A Symmetry Evaluation Model for Lower Limb Segments During Upper Limb Loading

Weight-bearing is a common behavior, whereas a load impact on human gait symmetry is seldom considered. This study aims to propose and verify a gait symmetry detection model, by using the gait symmetry under different weight-bearing states to establish a weight-bearing symmetry classification rule, and to measure the consistency of movement through multiple gait symmetry experiments. In details, a classifier combined with statistical learning is presented to quantify the signal difference between left and right legs. The quantization result is incorporated as an index to judge the magnitude of symmetry. Meanwhile, a dynamic time warping (DTW) algorithm is introduced to calculate the signal similarity of two legs in time domain as well as another index. The symmetry trend on leg signals is also considered and described as a third index. By casting these indexes in the proposed detection model, we employ several existing nonlinear classification methods to effectively disclose the difference between legs. Moreover, the information between legs can also be extracted through time warping, yielding the detection model competitive. The validity of the model is verified by hypothetical test. Compared with some state-of-the-art methods, our model achieves an improvement over 5% in the consistency measure of the calf. The symmetry measure of calf and thigh is also test. The proposed method outperforms other methods with the improvement of 12.5%.