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Human Activity Recognition Using WISDM: Exploring Class Balancing and ML Techniques

EasyChair Preprint 13545

6 pagesDate: June 4, 2024

Abstract

Wearable sensor for human activity recognition (HAR) is vital in activity sensing research. We addressed dataset imbalance in WISDM using three class balancing techniques: SMOTE, LoRAS, and ProWRA, applied to five machine learning models. LoRAS consistently achieved the highest accuracy, recall, precision, and F1-scores, outperforming SMOTE and ProWRA. Our results demonstrate LoRAS as the most effective technique for enhancing model performance in human activity recognition.

Keyphrases: LoRAS, ProWRA, SMOTE

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:13545,
  author    = {Maria Hanif and Rizwan Ahmad and Shams Qazi and Waqas Ahmed and Muhammad Mahtab Alam},
  title     = {Human Activity Recognition Using WISDM: Exploring Class Balancing and ML Techniques},
  howpublished = {EasyChair Preprint 13545},
  year      = {EasyChair, 2024}}
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