new footdrop correction machine system research

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Research from IEEE Trans Neural Syst Rehabil Eng. Mar 2004;12(1):81-8.

Evaluating robustness of gait event detection based on machine learning

and natural sensors.

Hansen M, Haugland MK, Sinkjaer T.

Alfred Mann Foundation, Valencia, CA 91355, USA.

A real-time system for deriving timing control for functional electrical

stimulation for foot-drop correction, using peripheral nerve activity as

a sensor input, was tested for reliability to investigate the potential

for clinical use. The system, which was previously reported on, was

tested on a hemiplegic subject instrumented with a recording cuff

electrode on the Sural nerve, and a stimulation cuff electrode on the

Peroneal cuff. Implanted devices enabled recording and stimulation

through telelinks. An input domain was derived from the recorded

electroneurogram and fed to a detection algorithm based on an adaptive

logic network for controlling the stimulation timing. The reliability

was tested by letting the subject wear different foot wear and walk on

different surfaces than when the training data was recorded. The

detection system was also evaluated several months after training. The

detection system proved able to successfully detect when walking with

different footwear on varying surfaces up to 374 days after training,

and thereby showed great potential for being clinically useful.