Recent advancements in machine learning enabled portable and wearable biosensors
Recent advances in noninvasive portable and wearable biosensors have attracted significant attention due to their capability to offer continual physiological information for continuous healthcare monitoring through the collection of biological signals. To make the collected biological data understan...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-12-01
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| Series: | Talanta Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666831923000875 |
| _version_ | 1827591088874979328 |
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| author | Sachin Kadian Pratima Kumari Shubhangi Shukla Roger Narayan |
| author_facet | Sachin Kadian Pratima Kumari Shubhangi Shukla Roger Narayan |
| author_sort | Sachin Kadian |
| collection | DOAJ |
| description | Recent advances in noninvasive portable and wearable biosensors have attracted significant attention due to their capability to offer continual physiological information for continuous healthcare monitoring through the collection of biological signals. To make the collected biological data understandable and improve the efficacy of these biosensors, scientists have integrated machine learning (ML) with biosensors to analyze large sensing data through various ML algorithms. In this article, we have highlighted the recent developments in ML-enabled noninvasive biosensors. Initially, we introduced and discussed the basic features of ML algorithms used in data processing to build an intelligent biosensor system and the capability to make clinical decisions. Next, the principles of portable and wearable biosensors, the application of different ML models in diverse biosensors for healthcare applications, and their impact on the performance of biosensors are discussed. The last section highlights the challenges (such as data privacy, consistency, stability, accuracy, scalable production, and adaptive learning capacity), future prospects, and necessary steps required to address these issues, spotlighting their revolutionizing impact on the healthcare industry for the development of next-generation ML-enabled efficient biosensors. |
| first_indexed | 2024-03-09T01:25:53Z |
| format | Article |
| id | doaj.art-ea847b81bda14410898530c8b1cd84c1 |
| institution | Directory Open Access Journal |
| issn | 2666-8319 |
| language | English |
| last_indexed | 2024-03-09T01:25:53Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Talanta Open |
| spelling | doaj.art-ea847b81bda14410898530c8b1cd84c12023-12-10T06:18:41ZengElsevierTalanta Open2666-83192023-12-018100267Recent advancements in machine learning enabled portable and wearable biosensorsSachin Kadian0Pratima Kumari1Shubhangi Shukla2Roger Narayan3Joint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, NC, USAUniveristy of California, San Francisco, CA, USAJoint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, NC, USAJoint Department of Biomedical Engineering, University of North Carolina and North Carolina State University, Raleigh, NC, USA; Corresponding author.Recent advances in noninvasive portable and wearable biosensors have attracted significant attention due to their capability to offer continual physiological information for continuous healthcare monitoring through the collection of biological signals. To make the collected biological data understandable and improve the efficacy of these biosensors, scientists have integrated machine learning (ML) with biosensors to analyze large sensing data through various ML algorithms. In this article, we have highlighted the recent developments in ML-enabled noninvasive biosensors. Initially, we introduced and discussed the basic features of ML algorithms used in data processing to build an intelligent biosensor system and the capability to make clinical decisions. Next, the principles of portable and wearable biosensors, the application of different ML models in diverse biosensors for healthcare applications, and their impact on the performance of biosensors are discussed. The last section highlights the challenges (such as data privacy, consistency, stability, accuracy, scalable production, and adaptive learning capacity), future prospects, and necessary steps required to address these issues, spotlighting their revolutionizing impact on the healthcare industry for the development of next-generation ML-enabled efficient biosensors.http://www.sciencedirect.com/science/article/pii/S2666831923000875Machine learningClinical decision makingPortable and wearable biosensors |
| spellingShingle | Sachin Kadian Pratima Kumari Shubhangi Shukla Roger Narayan Recent advancements in machine learning enabled portable and wearable biosensors Talanta Open Machine learning Clinical decision making Portable and wearable biosensors |
| title | Recent advancements in machine learning enabled portable and wearable biosensors |
| title_full | Recent advancements in machine learning enabled portable and wearable biosensors |
| title_fullStr | Recent advancements in machine learning enabled portable and wearable biosensors |
| title_full_unstemmed | Recent advancements in machine learning enabled portable and wearable biosensors |
| title_short | Recent advancements in machine learning enabled portable and wearable biosensors |
| title_sort | recent advancements in machine learning enabled portable and wearable biosensors |
| topic | Machine learning Clinical decision making Portable and wearable biosensors |
| url | http://www.sciencedirect.com/science/article/pii/S2666831923000875 |
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