Fall Detection and Motion Analysis Using Visual Approaches
Falls are considered one of the most ubiquitous problems leading to morbidity and disability in the elderly. This paper presents a vision-based approach toward the care and rehabilitation of the elderly by examining the important body symmetry features in falls and activities of daily living (AD...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2022-11-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | https://ijtech.eng.ui.ac.id/article/view/5840 |
| Summary: | Falls are considered
one of the most ubiquitous problems leading to morbidity and disability in the
elderly. This paper presents a vision-based approach toward the care and
rehabilitation of the elderly by examining the important body symmetry features
in falls and activities of daily living (ADL). The proposed method carries out
human skeleton estimation and detection on image datasets for feature extraction
to predict falls and to analyze gait motion. The extracted skeletal information
is further evaluated and analyzed for the fall risk factors in order to predict
a fall event. Four critical risk factors are found to be highly correlated to
falls, including 2D motion (gait speed), gait pose, 3D trunk angle or body
orientation, and body shape (width-to-height ratio). Different variants of deep
architectures, including 1D Convolutional Neural Network (CNN), Long Short-Term
Memory (LSTM) Network, Gated Recurrent Units (GRU) model, and attention-based
mechanism, are investigated with several fusion techniques to predict the fall
based on human body balance study. A given test gait sequence will be
classified into one of the three phases: non-fall, pre-impact fall, and fall.
With the attention-based GRU architecture, an accuracy of 96.2% can be achieved
for predicting a falling event. |
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| ISSN: | 2086-9614 2087-2100 |