Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data

Keyphrases

• Activity Profile 33%
• AI-driven 100%
• Automatic Feature Extraction 33%
• Close Distance 33%
• Convolutional Long Short-term Memory (ConvLSTM) 33%
• Deep Neural Network 33%
• E-healthcare 33%
• Healthcare 100%
• Human Activity Recognition 100%
• Image Stream 33%
• Infrared Frames 33%
• Infrared Sensor 100%
• K-nearest 33%
• Logistic Regression 33%
• Low-resolution Infrared Images 33%
• Multi-channel 33%
• Multiple Subjects 33%
• Networking Strategies 33%
• Noise Reduction Technique 66%
• Number of Sensors 33%
• Optimal Data 33%
• Optimum number 33%
• Orthogonal Transformation 33%
• Periodic Noise 66%
• Random Forest 33%
• Results-based 33%
• Sensor Data 100%
• Sensor Displacement 33%
• Sensor Layout 33%
• Sensor Robustness 33%
• Sensor Systems 33%
• Single-side 33%
• Spatio-temporal Activity 33%
• Support Vector Machine 33%
• Texture-based 33%
• Transformation-based 33%
• Vectorized 33%

Engineering

• Analysis Strategy 50%
• Artificial Intelligence 100%
• Collected Data 50%
• Deep Neural Network 50%
• Displacement Sensor 50%
• Feature Extraction 50%
• Infrared Sensor 100%
• Long Short-Term Memory 50%
• Lstm 50%
• Multichannel 50%
• Nearest Neighbor 50%
• Optimum Number 50%
• Potential Application 50%
• Random Forest 50%
• Sensor Data 100%
• Sensor Layout 50%
• Sensor System 50%
• Support Vector Machine 50%

Computer Science

• Artificial Intelligence 100%
• Collected Data 33%
• Deep Neural Network 33%
• Feature Extraction 33%
• Human Activity Recognition 100%
• Logistic Regression 33%
• Long Short-Term Memory Network 33%
• Network Strategy 33%
• Orthogonal Transformation 33%
• Potential Application 33%
• Random Decision Forest 33%
• Support Vector Machine 33%