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A Feature Selection Approach for Fall Detection Using Various Machine Learning Classifiers
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DOI：10.1109/ACCESS.2021.3105581.png)
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摘要
En 中文
Falls are one of the most serious dangers for elderly people who live alone at home. It has become a widespread issue all across the world. Reliable fall detection systems can help to mitigate the negative consequences of accidentally falling. Several techniques for automatically falling detection machines have been suggested in the past. The existing technologies are classified into three types of fall detectors: wearable sensor-based, ambient device-based, and computer vision-based approaches. This paper focuses on a dataset comprising signals from wearable sensors, ambient sensors, and vision devices. We propose a novel feature subset selection to reduce the number of effective input attributes based on a hybridized metaheuristic - an Adaptive Particle Swarm and Grey Wolf Optimization (APGWO). Classification results use various machine learning classifiers such as Logistic Regression (LR), K-Nearest Neighbor, Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Multilayer Perceptron (MLP), show that the proposed approach is highly effective. Classification accuracy and F-1 score can reach as high as 99% and 96%, respectively.
Keyword:
Feature extraction
Classification algorithms
Prediction algorithms
Senior citizens
Optimization
Fall detection
Accelerometers
Wrapper
feature subset selection
neural network
grey wolf optimization
fall detection
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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