A Clinical Tuning Framework for Continuous Kinematic and Impedance Control of a Powered Knee-Ankle Prosthesis

TL;DR

This work tackles the clinical challenge of personalizing a multimodal powered knee-ankle prosthesis by combining an IKC-based framework with a Clinical Tuning Interface (CTI) to map clinician inputs to continuous-phase/task controller parameters. It defines a hybrid kinematic-impedance controller where stance uses $K_\chi$, $B_\chi$, and $\theta_{eq,\chi}$ while swing relies on a kinematic model with $\theta^d(s,\chi)$, and it extends these to a decoupled sit-stand controller via $\theta_{eq}$ dynamics; IKC is distributed from level-ground data to incline/decline tasks. A case study demonstrates that full tuning can be achieved in under 20 minutes, with rapid iteration and automatic extrapolation to incline walking, producing torque and kinematic changes aligned with clinician intent while maintaining acceptable model fidelity (ankle RMSE improvements and knee RMSE < 15%). The results suggest substantial gains in clinical feasibility for multimodal robotic prostheses, though broader testing and acclimation are needed to validate generalizability and long-term usability.

Abstract

Configuring a prosthetic leg is an integral part of the fitting process, but the personalization of a multi-modal powered knee-ankle prosthesis is often too complex to realize in a clinical environment. This paper develops both the technical means to individualize a hybrid kinematic-impedance controller for variable-incline walking and sit-stand transitions, and an intuitive Clinical Tuning Interface (CTI) that allows prosthetists to directly modify the controller behavior. Utilizing an established method for predicting kinematic gait individuality alongside a new parallel approach for kinetic individuality, we personalize continuous-phase/task models of joint impedance (during stance) and kinematics (during swing) using tuned characteristics exclusively from level-ground walking. To take advantage of this method, we developed a CTI that translates common clinical tuning parameters into model adjustments for the walking and sit-stand controllers. We then conducted a case study where a prosthetist iteratively tuned the powered prosthesis to an above-knee amputee participant in a simulated clinical session involving sit-stand transitions and level walking, from which incline/decline walking features were automatically calibrated. The prosthetist fully tuned the multi-activity prosthesis controller in under 20 min. Each iteration of tuning (i.e., observation, parameter adjustment, and model reprocessing) took 2 min on average for walking and 1 min on average for sit-stand. The tuned behavior changes were appropriately manifested in the commanded prosthesis torques, both at the manually tuned tasks and automatically tuned tasks (inclines). This paper introduces a clinical tuning interface that simplifies the tuning process for multimodal robotic prosthetic legs, reducing the time required from several hours to just 20 min thus improving clinical feasibility.

Figures (9)

• Figure 1: Flowchart of the clinical tuning framework presented in this article. A prior and new analysis of able-bodied data validate the paradigm of individual kinematic/kinetic contributions (IKC) to gait. Following this validation, configurable variables are introduced to two existing hybrid kinematic-impedance controllers---one for variable-incline walking and one for sit-to-stand. A novel clinical tuning interface (CTI) is then introduced to enable manual tuning of the level-ground walking and sit-stand activities, followed by automatic tuning of incline/decline walking using the level-ground controller's kinematic and kinetic IKC. Finally, both tuned and untuned controllers are assessed and compared during sit-stand and walking activities, where the tuned incline controllers were automatically extrapolated from the manually-tuned level-ground controller.
• Figure 2: Individual Kinetic Contributions for one subject across all walking tasks within each joint. The bolded trajectory in each figure is the representative task used as the walking baseline. Note the similarities and differences between inclines.
• Figure 3: (a) The CTI with adjustable parameters. A dropdown menu allows the clinician to quickly select preset profiles. Saving and loading of a profile can be completed instantaneously, and this functionality was used to switch between tuned and untuned profiles for testing. Generate Model generates only the models with parameter changes to process the models quickly. Variance Accounted For (VAF) gauges for each joint allow for immediate knowledge of fit. Export to Model writes the new model to the board on the prosthesis, which can be updated instantly by an engineer when prompted. (b) The advanced options dialog box, where the clinician can separate out sit-to-stand and stand-to-sit support.
• Figure 4: Final individualized parameters for TF01 given the scale displayed in the UI. The shaded region covers values below normative able-bodied parameters.
• Figure 5: Continuously varying impedance parameters across different inclines for the ankle (left) and knee (right). The solid lines represent the untuned baseline configuration, while the dashed are individualized.