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Airline Point-of-Care System on Seat Belt for Hybrid Physiological Signal Monitoring Ji, Xiaoqiang; Rao, Zhi; Zhang, Wei; Liu, Chang; Wang, Zimo; Zhang, Shuo; Zhang, Butian; Hu, Menglei; Servati, Peyman; Xiao, Xiao
Abstract
With a focus on disease prevention and health promotion, a reactive and disease-centric healthcare system is revolutionized to a point-of-care model by the application of wearable devices. The convenience and low cost made it possible for long-term monitoring of health problems in long-distance traveling such as flights. While most of the existing health monitoring systems on aircrafts are limited for pilots, point-of-care systems provide choices for passengers to enjoy healthcare at the same level. Here in this paper, an airline point-of-care system containing hybrid electrocardiogram (ECG), breathing, and motion signals detection is proposed. At the same time, we propose the diagnosis of sleep apnea-hypopnea syndrome (SAHS) on flights as an application of this system to satisfy the inevitable demands for sleeping on long-haul flights. The hardware design includes ECG electrodes, flexible piezoelectric belts, and a control box, which enables the system to detect the original data of ECG, breathing, and motion signals. By processing these data with interval extraction-based feature selection method, the signals would be characterized and then provided for the long short-term memory recurrent neural network (LSTM-RNN) to classify the SAHS. Compared with other machine learning methods, our model shows high accuracy up to 84–85% with the lowest overfit problem, which proves its potential application in other related fields.
Item Metadata
| Title |
Airline Point-of-Care System on Seat Belt for Hybrid Physiological Signal Monitoring
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| Creator | |
| Publisher |
Multidisciplinary Digital Publishing Institute
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| Date Issued |
2022-11-01
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| Description |
With a focus on disease prevention and health promotion, a reactive and disease-centric healthcare system is revolutionized to a point-of-care model by the application of wearable devices. The convenience and low cost made it possible for long-term monitoring of health problems in long-distance traveling such as flights. While most of the existing health monitoring systems on aircrafts are limited for pilots, point-of-care systems provide choices for passengers to enjoy healthcare at the same level. Here in this paper, an airline point-of-care system containing hybrid electrocardiogram (ECG), breathing, and motion signals detection is proposed. At the same time, we propose the diagnosis of sleep apnea-hypopnea syndrome (SAHS) on flights as an application of this system to satisfy the inevitable demands for sleeping on long-haul flights. The hardware design includes ECG electrodes, flexible piezoelectric belts, and a control box, which enables the system to detect the original data of ECG, breathing, and motion signals. By processing these data with interval extraction-based feature selection method, the signals would be characterized and then provided for the long short-term memory recurrent neural network (LSTM-RNN) to classify the SAHS. Compared with other machine learning methods, our model shows high accuracy up to 84–85% with the lowest overfit problem, which proves its potential application in other related fields.
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| Subject | |
| Genre | |
| Type | |
| Language |
eng
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| Date Available |
2025-01-24
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
CC BY 4.0
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| DOI |
10.14288/1.0447842
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| URI | |
| Affiliation | |
| Citation |
Micromachines 13 (11): 1880 (2022)
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| Publisher DOI |
10.3390/mi13111880
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| Peer Review Status |
Reviewed
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| Scholarly Level |
Faculty; Researcher
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
CC BY 4.0