Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain
Positive biomechanical outcomes have been reported with lower limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This article presents a...
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| Published in | IEEE transactions on robotics Vol. 39; no. 3; pp. 2170 - 2182 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
IEEE
01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Positive biomechanical outcomes have been reported with lower limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This article presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multiactivity database of ten able-bodied subjects. We demonstrate in live experiments with a new cohort of ten able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (<inline-formula><tex-math notation="LaTeX">N=10</tex-math></inline-formula>, phase root-mean-square error (RMSE): 4.8 <inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula> 2.4%) and a real-world stress test with extremely uneven terrain (<inline-formula><tex-math notation="LaTeX">N=1</tex-math></inline-formula>, phase RMSE: 4.8 <inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula> 2.7%). |
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| AbstractList | Positive biomechanical outcomes have been reported with lower limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This article presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multiactivity database of ten able-bodied subjects. We demonstrate in live experiments with a new cohort of ten able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials ([Formula Omitted], phase root-mean-square error (RMSE): 4.8 [Formula Omitted] 2.4%) and a real-world stress test with extremely uneven terrain ([Formula Omitted], phase RMSE: 4.8 [Formula Omitted] 2.7%). Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in live experiments with a new cohort of 10 able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (N=10, phase RMSE: 4.8 ± 2.4%) and a real-world stress test with extremely uneven terrain (N=1, phase RMSE: 4.8 ± 2.7%). Positive biomechanical outcomes have been reported with lower limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This article presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multiactivity database of ten able-bodied subjects. We demonstrate in live experiments with a new cohort of ten able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (<inline-formula><tex-math notation="LaTeX">N=10</tex-math></inline-formula>, phase root-mean-square error (RMSE): 4.8 <inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula> 2.4%) and a real-world stress test with extremely uneven terrain (<inline-formula><tex-math notation="LaTeX">N=1</tex-math></inline-formula>, phase RMSE: 4.8 <inline-formula><tex-math notation="LaTeX">\pm</tex-math></inline-formula> 2.7%). Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in live experiments with a new cohort of 10 able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (N=10, phase RMSE: 4.8 ± 2.4%) and a real-world stress test with extremely uneven terrain (N=1, phase RMSE: 4.8 ± 2.7%).Positive biomechanical outcomes have been reported with lower-limb exoskeletons in laboratory settings, but these devices have difficulty delivering appropriate assistance in synchrony with human gait as the task or rate of phase progression change in real-world environments. This paper presents a controller for an ankle exoskeleton that uses a data-driven kinematic model to continuously estimate the phase, phase rate, stride length, and ground incline states during locomotion, which enables the real-time adaptation of torque assistance to match human torques observed in a multi-activity database of 10 able-bodied subjects. We demonstrate in live experiments with a new cohort of 10 able-bodied participants that the controller yields phase estimates comparable to the state of the art, while also estimating task variables with similar accuracy to recent machine learning approaches. The implemented controller successfully adapts its assistance in response to changing phase and task variables, both during controlled treadmill trials (N=10, phase RMSE: 4.8 ± 2.4%) and a real-world stress test with extremely uneven terrain (N=1, phase RMSE: 4.8 ± 2.7%). |
| Author | Gregg, Robert D. Medrano, Roberto Leo Rouse, Elliott J. Thomas, Gray Cortright Keais, Connor G. |
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| SubjectTerms | Biomechanics Control Controllers Estimation exoskeleton Exoskeletons Foot Gait Kalman filter Kinematics Legged locomotion Locomotion Machine learning Mathematical models phase Real time Root-mean-square errors Task analysis Terrain Torque |
| Title | Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain |
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