Derek T. O'Keeffe

Stimulus artifact removal using a software-based two-stage peak detection algorithm

The analysis of stimulus evoked neuromuscular potentials or m-waves is a useful technique for improved feedback control in functional electrical stimulation systems. Usually, however, these signals are contaminated by stimulus artifact. A novel software technique, which uses a two-stage peak detection algorithm, has been developed to remove the unwanted artifact from the recorded signal. The advantage of the technique is that it can be used on all stimulation artifact-contaminated electroneurophysiologic data provided that the artifact and the biopotential are non-overlapping. The technique does not require any estimation of the stimulus artifact shape or duration. With the developed technique, it is not necessary to record a pure artifact signal for template estimation, a process that can increase the complexity of experimentation. The technique also does not require the recording of any external hardware synchronisation pulses. The method avoids the use of analogue or digital filtering techniques, which endeavour to remove certain high frequency components of the artifact signal, but invariably have difficulty, resulting in the removal of frequencies in the same spectrum as the m-wave. With the new technique the signal is sampled at a high frequency to ensure optimum fidelity. Instrumentation saturation effects due to the artifact can be avoided with careful electrode placement. The technique was fully tested with a wide variety of electrical stimulation parameters (frequency and pulse width) applied to the common peroneal nerve to elicit contraction in the tibialis anterior. The program was also developed to allow batch processing of multiple files, using closed loop feedback correction. The two-stage peak detection artifact removal algorithm is demonstrated as an efficient post-processing technique for acquiring artifact free m-waves.

The development of a potential optimized stimulation intensity envelope for drop foot applications

An optimized stimulation intensity envelope for use in hemiplegic drop foot applications has been developed. The traditional trapezoidal stimulation intensity approach has been examined and found to be inconsistent with the muscle activity patterns observed in healthy gait and therefore unsuitable. Experimental functional electrical stimulation (FES)-elicited tibialis anterior (TA) electromyography (EMG) data was taken over the ankle range of interest (occurring during active dorsiflexion and loading response) while also taking into account the type of TA muscle contraction occurring (concentric, eccentric, and isometric) and the speed of hemiplegic ankle joint rotation. Using the processed data, a model of normalized EMG versus pulsewidth was developed. Implementation of this model showed the unsuitability of the trapezoidal approach in the reproducing of a natural EMG profile. An optimized stimulation intensity profile is proposed which is expected to accurately reproduce the natural TA EMG profile during gait.

Generation of quiz objects (QO) with a quiz engine developer (QED)

This paper introduces a novel open source platform independent quiz engine developer (QED). This is a standards compliant (IMS QTI) system which permits the user to develop questions at the lowest level of granularity (termed quiz objects). Quizzes are a natural symbiotic partner to learning and therefore are equally as important to complete the learning experience. These quiz objects are structured similarly to learning objects (IEEE LOM) and similarly this object-orientated approach supports interoperability and reusability by the development of quiz banks that are searchable and shareable between independent developers. The quiz engine that has been designed is Web-based, cross platform (mobile & desktop) and feature rich and allows several modes of interaction with the user (e.g. template driven, power user). An important aspect of the system is its ability to search individual quiz objects, which allows greater reuse of questions.

A Programmable and Portable NMES Device for Drop Foot Correction and Blood Flow Assist Applications

The Duo-STIM, a new, programmable and portable neuromuscular stimulation system for drop foot correction and blood flow assist applications is presented. The system consists of a programmer unit and a portable, programmable stimulator unit. The portable stimulator features fully programmable, sensor-controlled, constant-voltage, dual-channel stimulation and accommodates a range of customized stimulation profiles. Trapezoidal and free-form adaptive stimulation intensity envelope algorithms are provided for drop foot correction applications, while time dependent and activity dependent algorithms are provided for blood flow assist applications. A variety of sensor types can be used with the portable unit, including force sensitive resistor based foot switches and NMES based accelerometer and gyroscope devices. The paper provides a detailed description of the hardware and block-level system design for both units. The programming and operating procedures for the system are also presented. Finally, functional bench test results for the system are presented.

A Wearable Pelvic Sensor Design for Drop Foot Treatment in Post-Stroke Patients

A novel wearable pelvic sensor design for gait analysis was developed and evaluated in both normal and pathological gait. The device is a hip worn fusion of gyroscopes and accelerometers which allows for monitoring of the periodic vertical rotation of the pelvis during the walking cycle and uses this information as a predictor of gait events such as heel-strike (HS) and toe-off (TO). The gait pattern of two age and gender-matched groups (40-65 years) of 10 healthy subjects (5 male, 5 female) and 10 subjects with hemiplegic drop foot were examined. The pelvic sensor method was correlated against an optical motion system and footswitches, to evaluate the techniques efficacy at detecting foot contact events in walking and hip pattern. Data analysis showed the device was able to predict foot contact events from recorded maximum and minimum pelvic angle (TO: Healthy - 130 ms Hemiplegic - 95 ms; HS: Healthy - 127 ms, Hemiplegic- 96 ms). This ability to detect gait events would allow this sensor design to be used in simplifying drop foot stimulation systems. The proposed method also records the relative range of motion of the pelvis from which useful information on gait symmetry can be obtained and used in ambulatory monitoring or treatment intervention analysis.