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Volume 17, Issue 3 (7-2025)
itrc 2025, 17(3): 46-57 |
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Oveisi ُ, Goodarzi M, Moin M. Analysis and Evaluation of the Performance of Smart Wearable Systems Based on Internet of Things in the Healthcare Sector. itrc 2025; 17 (3) :46-57
URL: http://ijict.itrc.ac.ir/article-1-639-en.html
URL: http://ijict.itrc.ac.ir/article-1-639-en.html
1- Center for Innovation and Development of AI (CIDAI), ICT Research Institute
2- Center for Innovation and Development of AI (CIDAI) ICT Research Institute ,moin@itrc.ac.ir
2- Center for Innovation and Development of AI (CIDAI) ICT Research Institute ,
Abstract: (428 Views)
With the expansion of artificial intelligence-based products, including IOT systems, as well as rap id adoption of algorithms in business and global society, there is a growing concern to preserve public interest, and the quality of these systems for practical use is of high importance. Smart AI wearable systems are among these products; these devices have various sensors such as accelerometer, gyroscope and other sensors that identify and categorize a person's physical activities, receive environmental and physiological information and process them using artificial intelligence, presenting useful information including physiological function, health and human behavior. As a result, this field can be valuable for scientific research, medicine, sports, industries and even daily life. For widespread use of smart AI wearable products, it is necessary to guarantee the quality of these products, and for this purpose, regulatory frameworks and evaluation criteria have been established in these systems. In this article, we used WSDM data to classify the activities performed by a user using CNN2 network and subsequently evaluated and tested Smart AI wearable systems in order to standardize these products. These tests include bias tests, black tests, robustness and generalization. The results of these tests have been discussed and evaluated at the end of article, which indicate successful design and implementation of this product.
With the expansion of artificial intelligence-based products, including IOT systems, as well as rap id adoption of algorithms in business and global society, there is a growing concern to preserve public interest, and the quality of these systems for practical use is of high importance. Smart AI wearable systems are among these products; these devices have various sensors such as accelerometer, gyroscope and other sensors that identify and categorize a person's physical activities, receive environmental and physiological information and process them using artificial intelligence, presenting useful information including physiological function, health and human behavior. As a result, this field can be valuable for scientific research, medicine, sports, industries and even daily life. For widespread use of smart AI wearable products, it is necessary to guarantee the quality of these products, and for this purpose, regulatory frameworks and evaluation criteria have been established in these systems. In this article, we used WSDM data to classify the activities performed by a user using CNN2 network and subsequently evaluated and tested Smart AI wearable systems in order to standardize these products. These tests include bias tests, black tests, robustness and generalization. The results of these tests have been discussed and evaluated at the end of article, which indicate successful design and implementation of this product.
With the expansion of artificial intelligence-based products, including IOT systems, as well as rap id adoption of algorithms in business and global society, there is a growing concern to preserve public interest, and the quality of these systems for practical use is of high importance. Smart AI wearable systems are among these products; these devices have various sensors such as accelerometer, gyroscope and other sensors that identify and categorize a person's physical activities, receive environmental and physiological information and process them using artificial intelligence, presenting useful information including physiological function, health and human behavior. As a result, this field can be valuable for scientific research, medicine, sports, industries and even daily life. For widespread use of smart AI wearable products, it is necessary to guarantee the quality of these products, and for this purpose, regulatory frameworks and evaluation criteria have been established in these systems. In this article, we used WSDM data to classify the activities performed by a user using CNN2 network and subsequently evaluated and tested Smart AI wearable systems in order to standardize these products. These tests include bias tests, black tests, robustness and generalization. The results of these tests have been discussed and evaluated at the end of article, which indicate successful design and implementation of this product.
Type of Study: Applicable |
Subject:
Information Technology
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