Analysis of the Bayesian Gait-State Estimation Problem for Lower-Limb Wearable Robot Sensor Configurations

Overview

Journal IEEE Robot Autom Lett

Date 2022 Jul 5

PMID 35782346

Authors

Roberto Leo Medrano

Gray Cortright Thomas

Elliott J Rouse

Robert D Gregg

Affiliations

Soon will be listed here.

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 (.., 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 , 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.

Citing Articles

Robustification of Bayesian-Inference-Based Gait Estimation for Lower-limb Wearable Robots.

Hsu T, Gregg R, Thomas G IEEE Robot Autom Lett. 2024; 9(3):2104-2111.

PMID: 38313832 PMC: 10831317. DOI: 10.1109/lra.2024.3354558.

Effects of Personalization on Gait-State Tracking Performance Using Extended Kalman Filters.

Montes-Perez J, Thomas G, Gregg R Rep U S. 2023; 2023:6068-6074.

PMID: 38130337 PMC: 10732269. DOI: 10.1109/iros55552.2023.10342498.

Real-Time Gait Phase and Task Estimation for Controlling a Powered Ankle Exoskeleton on Extremely Uneven Terrain.

Medrano R, Thomas G, Keais C, Rouse E, Gregg R IEEE Trans Robot. 2023; 39(3):2170-2182.

PMID: 37304231 PMC: 10249462. DOI: 10.1109/tro.2023.3235584.

References

Zhang J, Fiers P, Witte K, Jackson R, Poggensee K, Atkeson C . Human-in-the-loop optimization of exoskeleton assistance during walking. Science. 2017; 356(6344):1280-1284. DOI: 10.1126/science.aal5054. View

Best T, Embry K, Rouse E, Gregg R . Phase-Variable Control of a Powered Knee-Ankle Prosthesis over Continuously Varying Speeds and Inclines. Rep U S. 2022; 2021:6182-6189. PMC: 8890507. DOI: 10.1109/iros51168.2021.9636180. View

Hu B, Simon A, Hargrove L . Deep generative models with data augmentation to learn robust representations of movement intention for powered leg prostheses. IEEE Trans Med Robot Bionics. 2022; 1(4):267-278. PMC: 9499185. DOI: 10.1109/tmrb.2019.2952148. View

Seo K, Park Y, Lee J, Hyung S, Lee M, Kim J . RNN-Based On-Line Continuous Gait Phase Estimation from Shank-Mounted IMUs to Control Ankle Exoskeletons. IEEE Int Conf Rehabil Robot. 2019; 2019:809-815. DOI: 10.1109/ICORR.2019.8779554. View

Lenzi T, Carrozza M, Agrawal S . Powered hip exoskeletons can reduce the user's hip and ankle muscle activations during walking. IEEE Trans Neural Syst Rehabil Eng. 2013; 21(6):938-48. DOI: 10.1109/TNSRE.2013.2248749. View

Liu M, Zhang F, Huang H . An Adaptive Classification Strategy for Reliable Locomotion Mode Recognition. Sensors (Basel). 2017; 17(9). PMC: 5621085. DOI: 10.3390/s17092020. View

Rezazadeh S, Quintero D, Divekar N, Reznick E, Gray L, Gregg R . A Phase Variable Approach for Improved Rhythmic and Non-Rhythmic Control of a Powered Knee-Ankle Prosthesis. IEEE Access. 2019; 7:109840-109855. PMC: 6813797. DOI: 10.1109/ACCESS.2019.2933614. View

Zhu H, Nesler C, Divekar N, Peddinti V, Gregg R . Design Principles for Compact, Backdrivable Actuation in Partial-Assist Powered Knee Orthoses. IEEE ASME Trans Mechatron. 2021; 26(6):3104-3115. PMC: 8670722. DOI: 10.1109/tmech.2021.3053226. View

Mooney L, Rouse E, Herr H . Autonomous exoskeleton reduces metabolic cost of walking. Annu Int Conf IEEE Eng Med Biol Soc. 2015; 2014:3065-8. DOI: 10.1109/EMBC.2014.6944270. View

10.

Gordon K, Ferris D . Learning to walk with a robotic ankle exoskeleton. J Biomech. 2007; 40(12):2636-44. DOI: 10.1016/j.jbiomech.2006.12.006. View

11.

Young A, Hargrove L . A Classification Method for User-Independent Intent Recognition for Transfemoral Amputees Using Powered Lower Limb Prostheses. IEEE Trans Neural Syst Rehabil Eng. 2015; 24(2):217-25. DOI: 10.1109/TNSRE.2015.2412461. View

12.

Tucker M, Olivier J, Pagel A, Bleuler H, Bouri M, Lambercy O . Control strategies for active lower extremity prosthetics and orthotics: a review. J Neuroeng Rehabil. 2015; 12:1. PMC: 4326520. DOI: 10.1186/1743-0003-12-1. View

13.

Kang I, Molinaro D, Duggal S, Chen Y, Kunapuli P, Young A . Real-Time Gait Phase Estimation for Robotic Hip Exoskeleton Control During Multimodal Locomotion. IEEE Robot Autom Lett. 2021; 6(2):3491-3497. PMC: 8488948. DOI: 10.1109/lra.2021.3062562. View

14.

Ding Y, Kim M, Kuindersma S, Walsh C . Human-in-the-loop optimization of hip assistance with a soft exosuit during walking. Sci Robot. 2020; 3(15). DOI: 10.1126/scirobotics.aar5438. View

15.

Panizzolo F, Freisinger G, Karavas N, Eckert-Erdheim A, Siviy C, Long A . Metabolic cost adaptations during training with a soft exosuit assisting the hip joint. Sci Rep. 2019; 9(1):9779. PMC: 6611879. DOI: 10.1038/s41598-019-45914-5. View

16.

Embry K, Gregg R . Analysis of Continuously Varying Kinematics for Prosthetic Leg Control Applications. IEEE Trans Neural Syst Rehabil Eng. 2020; 29:262-272. PMC: 7920935. DOI: 10.1109/TNSRE.2020.3045003. View

17.

Huang H, Zhang F, Hargrove L, Dou Z, Rogers D, Englehart K . Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion. IEEE Trans Biomed Eng. 2011; 58(10):2867-75. PMC: 3235670. DOI: 10.1109/TBME.2011.2161671. View

18.

Macaluso R, Embry K, Villarreal D, Gregg R . Parameterizing Human Locomotion Across Quasi-Random Treadmill Perturbations and Inclines. IEEE Trans Neural Syst Rehabil Eng. 2021; 29:508-516. PMC: 7938358. DOI: 10.1109/TNSRE.2021.3057877. View

19.

Gregg R, Lenzi T, Hargrove L, Sensinger J . Virtual Constraint Control of a Powered Prosthetic Leg: From Simulation to Experiments with Transfemoral Amputees. IEEE Trans Robot. 2015; 30(6):1455-1471. PMC: 4279455. DOI: 10.1109/TRO.2014.2361937. View

20.

Sawicki G, Beck O, Kang I, Young A . The exoskeleton expansion: improving walking and running economy. J Neuroeng Rehabil. 2020; 17(1):25. PMC: 7029455. DOI: 10.1186/s12984-020-00663-9. View