Classification of gait patterns in the time domain using accelerometer signal

Lower limb amputees suffer from mobility limitations that lead to a degradation of their quality of life. Recently, inertial measurement units have been widely used for the analysis and recognition of gait activities which are two essential works for the control of lower limb prostheses. In this paper, accelerometer signals from a dataset are used to recognize gait activities. Five lower limb movements are classified and recognized using linear discriminant analysis (LDA) for dimension reduction and support vector machine (SVM) based on a linear function kernel for classification, respectively. The results prove that classification based on time-domain features and accelerometer signals can achieve 95.69% recognition accuracy for gait activity recognition.