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Identifying the Gestures of Toddler, Pregnant Woman and Elderly using Segmented Pigeon Hole Feature Extraction Technique and IR-Threshold Classifier
Objectives: The Objective of this research is to develop a feature extractor and a classifier which will identify and classify the gestures of infants, elderly and pregnant woman using Gait Signal re-ceived from wearable electrodes which is positioned on the body of subjects. Methods/Statistical Analysis: Remote health care monitoring is a technology which enables monitoring a person outside usual medical settings i.e., in the house or residence, which may increase access to caretakers or person at home but it will decrease healthcare deliverance costs. Findings: A novel segmented pigeon hole data extraction and reduction technique is proposed for reducing data and feature extraction. Secondly an Iteration Reduced Threshold based Classifier (IR-Threshold Classifier) has been introduced, which classifies the reduced extracted data into Safe and Danger for toddler, Normal and contra for pregnant women and Stable and Fall for elderly. Feature extraction and reduction using Segmented Pigeon Hole algorithm reduced the dataset for this domain. It is compared with bench mark data set and it had produced the significant data reduction. The IR Threshold classifier had shown 95% of accuracy when compared with the other classifiers. Applications/Improvements: This gives the best predominant electrode set by reducing data which will increase the classification accuracy.
Fall and Normal Category, Feature Extraction, IR-Threshold Classifier, Machine Learning Algorithm, Segmented Pigeon Hole, Wearable Electrodes.
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