Optimal feature set for smartphone-based activity recognition

Keyphrases

• Smartphone-based 100%
• Optimal Feature Selection 100%
• Activity Recognition 100%
• Smartphone 83%
• Human Activity Recognition 50%
• System Complexity 33%
• Recognition Accuracy 33%
• Computational Complexity 33%
• Activity Recognition System 33%
• Performance Complexity 16%
• Static-dynamic 16%
• Wearable Devices 16%
• Human Users 16%
• Going down 16%
• Bayesian Network 16%
• Information Processing Capability 16%
• Built-in Sensor 16%
• Feature Selection 16%
• Energy Consumption 16%
• Mobile Devices 16%
• Smartphone Accelerometer 16%
• Decision Tree 16%
• Activity Classification 16%
• Processing Energy 16%
• Conventional Features 16%
• Efficient Features 16%
• Classification Process 16%
• Individual Activity 16%
• Classification Algorithms 16%
• Going up 16%
• Daily Behavior 16%
• Daily Life 16%
• System Performance 16%

Computer Science

• Activity Recognition 100%
• Human Activity Recognition 60%
• Computational Complexity 40%
• Recognition Accuracy 40%
• System Complexity 40%
• Recognition System 40%
• Classification Process 20%
• Energy Consumption 20%
• Feature Selection 20%
• Bayesian Networks 20%
• Feature Extraction 20%
• Mobile Device 20%
• Processing Capability 20%
• Individual Activity 20%
• Classification Algorithm 20%
• Systems Performance 20%
• Experimental Result 20%
• Decision Trees 20%
• Wearable Device 20%

Engineering

• Optimal Feature 100%
• Computational Complexity 33%
• Recognition Accuracy 33%
• Mobile Phones 33%
• Classification Process 16%
• Fast Walking 16%
• Feature Extraction 16%
• Slow Walking 16%
• Systems Performance 16%
• Daily Life 16%
• Limitations 16%
• Energy Engineering 16%
• Classification Algorithm 16%
• Experimental Result 16%
• Activity Classification 16%