High Accuracy Human Activity Monitoring Using Neural Network

Sharma, Annapurna; Lee, Young-Dong; Chung, Wan-Young

doi:10.1109/iccit.2008.394

Cited by 39 publications

(12 citation statements)

References 7 publications

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“…If a modelling package is missing, there is a prompt to install it. The package accommodates tools for data splitting, pre-processing, feature selection, model tuning using resampling, variable importance estimation, as well as other functionality [97,98]. A classification tree algorithm is a nonparametric approach.…”

Section: Classification and Regression Training (Caret) Packagementioning

confidence: 99%

“…We will compare four classifiers method with various features to select the best classifiers method based on the accuracy (5) g(x) = sign f (x) of each classifier. The whole work has been done in R [97,98] a free software programming language that is specially developed for statistical computing and graphics.…”

Section: Classification and Regression Training (Caret) Packagementioning

confidence: 99%

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Selecting critical features for data classification based on machine learning methods

et al. 2020

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In machine learning problems, high dimensional data, especially in terms of many features, is increasingly these days [1]. Many researchers focus on the experiment to solve these problems. Besides, to extract important features from these high dimensional of variables and data. The statistical techniques were used to minimize noise and redundant data. Nevertheless, we do not use all the features to train a model. We may improve our model with the features correlated and non-redundant, so feature selection plays an important role.

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Section: Classification and Regression Training (Caret) Packagementioning

confidence: 99%

Section: Classification and Regression Training (Caret) Packagementioning

confidence: 99%

Selecting critical features for data classification based on machine learning methods

et al. 2020

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“…In this paper, we considered 10, 20, and 40 hidden neurons. The final layer is the output layer that depends upon the number of class labels in the classification problem [64]. …”

Section: Appendix A1 Decision Treementioning

confidence: 99%

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Janidarmian

Fekr

Radecka

et al. 2017

Sensors

174

145

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Sensor-based motion recognition integrates the emerging area of wearable sensors with novel machine learning techniques to make sense of low-level sensor data and provide rich contextual information in a real-life application. Although Human Activity Recognition (HAR) problem has been drawing the attention of researchers, it is still a subject of much debate due to the diverse nature of human activities and their tracking methods. Finding the best predictive model in this problem while considering different sources of heterogeneities can be very difficult to analyze theoretically, which stresses the need of an experimental study. Therefore, in this paper, we first create the most complete dataset, focusing on accelerometer sensors, with various sources of heterogeneities. We then conduct an extensive analysis on feature representations and classification techniques (the most comprehensive comparison yet with 293 classifiers) for activity recognition. Principal component analysis is applied to reduce the feature vector dimension while keeping essential information. The average classification accuracy of eight sensor positions is reported to be 96.44% ± 1.62% with 10-fold evaluation, whereas accuracy of 79.92% ± 9.68% is reached in the subject-independent evaluation. This study presents significant evidence that we can build predictive models for HAR problem under more realistic conditions, and still achieve highly accurate results.

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“…), frequency domain features (e.g., Fourier Transform, Discrete Cosine Transform), and others (Principal Component Analysis, Linear Discriminant Analysis, Autoregressive Model) [6]. For activity classification, there are numerous classic methods, such as decision tree [23], Bayesian [28], neural networks [29], k-nearest neighbors [30], regression methods [31], support vector machines [32], and Markov models [33].…”

Section: Related Workmentioning

confidence: 99%

Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network

Zhou

Yang

2019

Sensors

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In the indoor environment, the activity of the pedestrian can reflect some semantic information. These activities can be used as the landmarks for indoor localization. In this paper, we propose a pedestrian activities recognition method based on a convolutional neural network. A new convolutional neural network has been designed to learn the proper features automatically. Experiments show that the proposed method achieves approximately 98% accuracy in about 2 s in identifying nine types of activities, including still, walk, upstairs, up elevator, up escalator, down elevator, down escalator, downstairs and turning. Moreover, we have built a pedestrian activity database, which contains more than 6 GB of data of accelerometers, magnetometers, gyroscopes and barometers collected with various types of smartphones. We will make it public to contribute to academic research.

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High Accuracy Human Activity Monitoring Using Neural Network

Cited by 39 publications

References 7 publications

Selecting critical features for data classification based on machine learning methods

Selecting critical features for data classification based on machine learning methods

A Comprehensive Analysis on Wearable Acceleration Sensors in Human Activity Recognition

Smartphone-Based Activity Recognition for Indoor Localization Using a Convolutional Neural Network

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