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Updated: May 25, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

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Published on: July 22, 2014

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

Xiaorong Zhang¹, He Huang, Qing Yang

¹Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 19, 2012

Summary

This summary is machine-generated.

This study introduces a neural-machine interface (NMI) for prosthetic legs, enabling real-time decoding of intended movements. The system accurately recognizes locomotion modes like walking and stair climbing for advanced artificial leg control.

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Area of Science:

Biomedical Engineering
Robotics
Neuroscience

Background:

Artificial legs require sophisticated control systems to mimic natural movement.
Decoding user intent in real-time is crucial for intuitive prosthetic limb function.
Existing neural-machine interfaces (NMIs) face challenges in processing speed and signal acquisition.

Purpose of the Study:

To design and implement a novel NMI for artificial legs capable of real-time movement decoding.
To integrate high-speed processing (FPGA) and data acquisition (MCU with ADCs) for enhanced performance.
To develop and evaluate a phase-dependent classifier for accurate recognition of locomotion modes.

Main Methods:

An embedded system integrating an FPGA (Altera Stratix III) and an MCU (Freescale MPC5566) was designed.
The system samples 12 EMG and 6 mechanical signals in real-time.
A complex phase-dependent classifier was developed for locomotion mode recognition.

Main Results:

The NMI successfully sampled 12 EMG and 6 mechanical signals in real-time.
Experimental results demonstrated acceptable performance in classifying three locomotion modes: level-ground walking, stairs ascent, and stairs descent.
The system showed potential for real-time control of artificial legs.

Conclusions:

The designed NMI provides a viable solution for real-time decoding of intended movements in artificial legs.
The integration of FPGA and MCU facilitates efficient data processing and signal acquisition.
This technology holds promise for improving the functionality and user experience of prosthetic limbs.