Application of angular rate gyroscopes as sensors in electrical orthoses for foot drop correction

Functional Electrical Stimulation (FES) is the application of electrical stimulation to neural pathways or muscles in order to achieve an effective muscle contraction with the aim of restoring lost or impaired function. In 1961 Liberson introduced the use of electrical stimulation for foot drop correction, a common condition following a cerebrovascular accident or stroke. Despite growing evidence on the beneficial use of FES for foot drop, and more than 40 years on from Liberson's work, FES systems for foot drop have not gained wide-spread use, and the basic design remains unchanged. It was the aim of this work to investigate the use of alternative sensors and the development of a sensor system that will improve the reliability, ease of use, and cosmetic aspects of a FES foot drop correction system. The proposed method is a novel approach of using a single gyroscope placed at the anterior aspect of the shank to obtain feedback for a FES foot drop correction system. Previous work carried out in the Centre for Biomedical Engineering had demonstrated the potential of the angular velocity gyroscope (Gyro) as an alternative sensor to foot switches. It is believed that the replacement of the heel switch with the gyroscopic sensor would offer several advantages, which could improve system reliability and function. The Gyro is a small and lightweight sensor with potential for further miniaturisation and implantation which can be easily donned and doffed positioning is not very critical with minimal encumbrance to the patient. The nature of the Gyro contributes to its high reliability and long lifetime during which there is little or no deterioration in its performance. The first part of the project involved the development of an automated reference method for gait event detection that can be used to assess the accuracy of the new gyroscopebased sensor. A kinematic-based approach was adopted and the new method was validated using data from 12 subjects. The new algorithm based method was compared to times given by visual inspection and force platforms. Ninety percent of all timings given by the algorithm were within one frame (16.7 ms) when compared to visual inspection. The new method for gait event detection required a thorough understanding of 3D co-

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