Robust phase-based control of prosthetic feet and biologically inspired joint coupling
Final Report Abstract
In this project, we investigated whether asymmetries and effort during level walking can be positively influenced using a novel prosthetic prototype that implements next to a monoarticular actuator at the ankle joint a biarticular actuator that is coupled to the thigh. Additionally, the goal was to develop a method that enables continuous interaction with the prosthesis for level walking, stair climbing, and transitions between these gait modes. To address both goals, a motion dataset (ground reaction forces, joint positions, motion data of limb segments through inertial measurement units, muscle activity) was recorded for a walking track with stairs at three different stair heights. The data shows that transition phases between gait modes exist, which significantly differ from level walking and stair ambulation. Furthermore, stair ascent requires up to 13 times the positive net leg work and stair descent requires up to 13 times the negative net leg work, compared to level walking. This indicates that active prosthetic components for the lower extremities should consider the specific characteristics of stair ascent and descent. Using the data, a prosthetic foot with mono- and biarticular actuation and control was designed. After initial tests with non-amputee subjects, the interaction between mono- and biarticular actuation was examined in individuals with unilateral transtibial amputation. As hypothesized, walking effort could be reduced (9% with a 22% level of biarticular support) and gait asymmetries could be influenced using the biarticular actuator instead of the monoarticular one. The highly individual gait behavior of the users suggests that a more pronounced customization of support throughout the gait cycle is necessary for both the overall behavior of the active ankle joint and the distribution between mono- and biarticular actuators. Furthermore, the dataset was used to develop solutions for gait recognition, phase detection, and stair slope identification. It was found that the low-dimensional concepts based on phase portraits outlined in the proposal do not allow for unambiguous determination. Therefore, beyond the scope of the proposal, higher-dimensional machine learning methods were investigated for their suitability. Kinematic measurements from inertial measurement units (IMUs) were used as input for an Artificial Neural Network (ANN). For validation on non-amputee subjects in various environments, a mobile measurement setup (FRIMU) with IMUs and ground contact sensors was developed. The results demonstrate that gait recognition, phase detection, and stair slope identification are feasible. Moreover, we see great potential for transferring these findings to thematically related fields of wearable robotics.
Publications
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A Novel Approach for Gait Phase Estimation for different Locomotion Modes using Kinematic Shank Information. IFAC-PapersOnLine, 53(2), 8697-8703.
Weigand, Florian; Zeiss, Julian; Grimmer, Martin & Konigorski, Ulrich
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Control of a Transtibial Prosthesis with Monoarticular and Biarticular Actuators. IFAC-PapersOnLine, 53(2), 8689-8696.
Zeiss, Julian; Weigand, Florian; Grimmer, Martin & Konigorski, Ulrich
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Lower limb joint biomechanics-based identification of gait transitions in between level walking and stair ambulation. PLOS ONE, 15(9), e0239148.
Grimmer, Martin; Zeiss, Julian; Weigand, Florian; Zhao, Guoping; Lamm, Sascha; Steil, Martin & Heller, Adrian
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“Cross-validation results for a gait phase estimation with artificial neural networks,” Proceedings on Automation in Medical Engineering, vol. 1, no. 1, pp. 026–026, 2020
F. Weigand, J. Zeiss, U. Konigorski & M. Grimmer
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“Combined estimation of gait phase and stair slope utilizing time history data,” AUTOMED - Automation in Medical Engineering, 2021
F. Weigand, J. Zeiss, M. Grimmer & U. Konigorski
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Continuous locomotion mode recognition and gait phase estimation based on a shank-mounted IMU with artificial neural networks. 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 12744-12751. IEEE.
Weigand, Florian; Höhl, Andreas; Zeiss, Julian; Konigorski, Ulrich & Grimmer, Martin
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Exploring surface electromyography (EMG) as a feedback variable for the human-in-the-loop optimization of lower limb wearable robotics. Frontiers in Neurorobotics, 16.
Grimmer, Martin; Zeiss, Julian; Weigand, Florian & Zhao, Guoping
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Design and evaluation of a powered prosthetic foot with monoarticular and biara ticular actuation. Technische Universität Darmstadt, 2023.
J. Zeiss
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Exoskeleton developments at the Technical University of Darmstadt. International Symposium on Technikpsychologie (TecPsy) 2023, 2023), 82-94. Sciendo.
Grimmer, Martin; Stasica, Maximilian & Zhao, Guoping
