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Implementation study of wearable sensors for human activity recognition systems

Implementation study of wearable sensors for human activity recognition systems

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This chapter addresses a number of activity recognition methods for a wearable sensor system. Three methods for data transmission, namely `stream-based', `feature-based' and `threshold-based' scenarios are applied to study the accuracy against energy efficiency of transmission and processing power that affects the mote's battery lifetime. The impact of variation of sampling frequency and data transmission rate on energy consumption of motes is also analysed for each method. This study leads the authors to propose a cross-layer optimisation of an activity recognition system for provisioning acceptable levels of accuracy and energy efficiency.

Chapter Contents:

  • 4.1 Introduction
  • 4.2 Application requirements
  • 4.2.1 Sensory information
  • 4.2.1.1 Privacy
  • 4.2.1.2 Sensor hardware and measurement circuits
  • 4.2.1.3 Sensor characteristics
  • 4.2.1.4 Types of sensor for HAR systems
  • 4.2.1.5 Number of sensors and sensor placement
  • 4.2.1.6 Adjusting sampling rate
  • 4.2.1.7 Sensor limitations
  • 4.2.2 Data collection phase
  • 4.2.2.1 Controlled/supervised
  • 4.2.2.2 Uncontrolled/unsupervised
  • 4.2.2.3 Controlled vs. uncontrolled
  • 4.2.3 Activity set
  • 4.2.4 Energy consumption
  • 4.2.4.1 Sources of energy consumption
  • 4.2.4.2 Energy-efficient design
  • 4.2.5 Processing
  • 4.2.6 Obtrusiveness and user's quality of experience
  • 4.2.7 Trade-offs
  • 4.3 Recognition architecture
  • 4.3.1 Sensor data acquisition and preprocessing
  • 4.3.2 Data segmentation
  • 4.3.2.1 Sliding window
  • 4.3.2.2 Energy-based segmentation
  • 4.3.2.3 Additional sensors and contextual sources
  • 4.3.2.4 Top-down
  • 4.3.2.5 Bottom-up
  • 4.3.3 Feature extraction
  • 4.3.3.1 Intuitive approach
  • 4.3.3.2 Statistical approach
  • 4.3.3.3 Wavelet approach
  • 4.3.4 Feature selection
  • 4.3.4.1 Filter approach
  • 4.3.4.2 Wrapper approach
  • 4.3.4.3 Embedded approach
  • 4.3.5 Classification methods
  • 4.3.5.1 Threshold-based classification
  • 4.3.5.2 Machine learning
  • 4.3.5.3 Transfer learning
  • 4.3.5.4 Deep learning
  • 4.3.6 Evaluating HAR systems
  • 4.3.6.1 Performance metrics
  • 4.3.6.2 Error analysis
  • 4.4 Communication platforms
  • 4.4.1 Tier-1
  • 4.4.2 Tier-2
  • 4.4.3 Tier-3
  • 4.5 HAR open problems
  • 4.5.1 Recognition challenges
  • 4.5.2 Hardware challenges
  • 4.5.3 Communication challenges
  • 4.6 Summary
  • References

Inspec keywords: health hazards; assisted living; optimisation; patient monitoring; wireless sensor networks

Other keywords: mote; activity recognition system; energy efficiency; sampling frequency; activity recognition methods; wearable sensors; wearable sensor system; implementation study; data transmission; processing power; threshold-based scenarios; human activity recognition systems

Subjects: Biology and medical computing; Optimisation techniques; Wireless sensor networks; Biomedical engineering; Optimisation techniques

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