023:1-6. [PMID: 37941198 DOI: 10.1109/icorr58425.2023.10304747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Accurate real-time estimation of the gait phase (GP) is crucial for many control methods in exoskeletons and prostheses. A class of approaches to GP estimation construct the phase portrait of a segment or joint angle, and use the normalized polar angle of this diagram to estimate the GP. Although several studies have investigated such methods, quantitative information regarding their performance is sparse. In this work, we assess the performance of 3 portrait-based methods in flat and inclined steady walking conditions, using quantitative metrics of accuracy, repeatability and linearity. Two methods use portraits of the hip angle versus angular velocity (AVP), and hip angle versus integral of the angle (IAP). In a novel third method, a linear transformation is applied to the portrait to improve its circularity (CSP). An independent heel-strike (HS) detection algorithm is employed in all algorithms, rather than assuming HSs to occur at a constant point on the portrait. The novel method shows improvements in all metrics, notably significant root-mean-square error reductions compared to IAP (-3%, p < 0.001) and AVP (-2.4%, p < 0.001) in slope, and AVP (-1.61%, p = 0.0015) in flat walking. A non-negligible inter-subject variability is observed between phase angles at HS (equivalent to up to 8.4% of error in the GP), highlighting the importance of explicit HS detection for portrait-based methods.
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Chen X, Chen C, Wang Y, Yang B, Ma T, Leng Y, Fu C. A Piecewise Monotonic Gait Phase Estimation Model for Controlling a Powered Transfemoral Prosthesis in Various Locomotion Modes. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3191945] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Xinxing Chen
- Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, China
| | - Chuheng Chen
- Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, China
| | - Yuxuan Wang
- Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, China
| | - Bowen Yang
- Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, China
| | - Teng Ma
- Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, China
| | - Yuquan Leng
- Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, China
| | - Chenglong Fu
- Key Laboratory of Biomimetic Robotics and Intelligent Systems, Shenzhen, China
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Medrano RL, Thomas GC, Rouse EJ, Gregg RD. Analysis of the Bayesian Gait-State Estimation Problem for Lower-Limb Wearable Robot Sensor Configurations. IEEE Robot Autom Lett 2022; 7:7463-7470. [PMID: 35782346 PMCID: PMC9246062 DOI: 10.1109/lra.2022.3183790] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2023]
Abstract
Many exoskeletons today are primarily tested in controlled, steady-state laboratory conditions that are unrealistic representations of their real-world usage in which walking conditions (e.g., speed, slope, and stride length) change constantly. One potential solution is to detect these changing walking conditions online using Bayesian state estimation to deliver assistance that continuously adapts to the wearer's gait. This paper investigates such an approach in silico, aiming to understand 1) which of the various Bayesian filter assumptions best match the problem, and 2) which gait parameters can be feasibly estimated with different combinations of sensors available to different exoskeleton configurations (pelvis, thigh, shank, and/or foot). Our results suggest that the assumptions of the Extended Kalman Filter are well suited to accurately estimate phase, stride frequency, stride length, and ramp inclination with a wide variety of sparse sensor configurations.
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Affiliation(s)
- Roberto Leo Medrano
- Department of Mechanical Engineering and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Gray Cortright Thomas
- Department of Electrical Engineering and Computer Science and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Elliott J. Rouse
- Department of Mechanical Engineering and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109 USA
| | - Robert D. Gregg
- Department of Electrical Engineering and Computer Science and the Robotics Institute, University of Michigan, Ann Arbor, MI 48109 USA
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Ma T, Wang Y, Chen X, Chen C, Hou Z, Yu H, Fu C. A Piecewise Monotonic Smooth Phase Variable for Speed-Adaptation Control of Powered Knee-Ankle Prostheses. IEEE Robot Autom Lett 2022. [DOI: 10.1109/lra.2022.3182536] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Teng Ma
- Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robotics in Universities, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yuxuan Wang
- Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robotics in Universities, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Xinxing Chen
- Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robotics in Universities, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Chuheng Chen
- Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robotics in Universities, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Zhimin Hou
- Department of Biomedical Engineering, National University of Singapore, SingaporeSingapore
| | - Haoyong Yu
- Department of Biomedical Engineering, National University of Singapore, SingaporeSingapore
| | - Chenglong Fu
- Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robotics in Universities, Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, China
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Embry KR, Gregg RD. Analysis of Continuously Varying Kinematics for Prosthetic Leg Control Applications. IEEE Trans Neural Syst Rehabil Eng 2020; 29:262-272. [PMID: 33320814 DOI: 10.1109/tnsre.2020.3045003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Powered prosthetic legs can improve the quality of life for people with transfemoral amputations by providing net positive work at the knee and ankle, reducing the effort required from the wearer, and making more tasks possible. However, the controllers for these devices use finite state machines that limit their use to a small set of pre-defined tasks that require many hours of tuning for each user. In previous work, we demonstrated that a continuous parameterization of joint kinematics over walking speeds and inclines provides more accurate predictions of reference kinematics for control than a finite state machine. However, our previous work did not account for measurement errors in gait phase, walking speed, and ground incline, nor subject-specific differences in reference kinematics, which occur in practice. In this work, we conduct a pilot experiment to characterize the accuracy of speed and incline measurements using sensors onboard our prototype prosthetic leg and simulate phase measurements on ten able-bodied subjects using archived motion capture data. Our analysis shows that given demonstrated accuracy for speed, incline, and phase estimation, a continuous parameterization provides statistically significantly better predictions of knee and ankle kinematics than a comparable finite state machine, but both methods' primary source of predictive error is subject deviation from average kinematics.
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