An individual-specific gait pattern prediction model based on generalized regression neural networks.

Robotics is gaining its popularity in gait rehabilitation. Gait pattern planning is important to ensure that the gait patterns induced by robotic systems are tailored to each individual and varying walking speed. Most research groups planned gait patterns for their robotics systems based on Clinical Gait Analysis (CGA) data. The major problem with the method using the CGA data is that it cannot accommodate inter-subject differences. In addition, CGA data is limited to only one walking speed as per the published data. The objective of this work was to develop an individual-specific gait pattern prediction model for gait pattern planning in the robotic gait rehabilitation systems. The waveforms of lower limb joint angles in the sagittal plane during walking were obtained with a motion capture system. Each waveform was represented and reconstructed by a Fourier coefficient vector which consisted of eleven elements. Generalized regression neural networks (GRNNs) were designed to predict Fourier coefficient vectors from given gait parameters and lower limb anthropometric data. The generated waveforms from the predicted Fourier coefficient vectors were compared to the actual waveforms and CGA waveforms by using the assessment parameters of correlation coefficients, mean absolute deviation (MAD) and threshold absolute deviation (TAD). The results showed that lower limb joint angle waveforms generated by the gait pattern prediction model were closer to the actual waveforms compared to the CGA waveforms.

Subject-specific lower limb waveforms planning via artificial neural network.

Robotic is gaining its popularity in gait rehabilitation. Gait pattern planning is important, in order to ensure the gait patterns induced by robotic systems on the patient are natural and smooth. It is known that the gait parameters (stride length, cadence) are the key factors, which affect gait pattern. However, a systematic methodology for gait pattern planning is missing. Therefore, a gait pattern generation methodology, GaitGen, was proposed in our previous work. In this paper, we introduce a new model to enhance the proposed methodology for generating the joint angle waveforms of the lower limb during walking, with the gait parameters and the lower limb anthropometric data as input. The walking motion was captured with a motion capture system using passive markers. The waveforms of lower limb joint angles were calculated from the experimental data and the waveforms were then decomposed into Fourier coefficients. Therefore, each joint angle waveform can be represented by a Fourier coefficient vector containing eleven elements to facilitate the waveform analysis. Multi-layer perceptron neural networks (MLPNNs) were designed to predict the Fourier coefficient vectors for specific subject and desired gait parameters. Assessment parameters such as correlation coefficient, mean absolute deviation (MAD) and threshold absolute deviation (TAD) were calculated to examine the quality of MLPNNs' prediction. The constructed waveforms from predicted Fourier coefficient vectors were compared with the actual waveforms calculated from experimental data by using the above-mentioned assessment parameters. The results show that the constructed waveforms closely match the experimental waveforms based on the assessment parameter outcomes.