Activity Detection using 2D LIDAR for Healthcare and Monitoring

Mondher Bouazizi, Chen Ye, Tomoaki Ohtsuki

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

Monitoring elderly people living alone is of the utmost importance given the amount of risk they are exposed to. Being aware of the activities of the elderly person in real time could help prevent/detect dangerous event that might occur such as falling. In this paper, we propose a method for activity detection using a 2D LIght Detection and Ranging (LIDAR) and deep learning. Unlike conventional work, where an activity refers to moving from one position to another, we use the term 'activity' to refer to a set of movements including walking, standing, falling and sitting. Not only does our approach detect these activities, but it also identifies a given person from his gait, and identifies unsteady gait (i.e., when he is about to fall or feeling dizzy). Throughout our experiments, we show that the proposed approach could reach an accuracy equal to 92.3% and 91.3% in activity and unsteady gait detection, respectively. It is also capable of identifying up to 3 people's gait with an accuracy equal to 92.4% using 10 seconds of walking data.

本文言語English
ホスト出版物のタイトル2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728181042
DOI
出版ステータスPublished - 2021
イベント2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain
継続期間: 2021 12月 72021 12月 11

出版物シリーズ

名前2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings

Conference

Conference2021 IEEE Global Communications Conference, GLOBECOM 2021
国/地域Spain
CityMadrid
Period21/12/721/12/11

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ サイエンスの応用
  • ハードウェアとアーキテクチャ
  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理
  • 健康情報学

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