NDLI: Conditional restricted Boltzmann machine as a generative model for body-worn sensor signals

Content Provider	IET Digital Library
Author	Karakus, Erkan Kose, Hatice
Abstract	Sensor-based human activity classification requires time and frequency domain feature extraction techniques. The set of choice in time and frequency domain features may have a significant impact on the overall classification accuracy. Another problem is to train deep learning models with sufficient dataset. The use of generative models eliminates the requirement of choosing certain features of the signal. As a generative model, restricted Boltzmann machine (RBM) is an energy-based probabilistic graphical model which factorises the probability distribution of a random variable over a binary probability distribution. Conditional restricted Boltzmann machines (CRBMs) is an extension to RBM, which can capture temporal information in time-series signals and can be deployed as a generative model in classification. In this study, the authors show how CRBMs can be trained to learn signal features. They present four generative model training results, RBM, CRBM, generative adversarial network, Wasserstein generative adversarial network – gradient penalty and compare the models' performances with a performance criterion. They show that the CRBM model can generate signals closest to true signals with a significantly higher success rate as compared to other presented generative models. They present a statistical analysis of the findings and show that the findings significantly hold.
Starting Page	725
Ending Page	736
Page Count	12
ISSN	17519675
Volume Number	14
e-ISSN	17519683
Issue Number	Issue 10, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-spr/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2020.0154
Journal	IET Signal Processing
Publisher Date	2021-01-05
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Binary Probability Distribution Biology And Medical Computing Biomedical Engineering Biomedical Measurement And Imaging Body-worn Sensor Signal Boltzmann Machines Classification Accuracy Classification Model Combinatorial Mathematics Conditional Restricted Boltzmann Machine CRBM Model Deep Learning (artificial Intelligence) Deep Learning-based Classification Technique Digital Signal Processing Energy-based Probabilistic Graphical Model Feature Extraction Frequency Domain Analysis Frequency Domain Feature Frequency Domain Feature Extraction Generative Model Training Gradient Penalty Graph Theory Mathematical Analysis Medical Signal Processing Performance Criterion Probability Theory Random Processes Random Variable RBM Sensor-based Human Activity Recognition Problem Set Theory Signal Classification Signal Feature Signal Processing And Detection Signal Processing Technique Statistical Analysis Statistical Distribution Statistics Stochastic Linearised SCUC Time Domain Analysis Time Domain Feature Time Series Time-series Signal Wasserstein Generative Adversarial Network
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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