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Assessment of the prosthetic gait is an important clinical approach to evaluate the quality and functionality of the prescribed lower limb prosthesis as well as to monitor rehabilitation progresses following limb amputation. Limited access to quantitative assessment tools generally affects the repeatability and consistency of prosthetic gait assessments in clinical practice. The rapidly developing wearable technology industry provides an alternative to objectively quantify prosthetic gait in the unconstrained environment. This study employs a neural network-based model in estimating three-dimensional body segmental orientation of the lower limb amputees during gait. Using a wearable system with inertial sensors attached to the lower limb segments, thirteen individuals with lower limb amputation performed two-minute walk tests on a robotic foot and a passive foot. The proposed model replicates features of a complementary filter to estimate drift free three-dimensional orientation of the intact and prosthetic limbs. The results indicate minimal estimation biases and high correlation, validating the ability of the proposed model to reproduce the properties of a complementary filter while avoiding the drawbacks, most notably in the transverse plane due to gravitational acceleration and magnetic disturbance. Results of this study also demonstrates the capability of the well-trained model to accurately estimate segmental orientation, regardless of amputation level, in different types of locomotion task. [ABSTRACT FROM AUTHOR]

Planar spiral spring is important for the dimensional miniaturisation of motor-based elastic actuators. However, when the stiffness calculation of the spring arm is based on simple beam bending theory, the results possess substantial errors compared with the stiffness obtained from finite-element analysis (FEA). It deems that the errors arise from the spiral length term in the calculation formula. Two Gaussian process regression models are trained to amend this term in the stiffness calculation of spring arm and complete spring. For the former, 216 spring arms' data sets, including different spiral radiuses, pitches, wrap angles and the stiffness from FEA, are employed for training. The latter engages 180 double-arm springs' data sets, including widths instead of wrap angles. The simulation of five spring arms and five planar spiral springs with arbitrary dimensional parameters verifies that the absolute values of errors between the predicted stiffness and the stiffness from FEA are reduced to be less than 0.5% and 2.8%, respectively. A planar spiral spring for a powered ankle–foot prosthesis is designed and manufactured to verify further, of which the predicted value possesses a 3.25% error compared with the measured stiffness. Therefore, the amendment based on the prediction of trained models is available. [ABSTRACT FROM AUTHOR]

In powered ankle–foot prostheses, multilevel hierarchical control systems are usually used with predetermined parameters (tuned during the prosthesis trial). Therefore, control systems cannot adaptively interact with terrains variation where control systems is most effective for ground level walking and less effective for ascending or descending stair/slop. In order to address the control system performance in ever-changing terrains, an adaptive mechanism should be included the control system structure. Here, we present a pilot study to illustrate the applicability of a genetic algorithm-based adaptive fuzzy logic control system. The design method could be divided into two stages: initial knowledge base and membership functions for the genetic pool on the basis of the analysis of biological ankle–foot behaviour. Additionally, the construction of genetic optimization mechanism rules and constraints (fitness function, mutation rats, replacement rate, etc.). Takagi–Sugeno–Kang fuzzy (TSK-fuzzy) inference system is selected because the system structure could depict the character of simple impedance control system. The control system and dynamic model were developed using C code and evaluated using MATLAB/Simulink (2019a). [ABSTRACT FROM AUTHOR]