scholarly journals A Feature Selection Approach for Fall Detection Using Various Machine Learning Classifiers

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Tuan Le Minh ◽  
Ly Van Tran ◽  
Son Vu Truong Dao
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Fall Detection ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Selection Approach ◽  
Feature Selection Approach

Machine Learning-Based Pre-Impact Fall Detection Model to Discriminate Various Types of Fall

2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Tae Hyong Kim ◽  
Ahnryul Choi ◽  
Hyun Mu Heo ◽  
Kyungran Kim ◽  
Kyungsuk Lee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Continuous Time ◽  
Classification Accuracy ◽  
Fall Detection ◽  
Series Data ◽  
Machine Learning Classifiers ◽  
Detection Model ◽  
Learning Classifiers ◽  
Proposed Model

Pre-impact fall detection can send alarm service faster to reduce long-lie conditions and decrease the risk of hospitalization. Detecting various types of fall to determine the impact site or direction prior to impact is important because it increases the chance of decreasing the incidence or severity of fall-related injuries. In this study, a robust pre-impact fall detection model was developed to classify various activities and falls as multiclass and its performance was compared with the performance of previous developed models. Twelve healthy subjects participated in this study. All subjects were asked to place an inertial measuring unit module by fixing on a belt near the left iliac crest to collect accelerometer data for each activity. Our novel proposed model consists of feature calculation and infinite latent feature selection (ILFS) algorithm, auto labeling of activities, and application of machine learning classifiers for discrete and continuous time series data. Nine machine-learning classifiers were applied to detect falls prior to impact and derive final detection results by sorting the classifier. Our model showed the highest classification accuracy. Results for the proposed model that could classify as multiclass showed significantly higher average classification accuracy of 99.57 ± 0.01% for discrete data-based classifiers and 99.84 ± 0.02% for continuous time series-based classifiers than previous models (p < 0.01). In the future, multiclass pre-impact fall detection models can be applied to fall protector devices by detecting various activities for sending alerts or immediate feedback reactions to prevent falls.

scholarly journals An Effective Multi-Label Feature Selection Model Towards Eliminating Noisy Features

2020 ◽  
Vol 10 (22) ◽  
pp. 8093
Author(s):  
Jun Wang ◽  
Yuanyuan Xu ◽  
Hengpeng Xu ◽  
Zhe Sun ◽  
Zhenglu Yang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Performance ◽  
Space Structures ◽  
Learning Tasks ◽  
Feature Spaces ◽  
Selection Approach ◽  
Label Correlations ◽  
Feature Selection Approach ◽  
Low Dimensional

Feature selection has devoted a consistently great amount of effort to dimension reduction for various machine learning tasks. Existing feature selection models focus on selecting the most discriminative features for learning targets. However, this strategy is weak in handling two kinds of features, that is, the irrelevant and redundant ones, which are collectively referred to as noisy features. These features may hamper the construction of optimal low-dimensional subspaces and compromise the learning performance of downstream tasks. In this study, we propose a novel multi-label feature selection approach by embedding label correlations (dubbed ELC) to address these issues. Particularly, we extract label correlations for reliable label space structures and employ them to steer feature selection. In this way, label and feature spaces can be expected to be consistent and noisy features can be effectively eliminated. An extensive experimental evaluation on public benchmarks validated the superiority of ELC.

scholarly journals Hybrid Machine Learning Classifiers for Indoor User Localization Problem

2021 ◽  
Vol 10 (3) ◽  
pp. 49-53
Author(s):  
Hamza Turabieh ◽  
Ahmad S. Alghamdi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
User Location

Wi-Fi technology is now everywhere either inside or outside buildings. Using Wi-fi technology introduces an indoor localization service(s) (ILS). Determining indoor user location is a hard and complex problem. Several applications highlight the importance of indoor user localization such as disaster management, health care zones, Internet of Things applications (IoT), and public settlement planning. The measurements of Wi-Fi signal strength (i.e., Received Signal Strength Indicator (RSSI)) can be used to determine indoor user location. In this paper, we proposed a hybrid model between a wrapper feature selection algorithm and machine learning classifiers to determine indoor user location. We employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm as a feature selection to select the most active access point (AP) based on RSSI values. Six different machine learning classifiers were used in this work (i.e., Decision Tree (DT), Support Vector Machine (SVM), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), Ensemble-Bagged Tree (EBaT), and Ensemble Boosted Tree (EBoT)). We examined all classifiers on a public dataset obtained from UCI repository. The obtained results show that EBoT outperforms all other classifiers based on accuracy value/

Sign in / Sign up

Export Citation Format

Share Document