Log-Likelihood Clustering-Enabled Passive RF Sensing for Residential Activity Recognition

Keyphrases

• Activity Recognition 100%
• Passive RF 100%
• RF Sensing 100%
• Log-likelihood Ratio 100%
• Residential Activities 100%
• Feature Extraction Methods 66%
• Sliding Window 33%
• Hidden Markov Model 33%
• Passive Radio Frequency 33%
• Feature-based 33%
• Pervasive Computing 33%
• Singular Value Decomposition 33%
• Activity Detection 33%
• Principal Coordinate Analysis (PCoA) 33%
• Proposed Feature Extraction 33%
• Time-adaptive 33%
• K-means 33%
• Unique Solution 33%
• Physical Features 33%
• Doppler Shift 33%
• Human-machine Interaction 33%
• E-healthcare 33%
• Traditional Features 33%
• Physical Activity Recognition 33%
• Radio Frequency Sensing 33%
• Passive Radar System 33%
• Invasive Activity 33%
• Medical Data Security 33%
• K-medoids Algorithm 33%

Computer Science

• Activity Recognition 100%
• Feature Extraction 100%
• Sliding Window 50%
• Singular Value 50%
• Pervasive Computing 50%
• Component Analysis 50%
• Principal Components 50%
• Human-Computer Interaction 50%
• Clustering Algorithm 50%
• passive radar 50%
• Healthcare Security 50%
• Unique Solution 50%