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

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 1245-1259, 2023, DOI:10.32604/iasc.2023.028290 - 06 June 2022

Abstract Smoking is a major cause of cancer, heart disease and other afflictions that lead to early mortality. An effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment initiatives. Smoking activities often accompany other activities such as drinking or eating. Consequently, smoking activity recognition can be a challenging topic in human activity recognition (HAR). A deep learning framework for smoking activity recognition (SAR) employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules (ResNetSE) to increase the effectiveness of the SAR framework. The More >

Muhammad Atif Hanif¹, Tallha Akram¹, Aamir Shahzad², Muhammad Attique Khan³, Usman Tariq⁴, Jung-In Choi⁵, Yunyoung Nam^6,*, Zanib Zulfiqar⁷

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3221-3234, 2022, DOI:10.32604/cmc.2022.019815 - 27 September 2021

Abstract Sensors based Human Activity Recognition (HAR) have numerous applications in eHeath, sports, fitness assessments, ambient assisted living (AAL), human-computer interaction and many more. The human physical activity can be monitored by using wearable sensors or external devices. The usage of external devices has disadvantages in terms of cost, hardware installation, storage, computational time and lighting conditions dependencies. Therefore, most of the researchers used smart devices like smart phones, smart bands and watches which contain various sensors like accelerometer, gyroscope, GPS etc., and adequate processing capabilities. For the task of recognition, human activities can be broadly… More >

Mohammed Alshehri^1,*, Purushottam Sharma², Richa Sharma², Osama Alfarraj³

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2525-2538, 2021, DOI:10.32604/cmc.2021.012469 - 28 December 2020

Abstract Sensors and physical activity evaluation are quite limited for motion-based commercial devices. Sometimes the accelerometer of the smartwatch is utilized; walking is investigated. The combination can perform better in terms of sensors and that can be determined by sensors on both the smartwatch and phones, i.e., accelerometer and gyroscope. For biometric efficiency, some of the diverse activities of daily routine have been evaluated, also with biometric authentication. The result shows that using the different computing techniques in phones and watch for biometric can provide a suitable output based on the mentioned activities. This indicates that… More >