Activity prediction using LSTM in smart home

Yegang Du, Yuto Lim, Yasuo Tan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)


In the near future, smart home systems will play more and more important role to provide comfortable and safe life to human. Today, we already have some realistic way to monitor the daily life of human and recognize their activities by cameras or wireless sensing technology. However, the current research still faces the challenge to the prediction of human activities. In this paper, we analyse the similarity between human activities of daily living and deep neural networks. Inspired by this, the paper proposes a method to predict human activity by deep learning model and evaluates the performance of the approach with real world data. Compared with the traditional algorithm, our approach reaches higher prediction accuracy. In the future, we will try to improve the prediction accuracy and add more kinds of activities.

Original languageEnglish
Title of host publication2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages2
ISBN (Electronic)9781728135755
Publication statusPublished - 2019 Oct
Externally publishedYes
Event8th IEEE Global Conference on Consumer Electronics, GCCE 2019 - Osaka, Japan
Duration: 2019 Oct 152019 Oct 18

Publication series

Name2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019


Conference8th IEEE Global Conference on Consumer Electronics, GCCE 2019


  • Activity Prediction
  • Human Activity Recognition
  • Smart Home
  • Wireless Sensing

ASJC Scopus subject areas

  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering


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