Fall Detection Based on 2-Stacked Bi-LSTM and Human-Skeleton Keypoints of RGBD Camera


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 11, pp. 491-500, Nov. 2021
https://doi.org/10.3745/KTSDE.2021.10.11.491,   PDF Download:
Keywords: Fall Detection, deep-learning, Skeleton Keypoints, Stacked Bi-LSTM
Abstract

In this study, we propose a method for detecting fall behavior using MS Kinect v2 RGBD Camera-based Human-Skeleton Keypoints and a 2-Stacked Bi-LSTM model. In previous studies, skeletal information was extracted from RGB images using a deep learning model such as OpenPose, and then recognition was performed using a recurrent neural network model such as LSTM and GRU. The proposed method receives skeletal information directly from the camera, extracts 2 time-series features of acceleration and distance, and then recognizes the fall behavior using the 2-Stacked Bi-LSTM model. The central joint was obtained for the major skeletons such as the shoulder, spine, and pelvis, and the movement acceleration and distance from the floor were proposed as features of the central joint. The extracted features were compared with models such as Stacked LSTM and Bi-LSTM, and improved detection performance compared to existing studies such as GRU and LSTM was demonstrated through experiments.


Statistics
Show / Hide Statistics

Statistics (Cumulative Counts from September 1st, 2017)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article
[IEEE Style]
S. B. Geun, K. U. Ho, L. S. Woo, Y. J. Young, K. Wongyum, "Fall Detection Based on 2-Stacked Bi-LSTM and Human-Skeleton Keypoints of RGBD Camera," KIPS Transactions on Software and Data Engineering, vol. 10, no. 11, pp. 491-500, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.11.491.

[ACM Style]
Shin Byung Geun, Kim Uung Ho, Lee Sang Woo, Yang Jae Young, and Kim Wongyum. 2021. Fall Detection Based on 2-Stacked Bi-LSTM and Human-Skeleton Keypoints of RGBD Camera. KIPS Transactions on Software and Data Engineering, 10, 11, (2021), 491-500. DOI: https://doi.org/10.3745/KTSDE.2021.10.11.491.