This paper presents an upper-body motion mode recognition method based on inertial measurement units (IMUs) using cascaded classification approaches and integrated machine learning algorithms. The proposed method is designed to be applied on a dynamic spine brace in the future to assess its usability. This study focuses on the problem of classifying upper-body motion modes by using four IMUs worn on the upper-body of the subjects. Six locomotion modes and ten locomotion transitions were investigated. A quadratic discriminant analysis (QDA) classifier and a support vector machine (SVM) classifier were deployed in our study. With selected cascade classification strategies, the system is demonstrated to achieve a satisfactory performance with an average of 96.77%(QDA) and 97.64%(SVM) recognition accuracy. The obtained results prove the effectiveness of the proposed method.