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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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Timely daily activity recognition from headmost sensor events.

Yaqing Liu1, Xiangxin Wang2, Zhengguo Zhai2

  • 1School of Information Science & Technology, Dalian Maritime University, Dalian 116026, China; College of Computer Science and Technology, Jilin University, Changchun 130012, China; Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Zigong 643000, China.

ISA Transactions
|May 13, 2019
PubMed
Summary

This study introduces a new method for timely daily activity recognition in smart homes, enabling recognition from the start of an activity using initial sensor events. This approach significantly improves real-time performance and reduces waiting times.

Keywords:
Daily activity recognitionReal-timeSensor eventsSmart homes

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Smart homes aim for automated safety and comfort.
  • Current real-time activity recognition methods are delayed, only recognizing activities after completion.
  • This delay limits the potential for proactive interventions and personalized user experiences in smart environments.

Purpose of the Study:

  • To develop a novel approach for timely daily activity recognition in smart homes.
  • To enable activity recognition from the beginning of an event, rather than upon its completion.
  • To improve the efficiency and responsiveness of smart home systems.

Main Methods:

  • Proposed an approach for timely daily activity recognition using incomplete sensor event streams.
  • Generated activity features from the initial sensor events of an activity.
  • Evaluated the method using a public dataset for smart home activities.

Main Results:

  • The presented method demonstrated effectiveness in timely daily activity recognition.
  • Achieved significant improvements in precision and recall.
  • Showcased substantial reductions in average saved time and saved time proportion.

Conclusions:

  • The developed approach enables real-time activity recognition to commence as soon as an activity begins.
  • This significantly enhances the timeliness and efficiency of smart home monitoring.
  • The findings support the development of more responsive and proactive smart home applications.