Real-Time Control of Ankle Orthosis Assistance Using a Locomotion Mode Prediction Tool

Robotic assistive devices have been equipped with locomotion mode (LM) decoding tools to adapt their assistance according to the users locomotion needs. However, most of the LM decoding tools are insufficient to predict the upcoming LM in advance; and do not consider the typically slow speeds of neurologically impaired users. This study aims to address these shortcomings by introducing an LM decoding tool to predict, in real-time, four LMs (standing, level-ground walking, stair descent, and stair ascent) when using a robotic assistive device (SmartOs system) at slow speeds. Thigh and shank segment angles feeding a Long Short-Term Memory provided the highest performance (accuracies of 98.6% and 97.2%, for able-bodied and stroke subjects, respectively). An LM-driven position control strategy was developed to assist the users based on the decoded LM. Real-time evaluations were performed under different speeds (self-selected and controlled), scenarios (indoor and outdoor), and assistance modes (zero-torque and LM-driven position controls). The proposed LM decoding tool showed low computational loads (1.58 1 0.42 ms). The SmartOs system was able to predict and adapt its assistance by 295 1 170 ms before the user entered the new LM. The proposed control strategy is a step towards LM-based assistance for stroke patients.