Lightweight real-time fall detection using bidirectional recurrent neural network

Sangyeon Kim, Gawon Lee, Jihie Kim

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

As the world's population is aging, the home care systems for elderly people have been getting high attention. According to the National Council on Aging, every 11 seconds, an older adult is treated in the emergency room for a fall, and every 19 minutes, an older adult dies from a fall. The number of single households is also increasing with an aging society. In a single household, there is no one to help the elderly when they fall. This could lead to serious problems such as disability or death. In this paper, we propose a lightweight real-time system for fall detection, distinguished from other activities of daily living (ADL). The entire system is divided into a preprocessing and prediction part. With the system, falls and ADLs can be distinguished with more than 92% accuracy which is higher than the existing approach even without any additional resampling method.

Original languageEnglish
Title of host publication2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728197326
DOIs
StatePublished - 5 Dec 2020
EventJoint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020 - Virtual, Tokyo, Japan
Duration: 5 Dec 20208 Dec 2020

Publication series

Name2020 Joint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020

Conference

ConferenceJoint 11th International Conference on Soft Computing and Intelligent Systems and 21st International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2020
Country/TerritoryJapan
CityVirtual, Tokyo
Period5/12/208/12/20

Keywords

  • Bidirectional Recurrent Neural Network
  • Butterworth Loss-pass Filter
  • Fall Detection
  • Human Activity Recognition
  • MobiAct dataset
  • Real-time Fall Detection

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