Earticle

초록

영어

Recently, cloud computing technology has been rapidly growing faster, offering cloud-based human activity recognition applications with lower latency. In this paper, we design and implement a new distributed driver activity recognition system (DHAR). The proposed distributed system absorbs a more significant number of input sensor data from humans with a lightweight model that provides high accuracy for driver activity recognition. In addition, our model has employed the entire convolution network – Long Short-term Memory (FCN-LSTM) to predict human activities of a total of 6 classes such as walking, walking upstairs, walking-downstairs, sitting, standing, and laying. We evaluate the proposed system using a well-known UCI-HAR opensource dataset containing a collection of smart-phones data for 30-subjects while performing various activities using a smartphone. We used various Amazon cloud computing services for the deployment of the proposed architecture. The experimental results show that the proposed architecture improves end-to-end latency by 2.7 times compared to the traditional architecture.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. UCI-HAR Dataset
B. Shun et al.'s Human Activity Recognition
III. PROPOSED METHODOLOGY
A. Global Model Deployment
IV. PRIMARILY EXPERIMENTAL SETUP AND RESULTS
V. CONCLUSION
ACKNOWLEDGMENT

키워드

저자

• Mehdi Pirahandeh [ Department of Electronic Engineering, Inha University ]
• Deok-Hwan Kim [ Department of Electronic Engineering, Inha University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004