Academic Journal
Fall Detection and Motion Analysis Using Visual Approaches
| العنوان: | Fall Detection and Motion Analysis Using Visual Approaches |
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| المؤلفون: | Xin Lin Lau, Tee Connie, Michael Kah Ong Goh, Siong Hoe Lau |
| المصدر: | International Journal of Technology, Vol 13, Iss 6, Pp 1173-1182 (2022) |
| بيانات النشر: | Universitas Indonesia, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Technology LCC:Technology (General) |
| مصطلحات موضوعية: | attention mechanism, deep learning, fall detection, gated recurrent unit (gru), vision approach, Technology, Technology (General), T1-995 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2086-9614 2087-2100 |
| Relation: | https://ijtech.eng.ui.ac.id/article/view/5840; https://doaj.org/toc/2086-9614; https://doaj.org/toc/2087-2100 |
| DOI: | 10.14716/ijtech.v13i6.5840 |
| URL الوصول: | https://doaj.org/article/2a91054a61e14399a50e2f921a79bbf1 |
| رقم الانضمام: | edsdoj.2a91054a61e14399a50e2f921a79bbf1 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 20869614 20872100 |
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| DOI: | 10.14716/ijtech.v13i6.5840 |