Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0
Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by conn...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/12/5754 |
| _version_ | 1797592675721936896 |
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| author | Lawrence He Mark Eastburn James Smirk Hong Zhao |
| author_facet | Lawrence He Mark Eastburn James Smirk Hong Zhao |
| author_sort | Lawrence He |
| collection | DOAJ |
| description | Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model’s performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance. |
| first_indexed | 2024-03-11T01:56:30Z |
| format | Article |
| id | doaj.art-e2ec9570c5cf468990b9c5770f007676 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T01:56:30Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e2ec9570c5cf468990b9c5770f0076762023-11-18T12:35:37ZengMDPI AGSensors1424-82202023-06-012312575410.3390/s23125754Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0Lawrence He0Mark Eastburn1James Smirk2Hong Zhao3Princeton High School, Princeton, NJ 08540, USAPrinceton High School, Princeton, NJ 08540, USAPrinceton High School, Princeton, NJ 08540, USAGildart Haase School of Computer Sciences and Engineering, Fairleigh Dickinson University, Teaneck, NJ 07666, USADriven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model’s performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance.https://www.mdpi.com/1424-8220/23/12/5754chemical sensorsbiosensorsIndustry 4.0Healthcare 4.0wearable devicesbody chemical sensors and biosensor networks (BSNs) |
| spellingShingle | Lawrence He Mark Eastburn James Smirk Hong Zhao Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0 Sensors chemical sensors biosensors Industry 4.0 Healthcare 4.0 wearable devices body chemical sensors and biosensor networks (BSNs) |
| title | Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0 |
| title_full | Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0 |
| title_fullStr | Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0 |
| title_full_unstemmed | Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0 |
| title_short | Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0 |
| title_sort | smart chemical sensor and biosensor networks for healthcare 4 0 |
| topic | chemical sensors biosensors Industry 4.0 Healthcare 4.0 wearable devices body chemical sensors and biosensor networks (BSNs) |
| url | https://www.mdpi.com/1424-8220/23/12/5754 |
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