scholarly journals An empirical study on fall-detection using K-means based training data subset selection

2021 ◽  
Vol 2 (1) ◽  
Keyword(s):  
Empirical Study ◽  
Subset Selection ◽  
Fall Detection ◽  
Training Data ◽  
Data Subset

scholarly journals The Performance of Post-Fall Detection Using the Cross-Dataset: Feature Vectors, Classifiers and Processing Conditions

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4638
Author(s):  
Bummo Koo ◽  
Jongman Kim ◽  
Yejin Nam ◽  
Youngho Kim
Keyword(s):  
Time Series ◽  
Fall Detection ◽  
Discrete Data ◽  
Classification Performance ◽  
Training Data ◽  
Processing Conditions ◽  
Feature Vectors ◽  
External Data ◽  
Public Dataset ◽  
The Cross

In this study, algorithms to detect post-falls were evaluated using the cross-dataset according to feature vectors (time-series and discrete data), classifiers (ANN and SVM), and four different processing conditions (normalization, equalization, increase in the number of training data, and additional training with external data). Three-axis acceleration and angular velocity data were obtained from 30 healthy male subjects by attaching an IMU to the middle of the left and right anterior superior iliac spines (ASIS). Internal and external tests were performed using our lab dataset and SisFall public dataset, respectively. The results showed that ANN and SVM were suitable for the time-series and discrete data, respectively. The classification performance generally decreased, and thus, specific feature vectors from the raw data were necessary when untrained motions were tested using a public dataset. Normalization made SVM and ANN more and less effective, respectively. Equalization increased the sensitivity, even though it did not improve the overall performance. The increase in the number of training data also improved the classification performance. Machine learning was vulnerable to untrained motions, and data of various movements were needed for the training.

Data Subset Selection With Imperfect Multiple Labels

2019 ◽  
Vol 30 (7) ◽  
pp. 2212-2221 ◽  
Author(s):  
Meng Fang ◽  
Tianyi Zhou ◽  
Jie Yin ◽  
Yang Wang ◽  
Dacheng Tao
Keyword(s):  
Subset Selection ◽  
Data Subset

scholarly journals Finding High-Value Training Data Subset Through Differentiable Convex Programming

2021 ◽  
pp. 666-681
Author(s):  
Soumi Das ◽  
Arshdeep Singh ◽  
Saptarshi Chatterjee ◽  
Suparna Bhattacharya ◽  
Sourangshu Bhattacharya
Keyword(s):  
Convex Programming ◽  
Training Data ◽  
Data Subset

scholarly journals Fall detection system based on BiLSTM neural network

2019 ◽  
Vol 7 (5) ◽  
pp. 01-12
Author(s):  
Biao YE ◽  
Lasheng Yu
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Detection System ◽  
Input Sequence ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Training Data ◽  
Sensor Data ◽  
Data Set ◽  
Sequence Modeling

The purpose of this article is to analyze the characteristics of human fall behavior to design a fall detection system. The existing fall detection algorithms have problems such as poor adaptability, single function and difficulty in processing large data and strong randomness. Therefore, a long-term and short-term memory recurrent neural network is used to improve the effect of falling behavior detection by exploring the internal correlation between sensor data. Firstly, the serialization representation method of sensor data, training data and detection input data is designed. The BiLSTM network has the characteristics of strong ability to sequence modeling and it is used to reduce the dimension of the data required by the fall detection model. then, the BiLSTM training algorithm for fall detection and the BiLSTM-based fall detection algorithm convert the fall detection into the classification problem of the input sequence; finally, the BiLSTM-based fall detection system was implemented on the TensorFlow platform. The detection and analysis of system were carried out using a bionic experiment data set which mimics a fall. The experimental results verify that the system can effectively improve the accuracy of fall detection to 90.47%. At the same time, it can effectively detect the behavior of Near-falling, and help to take corresponding protective measures.

AN EMPIRICAL EVALUATION OF REPETITIVE UNDERSAMPLING TECHNIQUES

2010 ◽  
Vol 20 (02) ◽  
pp. 173-195 ◽  
Author(s):  
JASON VAN HULSE ◽  
TAGHI M. KHOSHGOFTAAR ◽  
AMRI NAPOLITANO
Keyword(s):  
Data Mining ◽  
Empirical Study ◽  
Performance Metrics ◽  
Fundamental Problem ◽  
Empirical Evaluation ◽  
Class Imbalance ◽  
Information Loss ◽  
Training Data ◽  
Random Undersampling ◽  
Harmful Effects

Class imbalance is a fundamental problem in data mining and knowledge discovery which is encountered in a wide array of application domains. Random undersampling has been widely used to alleviate the harmful effects of imbalance, however, this technique often leads to a substantial amount of information loss. Repetitive undersampling techniques, which generate an ensemble of models, each trained on a different, undersampled subset of the training data, have been proposed to allieviate this difficulty. This work reviews three repetitive undersampling methods currently used to handle imbalance and presents a detailed and comprehensive empirical study using four different learners, four performance metrics and 15 datasets from various application domains. To our knowledge, this work is the most thorough study of repetitive undersampling techniques.

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