A special purpose embedded system for neural machine interface for artificial legs

This paper presents a design and implementation of a neural-machine interface (NMI) for artificial legs that can decode amputee´s intended movement in real time. The newly designed NMI integrates an FPGA chip for fast processing and a microcontroller unit (MCU) with multiple on-chip analog-to-digital converters (ADCs) for real-time data sampling. The resulting embedded system is able to sample in real time 12 EMG signals and 6 mechanical signals and execute a special complex phase-dependent classifier for accurate recognition of the user´s intended locomotion modes. The implementation and evaluation are based on Altera´s Stratix III 3S150 FPGA device coupled with Freescale´s MPC5566 MCU. The experimental results for classifying three locomotion modes (level-ground walking, stairs ascent, and stairs descent) based on data collected from an able-bodied human subject have shown acceptable performance for real-time controlling of artificial legs.