Human body activity recognition using wearable inertial sensors integrated with a feature extraction–based machine-learning classification algorithm

2020 ◽  
pp. 095440542093789
Author(s):  
Chih-Ta Yen ◽  
Jia-De Lin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Extraction ◽  
Discriminant Analysis ◽  
Activity Recognition ◽  
Inertial Sensors ◽  
Recognition Accuracy ◽  
Daily Activities ◽  
Linear Discriminant ◽  
Wearable Inertial Sensors

This study employed wearable inertial sensors integrated with an activity-recognition algorithm to recognize six types of daily activities performed by humans, namely walking, ascending stairs, descending stairs, sitting, standing, and lying. The sensor system consisted of a microcontroller, a three-axis accelerometer, and a three-axis gyro; the algorithm involved collecting and normalizing the activity signals. To simplify the calculation process and to maximize the recognition accuracy, the data were preprocessed through linear discriminant analysis; this reduced their dimensionality and captured their features, thereby reducing the feature space of the accelerometer and gyro signals; they were then verified through the use of six classification algorithms. The new contribution is that after feature extraction, data classification results indicated that an artificial neural network was the most stable and effective of the six algorithms. In the experiment, 20 participants equipped the wearable sensors on their waists to record the aforementioned six types of daily activities and to verify the effectiveness of the sensors. According to the cross-validation results, the combination of linear discriminant analysis and an artificial neural network was the most stable classification algorithm for data generalization; its activity-recognition accuracy was 87.37% on the training data and 80.96% on the test data.

scholarly journals HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

2016 ◽  
Vol 38 (1) ◽  
pp. 151-157 ◽  
Author(s):  
BIANCA MACHADO CAMPOS ◽  
ALEXANDRE PIO VIANA ◽  
SILVANA SILVA RED QUINTAL ◽  
CIBELLE DEGEL BARBOSA ◽  
ROGÉRIO FIGUEIREDO DAHER
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discriminant Analysis ◽  
Group Formation ◽  
Psidium Guajava ◽  
Optimal Number ◽  
Heterotic Group ◽  
Linear Discriminant ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

scholarly journals Recognition of Daily Human Activity Using an Artificial Neural Network and Smartwatch

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Min-Cheol Kwon ◽  
Sunwoong Choi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Activity Recognition ◽  
Human Activity ◽  
Body Movement ◽  
Human Activity Recognition ◽  
Daily Activities ◽  
Whole Body ◽  
Wide Range ◽  
Artificial Neural

Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

Slum Dwellers Perception Towards Ujjwala Yojana, a Government-Lead LPG Intervention Programme in India: An Empirical Investigation

2021 ◽  
pp. 239448112110203
Author(s):  
Koustubh Kanti Ray
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discriminant Analysis ◽  
Support Vector ◽  
Intervention Programme ◽  
Linear Discriminant ◽  
Customer Perception ◽  
Slum Dwellers ◽  
Functional Factors ◽  
Artificial Neural

Numerous studies are available in the academic literature that investigates the customer perception under different contexts. In the present research the researcher tries to investigate the customer perception towards the Indian Government-sponsored social programme from the slum dwellers’ prospective. The author believes that the customer perception towards the government-lead liquefied petroleum gas intervention programme is influenced by multiple functional factors. The functional factors include both process or delivery variables and the outcome factors. In order to test the hypothesis, machine learning binary classifiers like logit, support vector machine, linear discriminant analysis, quadratic discriminant analysis and artificial neural network models are adopted. The binary classifier model efficiencies are analysed with multiple performance measurement parameters like accuracy rate, error rate, F-score, precision, kappa coefficient, Matthews correlation coefficient and area under receiver operating characteristic. While evaluating between the degree of accuracy between actual and predicted cases, the model efficiency results indicate a better predictive power of the classifier models. In relative performance of classifier models, artificial neural network outperformed the other models adopted in the empirical research.

Seed classification of three species of amaranth (Amaranthus spp.) using artificial neural network and canonical discriminant analysis

2019 ◽  
Vol 157 (04) ◽  
pp. 333-341 ◽  
Author(s):  
A. Bagheri ◽  
L. Eghbali ◽  
R. Sadrabadi Haghighi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discriminant Analysis ◽  
Recognition Accuracy ◽  
Morphological Characteristics ◽  
Amaranthus Retroflexus ◽  
Canonical Discriminant Analysis ◽  
Identification Accuracy ◽  
Predictor Variables ◽  
Artificial Neural

AbstractThe current study was conducted in 2013 to identify the seeds of three species of Amaranthus, Amaranthus viridis L., Amaranthus retroflexus L. and Amaranthus albus L., by using the artificial neural network (ANN) and canonical discriminant analysis (CDA) methods. To begin with, photographs were taken of the seeds and 13 morphological characteristics of each seed extracted as predictor variables. Backward regression was used to find the most influential variables and seven variables were derived. Thus, predictor variables were divided into two sets of 13 and seven morphological characteristics. The results showed that the recognition accuracy of the ANN made using 13 and seven predictor variables was 81.1 and 80.3%, respectively. Meanwhile, recognition accuracy of the CDA using the seven and 13 predictor variables was 74.0 and 75.7%, respectively. Therefore, in comparison to CDA, ANN showed higher identification accuracy; however, the difference was not statistically significant. Identification accuracy for A. retroflexus was higher using the CDA method than ANN, while the ANN method had higher recognition accuracy for A. viridis than CDA. In addition, use of 13 predictor variables yielded a greater identification accuracy than seven. The results of the current study showed that using seed morphological characteristics extracted by computer vision could be effective for reliable identification of the similar seeds of Amaranthus species.

scholarly journals Online multiclass EEG feature extraction and recognition using modified convolutional neural network method

2021 ◽  
Vol 11 (5) ◽  
pp. 4016
Author(s):  
Haider Abdulkarim ◽  
Mohammed Z. Al-Faiz
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Discriminant Analysis ◽  
Convolutional Neural Network ◽  
Linear Discriminant Analysis ◽  
Majority Voting ◽  
Linear Discriminant ◽  
Emerging Trends ◽  
Accuracy Increase

<p>Many techniques have been introduced to improve both brain-computer interface (BCI) steps: feature extraction and classification. One of the emerging trends in this field is the implementation of deep learning algorithms. There is a limited number of studies that investigated the application of deep learning techniques in electroencephalography (EEG) feature extraction and classification. This work is intended to apply deep learning for both stages: feature extraction and classification. This paper proposes a modified convolutional neural network (CNN) feature extractorclassifier algorithm to recognize four different EEG motor imagery (MI). In addition, a four-class linear discriminant analysis (LDR) classifier model was built and compared to the proposed CNN model. The paper showed very good results with 92.8% accuracy for one EEG four-class MI set and 85.7% for another set. The results showed that the proposed CNN model outperforms multi-class linear discriminant analysis with an accuracy increase of 28.6% and 17.9% for both MI sets, respectively. Moreover, it has been shown that majority voting for five repetitions introduced an accuracy advantage of 15% and 17.2% for both EEG sets, compared with single trials. This confirms that increasing the number of trials for the same MI gesture improves the recognition accuracy</p>

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