Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning [electronic resource]

• Ngôn ngữ: eng
• Ký hiệu phân loại: 620.8 Human factors and safety engineering
• Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. National Nuclear Security Administration ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2019
• Mô tả vật lý: Size: p. 144-150 : , digital, PDF file.
• Bộ sưu tập: Metadata
• ID: 261871
• Chủ đề: 1. 60 applied life sciences; 2. Accelerometers; 3. Biological system modeling; 4. Biomedical measurement; 5. Data models multi-layer neural networks; 6. Et al; 7. Informatics; 8. Kernel; 9. Machine learning; 10. Sensors; 11. Time series analysis

Tóm tắt

Prior research in falls risk classification using inertial sensors has relied on the use of engineered features, which has concluded in a feature space containing hundreds of features that are likely redundant and possibly irrelevant. In this paper, we propose using fully convolutional neural networks (FCNNs) to classify older adults at low or high risk of falling using inertial sensor data collected from a smartphone. Due to the limited nature of older adult inertial gait data sets, we first pre-train the FCNN models using a publicly available data set for pedestrian activity recognition. Then via transfer learning, we train the network for falls risk classification. We reveal that via transfer learning, our falls risk classifier obtains an area under the receiver operating characteristic curve of 93.3%, which is 10.6% higher than the equivalent model trained without the use of transfer learning. Moreover, we show that our method outperforms other standard machine learning classifiers trained on features developed in prior research.