Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue
Most existing physiological testing systems broadly classify monitored physiological data into three categories: normal, abnormal, and highly abnormal, but do not consider differences in the importance of data within the same category, which may result in the loss of data of higher importance. In ad...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2022-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2022069?viewType=HTML |
| _version_ | 1819284690542002176 |
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| author | Kezhou Chen Xu Lu Rongjun Chen Jun Liu |
| author_facet | Kezhou Chen Xu Lu Rongjun Chen Jun Liu |
| author_sort | Kezhou Chen |
| collection | DOAJ |
| description | Most existing physiological testing systems broadly classify monitored physiological data into three categories: normal, abnormal, and highly abnormal, but do not consider differences in the importance of data within the same category, which may result in the loss of data of higher importance. In addition, the purpose of physiological monitoring is to detect health abnormalities in patients earlier and faster, thus enabling risk avoidance and real-time rescue. Therefore, we designed a system called the adaptive physiological monitoring and rescue system (APMRS) that innovatively incorporates emergency rescue functions into traditional physiological monitoring systems using the rescue of modified-MAC (RM-MAC) protocol. The relay selection (RS) algorithm of APMRS can select the appropriate relay to forward based on the importance of the physiological data, thus ensuring priority transmission of more important monitoring data. In addition, we apply deep learning target trajectory prediction technology to the indoor rescue module (IRM) of APMRS to provide high-performance scheduling of location tracking nodes in advance by trajectory prediction. It reduces network energy consumption and ensures perceptual tracking accuracy. When APMRS monitors abnormal physiological data that may endanger a patient's life, IRM can implement effective and fast location rescue to avoid risks. |
| first_indexed | 2024-12-24T01:51:23Z |
| format | Article |
| id | doaj.art-b50fac4c6aaf49d3a9a9c5d5e3593bcc |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-24T01:51:23Z |
| publishDate | 2022-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-b50fac4c6aaf49d3a9a9c5d5e3593bcc2022-12-21T17:21:44ZengAIMS PressMathematical Biosciences and Engineering1551-00182022-01-011921496151410.3934/mbe.2022069Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescueKezhou Chen0Xu Lu1Rongjun Chen2Jun Liu31. College of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China1. College of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China 2. Pazhou Lab, Guangzhou 510330, China1. College of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China1. College of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, ChinaMost existing physiological testing systems broadly classify monitored physiological data into three categories: normal, abnormal, and highly abnormal, but do not consider differences in the importance of data within the same category, which may result in the loss of data of higher importance. In addition, the purpose of physiological monitoring is to detect health abnormalities in patients earlier and faster, thus enabling risk avoidance and real-time rescue. Therefore, we designed a system called the adaptive physiological monitoring and rescue system (APMRS) that innovatively incorporates emergency rescue functions into traditional physiological monitoring systems using the rescue of modified-MAC (RM-MAC) protocol. The relay selection (RS) algorithm of APMRS can select the appropriate relay to forward based on the importance of the physiological data, thus ensuring priority transmission of more important monitoring data. In addition, we apply deep learning target trajectory prediction technology to the indoor rescue module (IRM) of APMRS to provide high-performance scheduling of location tracking nodes in advance by trajectory prediction. It reduces network energy consumption and ensures perceptual tracking accuracy. When APMRS monitors abnormal physiological data that may endanger a patient's life, IRM can implement effective and fast location rescue to avoid risks.https://www.aimspress.com/article/doi/10.3934/mbe.2022069?viewType=HTMLwireless wearable biosensorsreal-time physiological monitoringdeep learning node schedulingrisk avoidance and rescue |
| spellingShingle | Kezhou Chen Xu Lu Rongjun Chen Jun Liu Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue Mathematical Biosciences and Engineering wireless wearable biosensors real-time physiological monitoring deep learning node scheduling risk avoidance and rescue |
| title | Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue |
| title_full | Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue |
| title_fullStr | Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue |
| title_full_unstemmed | Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue |
| title_short | Wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue |
| title_sort | wireless wearable biosensor smart physiological monitoring system for risk avoidance and rescue |
| topic | wireless wearable biosensors real-time physiological monitoring deep learning node scheduling risk avoidance and rescue |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2022069?viewType=HTML |
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