Top-K Human Activity Recognition Dataset

Top-K Human Activity Recognition Dataset

top-k personalized dataset

gravimeter filtering technique

tree oriented

The availability of Smartphones has increased the possibility of self-monitoring to increase physical activity and behavior change to prevent obesity. However self-monitoring on a Smartphtone comes with some challenges such as unavailability of lightweight classification algorithm, personalized dataset to completely capture bodily postures, subject sensitivity, limited storage and computational power. However, most classification algorithms such as Support Vector Machines, C4.5, Naïve Bayes and K Neighbor relies on larger dataset to accurately predict human activities. In this paper, we present top-k of compressed small personalized dataset to reduce computational cost with increased accuracy. We collected top-k personalized dataset from 13 recruited subjects. After benchmarking our collected dataset we found that the dataset is suitable for tree-oriented algorithm, especially the Random Forest, C4.5 and Boosted tree with accuracy and precision of 100% except for KNN, Support Vector and Naïve Bayes. Further, our top-k personalized dataset improves pruning and overfitting of tree-oriented algorithms. Moreover, the linear consistence of static human activities reveals the potential of our top-k dataset to be replicated to multiple-subject to close subject sensitivity challenge.

Gadebe, Moses Lesiba

Kogeda, Okuthe Paul

International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 18 (2020); pp. 68-86

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2020-11-10

Copyright (c) 2020 Moses Lesiba Gadebe, Okuthe Paul Kogeda

application/pdf

eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article