Maie Aboghazalah^1,*, Passent El-kafrawy², Abdelmoty M. Ahmed³, Rasha Elnemr⁵, Belgacem Bouallegue³, Ayman El-sayed⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3855-3875, 2024, DOI:10.32604/cmc.2023.039936

Abstract Heart monitoring improves life quality. Electrocardiograms (ECGs or EKGs) detect heart irregularities. Machine learning algorithms can create a few ECG diagnosis processing methods. The first method uses raw ECG and time-series data. The second method classifies the ECG by patient experience. The third technique translates ECG impulses into Q waves, R waves and S waves (QRS) features using richer information. Because ECG signals vary naturally between humans and activities, we will combine the three feature selection methods to improve classification accuracy and diagnosis. Classifications using all three approaches have not been examined till now. Several More >

Jianguo Wang¹, Kuan Zhang^1,*, Yuesheng Zhao^2,*, Xiaoling Wang², Muhammad Shamrooz Aslam²

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 79-96, 2023, DOI:10.32604/cmc.2023.040506

Abstract The purpose of Human Activities Recognition (HAR) is to recognize human activities with sensors like accelerometers and gyroscopes. The normal research strategy is to obtain better HAR results by finding more efficient eigenvalues and classification algorithms. In this paper, we experimentally validate the HAR process and its various algorithms independently. On the base of which, it is further proposed that, in addition to the necessary eigenvalues and intelligent algorithms, correct prior knowledge is even more critical. The prior knowledge mentioned here mainly refers to the physical understanding of the analyzed object, the sampling process, the More >