Experimental Evaluation of Variations in Primary Features Used for Accelerometric Context Recognition 1st edition by Ernst Heinz, Kai Kunze, Stefan Sulistyo, Holger Junker, Paul Lukowicz, Gerhard Troster ISBN 3540204183 9783540204183

Experimental Evaluation of Variations in Primary Features Used for Accelerometric Context Recognition 1st edition by Ernst A. Heinz, Kai S. Kunze, Stefan Sulistyo, Holger Junker, Paul Lukowicz, Gerhard Tröster – Ebook PDF Instant Download/Delivery. 3540204183, 978-3540204183
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Product details:

ISBN 10:       3540204183
ISBN 13: 978-3540204183
Author:        Ernst A. Heinz, Kai S. Kunze, Stefan Sulistyo, Holger Junker, Paul Lukowicz, Gerhard Tröster 
 

Experimental Evaluation of Variations in Primary Features Used for Accelerometric Context Recognition 1st Table of contents:

• Introduction

  • 1.1 Background and Motivation
  • 1.2 Importance of Context Recognition in Accelerometry
  • 1.3 Objectives and Scope of the Study
  • 1.4 Paper Structure

• Related Work

  • 2.1 Context Recognition Using Accelerometers
  • 2.2 Primary Features in Accelerometric Context Recognition
  • 2.3 Existing Approaches for Feature Selection and Evaluation
  • 2.4 Challenges in Accelerometric Context Recognition

• Accelerometer Data Collection and Preprocessing

  • 3.1 Description of the Dataset
  • 3.2 Accelerometer Setup and Experimental Conditions
  • 3.3 Data Preprocessing and Normalization
  • 3.4 Feature Extraction from Accelerometer Data
    • 3.4.1 Time-domain Features
    • 3.4.2 Frequency-domain Features
    • 3.4.3 Statistical and Geometric Features

• Feature Variations and Their Impact

  • 4.1 Types of Primary Features Used in Context Recognition
  • 4.2 Variation in Feature Values Across Different Contexts
  • 4.3 Influence of Sensor Placement and Orientation on Feature Variation
  • 4.4 Feature Correlations and Redundancy Analysis
  • 4.5 Selection Criteria for Evaluating Features

• Experimental Setup

  • 5.1 Experimental Design and Hypothesis
  • 5.2 Evaluation Metrics for Context Recognition
    • 5.2.1 Classification Accuracy
    • 5.2.2 Precision, Recall, and F-Score
    • 5.2.3 Confusion Matrix Analysis
  • 5.3 Algorithms and Machine Learning Models Used
    • 5.3.1 Decision Trees
    • 5.3.2 Support Vector Machines
    • 5.3.3 Random Forests
  • 5.4 Cross-validation and Statistical Analysis

• Results and Analysis

  • 6.1 Performance of Different Feature Sets
  • 6.2 Impact of Feature Variations on Classification Accuracy
  • 6.3 Comparison Between Time-domain and Frequency-domain Features
  • 6.4 Sensitivity of Features to Different Contexts
  • 6.5 Statistical Significance of the Results
  • 6.6 Visualization of Feature Impact and Classification Performance

• Discussion

  • 7.1 Interpretation of Experimental Findings
  • 7.2 Effectiveness of Primary Features for Accelerometric Context Recognition
  • 7.3 Implications for Real-world Applications (e.g., Activity Recognition, Healthcare)
  • 7.4 Feature Selection Strategies for Practical Use
  • 7.5 Limitations and Potential Sources of Error

• Conclusion

  • 8.1 Summary of Key Findings
  • 8.2 Recommendations for Feature Selection in Accelerometric Context Recognition
  • 8.3 Directions for Future Research

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