Towards ubiquitous mobile-computing-based artificial leg control

This paper presents a rapid prototype approach for the development of a real-time capable neural-machine-interface (NMI) for control of artificial legs based on mobile processor technology (Intel Atom™ Z530 Processor.) By effectively exploiting the architectural features of a mobile embedded CPU, we implemented a decision-making algorithm, based on neuromuscular-mechanical fusion and gait phase-dependent support vector machines (SVM) classification to meet the demanding performance constraints. To demonstrate the feasibility of a real-time mobile computing based NMI, real-time experiments were performed on an able bodied subject with window increments of 50ms. The experiments showed that the mobile computing based NMI provided fast and accurate classifications of four major human locomotion tasks (level-ground walking, stair ascent, stair descent, and standing) and a 46X speedup over an equivalent MATLAB implementation. The testing yielded accuracies of 96.31% with low power consumption. An offline analysis showed the accuracy could be increased to 98.87% with minor modifications to the application.