Identification of Soft Fall based on Falling State Occurrences

Main Article Content

Thein Gi Kyaw
Anant Choksuriwong
Nikom Suvonvorn


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.

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How to Cite
Thein Gi Kyaw, Anant Choksuriwong, and Nikom Suvonvorn, “Identification of Soft Fall based on Falling State Occurrences”, ECTI-CIT Transactions, vol. 15, no. 3, pp. 324–332, Nov. 2021.
Research Article
Author Biographies

Thein Gi Kyaw, Prince of Songkla University, HatYai, Songkhla, Thailand

Ph.D candidate in Prince of Songkla University (PSU), Hat Yai, Songkhla, Thailand.

Anant Choksuriwong, Prince of Songkla University, HatYai, Songkhla, Thailand

Lecturer at Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University, Hatyai, Thailand.

Nikom Suvonvorn, Prince of Songkla University, HatYai, Songkhla, Thailand

Lecturer at Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University, Hatyai, Thailand.


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