Hierarchical Optimization for Control of Robotic Knee Prostheses Toward Improved Symmetry of Propulsive Impulse.

Automatically personalizing complex control of robotic prostheses to improve gait performance, such as gait symmetry, is challenging. Recently, human-in-the-loop (HIL) optimization and reinforcement learning (RL) have shown promise in achieving optimized control of wearable robots for each individual user. However, HIL optimization methods lack scalability for high-dimensional space, while RL has mostly focused on optimizing robot kinematic performance. Thus, we propose a novel hierarchical framework to personalize robotic knee prosthesis control and improve overall gait performance. Specifically, in this study the framework was implemented to simultaneously design target knee kinematics and tune 12 impedance control parameters for improved symmetry of propulsive impulse in walking. In our proposed framework, HIL optimization is used to identify an optimal target knee kinematics with respect to symmetry improvement, while RL is leveraged to yield an optimal policy for tuning impedance parameters in high-dimensional space to match the kinematics target. The proposed framework was validated on human subjects, walking with robotic knee prosthesis. The results showed that our design successfully shaped the target knee kinematics as well as configured 12 impedance control parameters to improve propulsive impulse symmetry of the human users. The knee kinematics that yielded best propulsion symmetry did not preserve the normative knee kinematics profile observed in non-disabled individuals, suggesting that restoration of normative joint biomechanics in walking does not necessarily optimize the gait performance of human-prosthesis systems. This new framework for prosthesis control personalization may be extended to other wearable devices or different gait performance optimization goals in the future.

Evaluation of the geometric accuracy of computed tomography and microcomputed tomography of the articular surface of the distal portion of the radius of cats.

OBJECTIVE: To evaluate accuracy of articular surfaces determined by use of 2 perpendicular CT orientations, micro-CT, and laser scanning. SAMPLE: 23 cat cadavers. PROCEDURES: Images of antebrachia were obtained by use of CT (voxel size, 0.6 mm) in longitudinal orientation (CTLO images) and transverse orientation (CTTO images) and by use of micro-CT (voxel size, 0.024 mm) in a longitudinal orientation. Images were reconstructed. Craniocaudal and mediolateral length, radius of curvature, and deviation of the articular surface of the distal portion of the radius of 3-D renderings for CTLO, CTTO, and micro-CT images were compared with results of 3-D renderings acquired with a laser scanner (resolution, 0.025 mm). RESULTS: Measurement of CTLO and CTTO images overestimated craniocaudal and mediolateral length of the articular surface by 4% to 10%. Measurement of micro-CT images underestimated craniocaudal and mediolateral length by 1%. Measurement of CTLO and CTTO images underestimated mediolateral radius of curvature by 15% and overestimated craniocaudal radius of curvature by > 100%; use of micro-CT images underestimated them by 3% and 5%, respectively. Mean ± SD surface deviation was 0.26 ± 0.09 mm for CTLO images, 0.30 ± 0.28 mm for CTTO images, and 0.04 ± 0.02 mm for micro-CT images. CONCLUSIONS AND CLINICAL RELEVANCE: Articular surface models derived from CT images had dimensional errors that approximately matched the voxel size. Thus, CT cannot be used to plan conforming arthroplasties in small joints and could lack precision when used to plan the correction of a limb deformity or repair of a fracture.