Open AccessIdentification of Soft Fall based on Falling State Occurrences
Author(s) -
Thein Gi Kyaw,
Anant Choksuriwong,
Nikom Suvonvorn
Publication year - 2021
Publication title -
ecti transactions on computer and information technology
Language(s) - English
Resource type - Journals
ISSN - 2286-9131
DOI - 10.37936/ecti-cit.2021153.240720
Subject(s) - falling (accident) , accidental fall , work (physics) , state (computer science) , computer science , forensic engineering , engineering , psychology , medicine , algorithm , mechanical engineering , surgery , psychiatry
Fall detection techniques for helping the elderly were developed based on identifying falling states using simulated falls. However, some real-life falling states were left undetected, which led to this work on analysing falling states. The aim was to find the differences between active daily living and soft falls where falling states were undetected. This is the first consideration to be based on the threshold-based algorithms using the acceleration data stored in an activity database. This study addresses soft falls in addition to the general falls based on two falling states. Despite the number of false alarms being higher rising from 18.5% to 56.5%, the sensitivity was increased from 52% to 92.5% for general falls, and from 56% to 86% for soft falls. Our experimental results show the importance of state occurrence for soft fall detection, and will be used to build a learning model for soft fall detection.