Biomedical subjects

E L Morin

Publications and source records attributed to E L Morin.

4 recordsLinked to original sources

PubMed2004

A suite of objective biomechanical measurement tools for personal load carriage system assessment.

For application to military and civilian needs, Defence Research and Development Canada--Toronto contracted Queen's University, Kingston to develop a suite of biomechanical assessment and analytical tools to supplement human-based load carriage system assessment methods. This suite of tools permitted efficient objective evaluation of biomechanical aspects of load-bearing webbing, vests, packs and their components, and therefore contributed to early system assessment and a rapid iterative design process. This paper is a summary of five assessment and analytical tools. A dynamic load carriage simulator was developed to simulate cadence of walking, jogging and running. The simulator comprised a computer-controlled pneumatic platform that oscillated anthropometrically weighted mannequins of varying dimensions from which measures of skin contact pressure, hip reaction forces and moments and relative pack-person displacements were taken. A stiffness tester for range of motion provided force-displacement data on pack suspension systems. A biomechanical model was used to determine forces and moments on the shoulders and hips, and validated using a static load distribution mannequin. Subjective perceptual rating systems were used gather soldier feedback during a standardized mobility circuit. Objective outcome measures were validated by means of other objective measures (e.g., Optotrak, video, Instron, etc.) and then compared to subjective ratings. This approach led to development of objective performance criteria for load carriage systems and to improvements in load carriage designs that could be used both in the military and in general.

Biomechanical Phenomena↗

PubMed2002

Sampling, noise-reduction and amplitude estimation issues in surface electromyography.

This paper reviews data acquisition and signal processing issues relative to producing an amplitude estimate of surface EMG. The paper covers two principle areas. First, methods for reducing noise, artefact and interference in recorded EMG are described. Wherever possible noise should be reduced at the source via appropriate skin preparation, and the use of well designed active electrodes and signal recording instrumentation. Despite these efforts, some noise will always accompany the desired signal, thus signal processing techniques for noise reduction (e.g. band-pass filtering, adaptive noise cancellation filters and filters based on the wavelet transform) are discussed. Second, methods for estimating the amplitude of the EMG are reviewed. Most advanced, high-fidelity methods consist of six sequential stages: noise rejection/filtering, whitening, multiple-channel combination, amplitude demodulation, smoothing and relinearization. Theoretical and experimental research related to each of the above topics is reviewed and the current recommended practices are described.

Artifacts↗

PubMed1998

Feature-based classification of myoelectric signals using artificial neural networks.

A pattern classification system, designed to separate myoelectric signal records based on contraction tasks, is described. The amplitude of the myoelectric signal during the first 200 ms following the onset of a contraction has a non-random structure that is specific to the task performed. This permits the application of advanced pattern recognition techniques to separate these signals. The pattern classification system described consists of a spectrographic preprocessor, a feature extraction stage and a classifier stage. The preprocessor creates a spectrogram by generating a series of power spectral densities over adjacent time segments of the input signal. The feature extraction stage reduces the dimensionality of the spectrogram by identifying features that correspond to subtle underlying structures in the input signal data. This is realised by a self-organising artificial neural network (ANN) that performs an advanced statistical analysis procedure known as exploratory projection pursuit. The extracted features are then classified by a supervised-learning ANN. An evaluation of the system, in terms of system performance and the complexity of the ANNs, is presented.

Electromyography↗

PubMedDate not supplied

Automatic discrimination of myoelectric signals via parallel cascade identification.

It has recently been shown that it is possible to discriminate accurately among myoelectric signals underlying different muscle contraction types, specifically elbow flexion and extension and forearm pronation and supination. It was reported that once a number of distinctive features had been extracted from the myoelectric signals, a neural network could be trained to distinguish the contraction types with an impressively high accuracy. In the present paper, we show that a technique known as parallel cascade identification can be used to construct classifiers that can also accurately differentiate the contraction types. The use of parallel cascades has the benefit of dispensing with the need for feature extraction, so that raw myoelectric signal data can be used directly. In addition, very little data are required to train the parallel cascades to distinguish accurately novel incoming myoelectric signals. Results of using parallel cascades to distinguish forearm pronation, supination, and elbow flexion are presented.

Electromyography↗