Research Article Open Access

Distributed and Parallel Decision Forest for Human Activities Prediction: Experimental Analysis on HAR-Smartphones Dataset

Budi Padmaja1, Venkata Rama Prasad Vaddella2 and Kota Venkata Naga Sunitha3
  • 1 Institute of Aeronautical Engineering, India
  • 2 Sree Vidyanikethan Engg College, India
  • 3 BVRITH College of Engg for Women, India

Abstract

Sensor-based human motion detection requires the subtle amount of knowledge about various human activities from fitted sensor observations and readings. The prevalent pattern recognition methodologies have made immense progress over recent years. Nonetheless, these kind of methods usually rely on the particular heuristic variable extraction, which could inhibit generalization realization. This paper presents a distributed and parallel decision forest approach for modeling the Human Activity Recognition Using Smartphones Data. We made an attempt to achieve an optimal generalization performance with possible reduction in overfitting. Later, we compared the performance of proposed procedure with some existing approaches. It is observed that our adopted procedure outperforms with comparatively better statistical performance measures. It also gained 4.7x speed up in computation.

Journal of Computer Science
Volume 15 No. 5, 2019, 673-680

DOI: https://doi.org/10.3844/jcssp.2019.673.680

Submitted On: 13 February 2019 Published On: 11 May 2019

How to Cite: Padmaja, B., Vaddella, V. R. P. & Sunitha, K. V. N. (2019). Distributed and Parallel Decision Forest for Human Activities Prediction: Experimental Analysis on HAR-Smartphones Dataset. Journal of Computer Science, 15(5), 673-680. https://doi.org/10.3844/jcssp.2019.673.680

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Keywords

  • Smart Environments
  • Parallel Processing
  • Deep Learning
  • Human Action
  • Random Oblique KNN
  • Dual Problem
  • Decision Forest