Human knee joint walking pattern generation using computational intelligence techniques

Computational intelligence techniques (CITs) can be used to generate the human knee joint angle walking pattern in the sagittal plane, useful in medical rehabilitation as a specific reference of normal pattern depending on the subject’s age, mass, height and stride duration. In this paper, the knee joint angle reference curves in the sagittal plane were generated by using three different CITs: artificial neural network, extreme learning machine (ELM) and multi-output support vector regression. The gait pattern of a woman is different of the gait pattern of a man, and consequently, their knee joint angle curves are also different. Thus, it was necessary to train and test each of the three CITs for each gender. The data used by the CIT were obtained from volunteers with healthy gait and with different characteristics (gender, age, height and weight). The volunteers’ knee joint angle curves were collected by a system mainly constituted by a treadmill, two web cameras and passive marks positioned at volunteers’ joints. These gait analyses were made for five different walking speeds. It was observed that the best curves for each gender were generated using the ELM. Therefore, the ELM can be used to generate the normal knee joint angle curves expected for any person with specific characteristics (age, mass, height, stride duration), and physicians can use these specific normal curves for comparison purposes instead of using the standard knee joint angle curves of the literature which do not take into consideration the specific characteristics of the joint angle source.