Information Technology Department, Faculty of Computers & Informatics, Zagazig University, Zagazig 44519, Egypt.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
Sensors (Basel). 2023 Jun 24;23(13):5862. doi: 10.3390/s23135862.
In our current digital world, smartphones are no longer limited to communication but are used in various real-world applications. In the healthcare industry, smartphones have sensors that can record data about our daily activities. Such data can be used for many healthcare purposes, such as elderly healthcare services, early disease diagnoses, and archiving patient data for further use. However, the data collected from the various sensors involve high dimensional features, which are not equally helpful in human activity recognition (HAR).
This paper proposes an algorithm for selecting the most relevant subset of features that will contribute efficiently to the HAR process. The proposed method is based on a hybrid version of the recent Coronavirus Disease Optimization Algorithm (COVIDOA) with Simulated Annealing (SA). SA algorithm is merged with COVIDOA to improve its performance and help escape the local optima problem.
The UCI-HAR dataset from the UCI machine learning repository assesses the proposed algorithm's performance. A comparison is conducted with seven well-known feature selection algorithms, including the Arithmetic Optimization Algorithm (AOA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Reptile Search Algorithm (RSA), Zebra Optimization Algorithm (ZOA), Gradient-Based Optimizer (GBO), Seagull Optimization Algorithm (SOA), and Coyote Optimization Algorithm (COA) regarding fitness, STD, accuracy, size of selected subset, and processing time.
The results proved that the proposed approach outperforms state-of-the-art HAR techniques, achieving an average performance of 97.82% in accuracy and a reduction ratio in feature selection of 52.7%.
在我们当前的数字世界中，智能手机不再局限于通信，而是在各种现实应用中得到广泛应用。在医疗保健行业，智能手机配备了可以记录我们日常活动数据的传感器。这些数据可用于许多医疗保健用途，如老年人保健服务、早期疾病诊断以及为进一步使用而存档患者数据。然而，从各种传感器收集的数据涉及高维特征，这些特征在人类活动识别（HAR）中并不都有帮助。
本文提出了一种用于选择最相关特征子集的算法，这些子集将有效地促进 HAR 过程。所提出的方法基于最近的冠状病毒疾病优化算法（COVIDOA）与模拟退火（SA）的混合版本。SA 算法与 COVIDOA 融合，以提高其性能并帮助其克服局部最优问题。
使用 UCI 机器学习存储库中的 UCI-HAR 数据集评估了所提出算法的性能。与七种著名的特征选择算法进行了比较，包括算术优化算法（AOA）、灰狼优化算法（GWO）、鲸鱼优化算法（WOA）、爬行动物搜索算法（RSA）、斑马优化算法（ZOA）、基于梯度的优化算法（GBO）、海鸥优化算法（SOA）和郊狼优化算法（COA），比较指标包括适应性、标准差、准确性、所选子集的大小和处理时间。
结果证明，所提出的方法优于最先进的 HAR 技术，在准确性方面的平均性能达到 97.82%，在特征选择方面的缩减率达到 52.7%。
