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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Comparing Sampling Strategies for Tackling Imbalanced Data in Human Activity Recognition.

Fayez Alharbi1,2, Lahcen Ouarbya2, Jamie A Ward2

  • 1Computer Sciences and Information Technology College, Majmaah University, Al Majmaah 15341, Saudi Arabia.

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Summary

Human activity recognition (HAR) using wearable sensors faces challenges with imbalanced datasets. Novel hybrid sampling methods (DBM, NDBM, CBM) significantly improve classifier performance by generating diverse synthetic data, enhancing F1 scores by 9-20%.

Keywords:
activity recognitionimbalanced activitiessampling methodswearable sensors

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Area of Science:

  • Machine Learning
  • Wearable Sensor Technology
  • Data Science

Background:

  • Human Activity Recognition (HAR) leverages wearable sensors like smartphones and smartwatches for applications in healthcare, physical therapy, and fitness.
  • A key challenge in HAR, especially with supervised learning, is the inherent class imbalance in datasets, where some activities are less frequent than others.
  • This imbalance hinders HAR classifiers' ability to accurately recognize minority activities.

Purpose of the Study:

  • To introduce three novel hybrid sampling strategies designed to generate diverse synthetic data and address class imbalance in HAR datasets.
  • To evaluate the effectiveness of these hybrid methods against individual constituent methods and baseline approaches.

Main Methods:

  • Distance-based Method (DBM): Combines Synthetic Minority Oversampling Techniques (SMOTE) with Random_SMOTE, both based on k-nearest neighbors (KNN).
  • Noise Detection-based Method (NDBM): Integrates SMOTE Tomek links (SMOTE_Tomeklinks) and Modified Synthetic Minority Oversampling Technique (MSMOTE).
  • Cluster-based Method (CBM): Merges Cluster-Based Synthetic Oversampling (CBSO) with Proximity Weighted Synthetic Oversampling Technique (ProWSyn).
  • Performance was assessed using accelerometer data from three benchmark HAR datasets.

Main Results:

  • All three hybrid methods (DBM, NDBM, CBM) effectively reduced the impact of class imbalance.
  • These methods demonstrated significant improvements in F1 scores, ranging from 9-20 percentage points higher than their constituent sampling methods.
  • The Cluster-based Method (CBM) showed superior performance statistically (Friedman test), while the Distance-based Method (DBM) offered lower computational costs.

Conclusions:

  • The proposed hybrid sampling strategies offer effective solutions for mitigating class imbalance in HAR.
  • These methods enhance the performance of HAR classifiers, leading to more robust activity recognition.
  • The choice between CBM and DBM may depend on the specific application's requirements for performance versus computational efficiency.