Locomotion Mode Classification Based on Support Vector Machines and Hip Joint Angles: A Feasibility Study for Applications in Wearable Robotics

Intention decoding of locomotion-related activities covers an essential role in the control architecture of active orthotic devices for gait assistance. This work presents a subject-independent classification method, based on support vector machines, for the identification of locomotion-related activities, i.e. overground walking, ascending and descending stairs. The algorithm uses features extracted only from hip angles measured by joint encoders integrated on a lower-limb active orthosis for gait assistance. Different sets of features are tested in order to identify the configuration with better performance. The highest success rate (i.e. 99% of correct classification) is achieved using the maximum number of features, namely seven features. In future works the algorithm based on the identified set of features will be implemented on the real-time controller of the active pelvis orthosis and tested in activities of daily life.