Real-Time Data-Driven Gait Phase Detection Using Infinite Gaussian Mixture Model and Parallel Particle Filter

2016 ◽  
Author(s):  
Ioannis Papavasileiou ◽  
Wenlong Zhang ◽  
Song Han
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Data Driven ◽  
Phase Detection ◽  
Time Data ◽  
Real Time Data ◽  
Gait Phase

scholarly journals Machine Learning based Improved Gaussian Mixture Model for IoT Real-Time Data Analysis

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Sivadi Sivadi ◽  
Moorthy Moorthy ◽  
Vijender Solanki
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Real Time ◽  
Gaussian Mixture Model ◽  
Gaussian Mixture ◽  
Sensor Data ◽  
Cloud Platform ◽  
Time Data ◽  
Huge Amount ◽  
Real Time Data

Introduction: The article is the product of the research “Due to the increase in popularity of Internet of Things (IoT), a huge amount of sensor data is being generated from various smart city applications”, developed at Pondicherry University in the year 2019. Problem:To acquire and analyze the huge amount of sensor-generated data effectively is a significant problem when processing the data. Objective:  To propose a novel framework for IoT sensor data analysis using machine learning based improved Gaussian Mixture Model (GMM) by acquired real-time data.  Methodology:In this paper, the clustering based GMM models are used to find the density patterns on a daily or weekly basis for user requirements. The ThingSpeak cloud platform used for performing analysis and visualizations. Results:An analysis has been performed on the proposed mechanism implemented on real-time traffic data with Accuracy, Precision, Recall, and F-Score as measures. Conclusions:The results indicate that the proposed mechanism is efficient when compared with the state-of-the-art schemes. Originality:Applying GMM and ThingSpeak Cloud platform to perform analysis on IoT real-time data is the first approach to find traffic density patterns on busy roads. Restrictions:There is a need to develop the application for mobile users to find the optimal traffic routes based on density patterns. The authors could not concentrate on the security aspect for finding density patterns.

Heart Watch: Dynamical Systems Based Real Time Data Driven ECG Synthesis

2021 ◽  
Author(s):  
Ashutosh Simha ◽  
Suryansh Sharma ◽  
Sujay Narayana ◽  
R. Venkatesh Prasad
Keyword(s):  
Dynamical Systems ◽  
Real Time ◽  
Data Driven ◽  
Time Data ◽  
Real Time Data

A REAL-TIME DATA-DRIVEN CONTROL SYSTEM FOR MULTI-MOTOR-DRIVEN MECHANISMS

2017 ◽  
Vol 32 (6) ◽  
Author(s):  
Huashan Liu ◽  
Lingbin Zeng ◽  
Wuneng Zhou ◽  
Shiqiang Zhu
Keyword(s):  
Control System ◽  
Real Time ◽  
Data Driven ◽  
Time Data ◽  
Real Time Data

An Improved Algorithm for Real-Time Moving Target Detection Based on Gaussian Mixture Model

2014 ◽  
Vol 599-601 ◽  
pp. 814-818 ◽  
Author(s):  
Xue Yuan Chen ◽  
Xia Fu Lv ◽  
Jie Liu
Keyword(s):  
Real Time ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Detection System ◽  
Gaussian Mixture ◽  
Learning Rate ◽  
Model Parameters ◽  
Illumination Variation ◽  
Matching Algorithm ◽  
Passive Learning

Gaussian Mixture Model is a popular method to detect moving targets for static cameras. Since the traditional Gaussian Mixture Model has a poor adaptability when the illumination is changing in the scene and has passive learning rate, this paper describes a method that can detect illumination variation and update the learning rate adaptively. It proposes an approach which uses the color histogram matching algorithm and adjusts the learning rate automatically after introducing illumination variation factor and model parameters. Furthermore, the proposed method can select the number of describing model component adaptively, so this method reduced the computation complexity and improved the real-time performance. The experiment results indicate that the detection system gets better robustness, adaptability and stability.

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