Manh-Tuyen Vi¹, Duc-Nghia Tran², Vu Thi Thuong^3,4, Nguyen Ngoc Linh^5,*, Duc-Tan Tran^1,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1729-1756, 2024, DOI:10.32604/cmc.2024.055511 - 15 October 2024

Abstract Analyzing physical activities through wearable devices is a promising research area for improving health assessment. This research focuses on the development of an affordable and real-time Human Activity Recognition (HAR) system designed to operate on low-performance microcontrollers. The system utilizes data from a body-worn accelerometer to recognize and classify human activities, providing a cost-effective, easy-to-use, and highly accurate solution. A key challenge addressed in this study is the execution of efficient motion recognition within a resource-constrained environment. The system employs a Random Forest (RF) classifier, which outperforms Gradient Boosting Decision Trees (GBDT), Support Vector Machines… More >

Hamed Taherdoost^1,2,3,4,*

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 79-104, 2024, DOI:10.32604/cmc.2024.054378 - 15 October 2024

Abstract Technical and accessibility issues in hospitals often prevent patients from receiving optimal mental and physical health care, which is essential for independent living, especially as societies age and chronic diseases like diabetes and cardiovascular disease become more common. Recent advances in the Internet of Things (IoT)-enabled wearable devices offer potential solutions for remote health monitoring and everyday activity recognition, gaining significant attention in personalized healthcare. This paper comprehensively reviews wearable healthcare technology integrated with the IoT for continuous vital sign monitoring. Relevant papers were extracted and analyzed using a systematic numerical review method, covering various More >

GyuHyun Jeon¹, Hojun Jin¹, Ju Hyeon Lee¹, Seungho Jeon², Jung Taek Seo^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1575-1622, 2024, DOI:10.32604/cmes.2024.053465 - 27 September 2024

Abstract The Internet of Wearable Things (IoWT) or Wearable Internet of Things (WIoT) is a new paradigm that combines IoT and wearable technology. Advances in IoT technology have enabled the miniaturization of sensors embedded in wearable devices and the ability to communicate data and access real-time information over low-power mobile networks. IoWT devices are highly interdependent with mobile devices. However, due to their limited processing power and bandwidth, IoWT devices are vulnerable to cyberattacks due to their low level of security. Threat modeling and frameworks for analyzing cyber threats against existing IoT or low-power protocols have… More >

Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 139-151, 2023, DOI:10.32604/iasc.2023.033940 - 05 February 2024

Abstract Accidents are still an issue in an intelligent transportation system, despite developments in self-driving technology (ITS). Drivers who engage in risky behavior account for more than half of all road accidents. As a result, reckless driving behaviour can cause congestion and delays. Computer vision and multimodal sensors have been used to study driving behaviour categorization to lessen this problem. Previous research has also collected and analyzed a wide range of data, including electroencephalography (EEG), electrooculography (EOG), and photographs of the driver’s face. On the other hand, driving a car is a complicated action that requires… More >

Fadhil Mukhlif^1,*, Norafida Ithnin¹, Roobaea Alroobaea², Sultan Algarni³, Wael Y. Alghamdi², Ibrahim Hashem⁴

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 583-599, 2023, DOI:10.32604/cmc.2023.038757 - 31 October 2023

Abstract The World Health Organization (WHO) refers to the 2019 new coronavirus epidemic as COVID-19, and it has caused an unprecedented global crisis for several nations. Nearly every country around the globe is now very concerned about the effects of the COVID-19 outbreaks, which were previously only experienced by Chinese residents. Most of these nations are now under a partial or complete state of lockdown due to the lack of resources needed to combat the COVID-19 epidemic and the concern about overstretched healthcare systems. Every time the pandemic surprises them by providing new values for various… More >

Sakorn Mekruksavanich¹, Narit Hnoohom², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2669-2686, 2023, DOI:10.32604/iasc.2023.038549 - 11 September 2023

Abstract Recognition of human activity is one of the most exciting aspects of time-series classification, with substantial practical and theoretical implications. Recent evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor environments. Consequently, researchers have demonstrated considerable passion for developing cutting-edge deep learning systems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many contexts. This study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called SenPyramidNet… More >

Junqi Guo^1,2, Qingyun Xiong^1,*, Minghui Yang¹, Ziyun Zhao¹

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 827-848, 2023, DOI:10.32604/cmc.2023.036450 - 08 June 2023

Abstract Nowadays, smart wearable devices are used widely in the Social Internet of Things (IoT), which record human physiological data in real time. To protect the data privacy of smart devices, researchers pay more attention to federated learning. Although the data leakage problem is somewhat solved, a new challenge has emerged. Asynchronous federated learning shortens the convergence time, while it has time delay and data heterogeneity problems. Both of the two problems harm the accuracy. To overcome these issues, we propose an asynchronous federated learning scheme based on double compensation to solve the problem of time… More >

Shehab Khan Noor¹, Arif Mawardi Ismail¹, Mohd Najib Mohd Yasin^1,*, Mohamed Nasrun Osman¹, Thennarasan Sabapathy¹, Shakhirul Mat Salleh², Ping Jack Soh³, Ali Hanafiah Rambe⁴, Nurulazlina Ramli⁵

Computer Systems Science and Engineering, Vol.47, No.1, pp. 537-551, 2023, DOI:10.32604/csse.2023.037381 - 26 May 2023

Abstract This paper presents a flexible and wearable textile array antenna designed to generate Orbital Angular Momentum (OAM) waves with Mode +2 at 3.5 GHz (3.4 to 3.6 GHz) of the sub-6 GHz fifth-generation (5G) New Radio (NR) band. The proposed antenna is based on a uniform circular array of eight microstrip patch antennas on a felt textile substrate. In contrast to previous works involving the use of rigid substrates to generate OAM waves, this work explored the use of flexible substrates to generate OAM waves for the first time. Other than that, the proposed antenna… More >

Baraa Wasfi Salim¹, Bzar Khidir Hussan², Zainab Salih Ageed³, Subhi R. M. Zeebaree^4,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4593-4609, 2023, DOI:10.32604/cmc.2023.037514 - 31 March 2023

Abstract Recently, human healthcare from body sensor data has gained considerable interest from a wide variety of human-computer communication and pattern analysis research owing to their real-time applications namely smart healthcare systems. Even though there are various forms of utilizing distributed sensors to monitor the behavior of people and vital signs, physical human action recognition (HAR) through body sensors gives useful information about the lifestyle and functionality of an individual. This article concentrates on the design of an Improved Transient Search Optimization with Machine Learning based Behavior Recognition (ITSOML-BR) technique using body sensor data. The presented… More >

Narit Hnoohom¹, Sakorn Mekruksavanich², Anuchit Jitpattanakul^3,4,*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3371-3385, 2023, DOI:10.32604/iasc.2023.036551 - 15 March 2023

Abstract Falls are the contributing factor to both fatal and nonfatal injuries in the elderly. Therefore, pre-impact fall detection, which identifies a fall before the body collides with the floor, would be essential. Recently, researchers have turned their attention from post-impact fall detection to pre-impact fall detection. Pre-impact fall detection solutions typically use either a threshold-based or machine learning-based approach, although the threshold value would be difficult to accurately determine in threshold-based methods. Moreover, while additional features could sometimes assist in categorizing falls and non-falls more precisely, the estimated determination of the significant features would be… More >