Long short-term memory networks based fall detection using unified pose estimation

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Authors: Adhikari, K., Bouchachia, H. and Nait-Charif, H.

Journal: Proceedings of SPIE - The International Society for Optical Engineering

Volume: 11433

eISSN: 1996-756X

ISBN: 9781510636439

ISSN: 0277-786X

DOI: 10.1117/12.2556540

© 2020 SPIE. Falls are one of the major causes of injury and death among elderly globally. The increase in the ageing population has also increased the possibility of re-occurrence of falls. This has further added social and economic burden due to the higher demand for the caretaker and costly treatments. Detecting fall accurately, therefore, can save lives as well as reduce the higher cost by reducing the false alarm. However, recognising falls are challenging as they involve pose translation at a greater speed. Certain activities such as abruptly sitting down, stumble and lying on a sofa demonstrate strong similarities in action with a fall event. Hence accuracy in fall detection is highly desirable. This paper presents a Long Short-Term Memory (LSTM) based fall detection using location features from the group of available joints in the human body. The result from the confusion matrix suggests that our proposed model can detect fall class with a precision of 1.0 which is highly desirable.

This data was imported from Web of Science (Lite):

Authors: Adhikari, K., Bouchachia, H. and Nait-Charif, H.

Journal: TWELFTH INTERNATIONAL CONFERENCE ON MACHINE VISION (ICMV 2019)

Volume: 11433

eISSN: 1996-756X

ISSN: 0277-786X

DOI: 10.1117/12.2556540

The data on this page was last updated at 05:19 on October 21, 2020.