Correlating Sensors and Activities in an Intelligent Environment A Logistic Regression Approach 1st edition by Fahd Al Bin Ali, Prasad Boddupalli, Nigel Davies, Adrian Friday ISBN 3540204183 9783540204183

Correlating Sensors and Activities in an Intelligent Environment: A Logistic Regression Approach 1st edition by Fahd Al-Bin-Ali, Prasad Boddupalli, Nigel Davies, Adrian Friday – Ebook PDF Instant Download/Delivery. 3540204183, 978-3540204183
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ISBN 10:       3540204183
ISBN 13: 978-3540204183
Author:          Fahd Al-Bin-Ali, Prasad Boddupalli, Nigel Davies, Adrian Friday
 

Correlating Sensors and Activities in an Intelligent Environment: A Logistic Regression Approach 1st  Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 Intelligent Environments and Activity Recognition
  • 1.3 The Role of Sensors in Smart Environments
  • 1.4 Research Objectives and Scope
  • 1.5 Structure of the Paper

• Related Work

  • 2.1 Activity Recognition in Intelligent Environments
  • 2.2 Sensor Fusion Techniques for Context Awareness
  • 2.3 Use of Logistic Regression in Activity and Sensor Correlation
  • 2.4 Challenges and Limitations in Activity Recognition
  • 2.5 Overview of Existing Sensor Correlation Models

• Methodology

  • 3.1 Overview of Logistic Regression
  • 3.2 Data Collection and Sensor Setup
    • 3.2.1 Types of Sensors Used
    • 3.2.2 Description of the Intelligent Environment
  • 3.3 Preprocessing of Sensor Data
    • 3.3.1 Data Cleaning and Normalization
    • 3.3.2 Feature Extraction from Sensor Signals
  • 3.4 Logistic Regression Model for Sensor-Activity Correlation
    • 3.4.1 Model Design and Assumptions
    • 3.4.2 Training the Logistic Regression Model
    • 3.4.3 Hyperparameter Tuning and Model Selection

• Sensor-Activity Correlation Analysis

  • 4.1 Identification of Relevant Activities
  • 4.2 Correlation Between Sensor Data and Activities
    • 4.2.1 Multimodal Sensor Integration
    • 4.2.2 Exploring Temporal and Spatial Correlations
  • 4.3 Feature Selection for Activity Prediction
  • 4.4 Evaluation of Model Performance
    • 4.4.1 Accuracy and Precision
    • 4.4.2 Confusion Matrix Analysis
    • 4.4.3 ROC Curve and AUC (Area Under the Curve)

• Experimental Setup and Results

  • 5.1 Experimental Environment and Dataset
    • 5.1.1 Description of the Dataset and Activities
    • 5.1.2 Sensor Types and Configurations
  • 5.2 Evaluation Metrics
    • 5.2.1 Classification Accuracy
    • 5.2.2 Cross-validation and Robustness
  • 5.3 Results of Logistic Regression Model
    • 5.3.1 Comparison of Model Performance with Other Techniques
    • 5.3.2 Sensitivity to Sensor Noise and Data Variability
  • 5.4 Impact of Feature Selection on Performance

• Discussion

  • 6.1 Insights from Correlating Sensors with Activities
  • 6.2 Effectiveness of Logistic Regression for Sensor-Activity Correlation
  • 6.3 Limitations of the Study
    • 6.3.1 Data Quality and Sensor Placement Challenges
    • 6.3.2 Assumptions in the Logistic Regression Model
  • 6.4 Real-world Implications and Applications
    • 6.4.1 Smart Homes and Healthcare
    • 6.4.2 Activity Monitoring and Behavior Analysis
  • 6.5 Future Work and Improvements
    • 6.5.1 Integration of More Sensors and Data Modalities
    • 6.5.2 Exploring Deep Learning for Sensor-Activity Correlation

• Conclusion

  • 7.1 Summary of Findings
  • 7.2 Contributions of the Paper
  • 7.3 Final Remarks and Future Directions

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