Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A novel feature selection method to improve classification of gene expression data
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques (Computational Intelligence and Its Applications Series)
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
iGAIT: An interactive accelerometer based gait analysis system
Computer Methods and Programs in Biomedicine
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In this paper, we explored the feasibility of analysing gait patterns during the Short Physical Performance Battery test by using an accelerometer to record the movement of the subject. 12 subjects with Complex Regional Pain Syndrome (CRPS) and 10 control subjects were recruited in this study. 21 gait features including temporal, frequency, regularity and symmetric information were extracted from each recording. The differences of each feature value on control subjects and patient subjects were assessed and compared. Features were selected based on the signal to noise ratio (SNR) ranking. Multilayer perceptron neural-networks were employed to differentiate between the normal and abnormal gait patterns. The result shows when using five features the best classification accuracy (97.5%) was achieved. It is feasible to discriminate the patients with CRPS from the control subjects using a small set of gait features extracted from walking acceleration data recorded during the SPPB test.