Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer’s disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual’s activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults’ activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful.

This study introduces an innovative method for minimizing artifacts in electroencephalography (EEG) signals by integrating brainstorm optimization (BSO) with a variational autoencoder generative adversarial network (VAE-GAN), resulting in the BrOpt_VAGAN model. EEG signals are critical for the diagnosis of neurological disorders, for brain-computer interface (BCI) applications, and for the monitoring of neurological disabilities. However, EEG data often contains artifacts from physiological sources — such as electrooculographic (EOG), electromyographic (EMG), and electrocardiographic (ECG) signals — which can distort the accuracy of brain activity readings. Our proposed BrOpt_VAGAN model combines BSO with a VAE-GAN framework to more effectively remove these artifacts, thus improving the clarity and accuracy of EEG signals. In this model, the VAE first reduces the raw EEG signals into a lower-dimensional representation that captures the essential signal patterns while filtering out the noise. The GAN component then refines this representation via adversarial training, effectively minimizing artifacts and improving the quality of the processed EEG data. BSO optimally adjusts the encoding and decoding parameters within the VAE-GAN structure, enabling the model to handle different noise levels and helps to find different neurological disorders. Preliminary results show that BrOpt_VAGAN performs significantly better with an accuracy of 98.5 % and an error rate of 11.23 %, enabling a clearer and more precise EEG signal reconstruction.

Brain-Computer Interfaces (BCIs) facilitate the translation of brain activity into actionable commands and act as a crucial link between the human brain and the external environment. Electroencephalography (EEG)-based BCIs, which focus on motor imagery, have emerged as an important area of study in this domain. They are used in neurorehabilitation, neuroprosthetics, and gaming, among other applications. Optimal Deep Learning-Based Recognition for EEG Signal Motor Imagery (ODLR-EEGSM) is a novel approach presented in this article that aims to improve the recognition of motor imagery from EEG signals. The proposed method includes several crucial stages to improve the precision and effectiveness of EEG-based motor imagery recognition. The pre-processing phase starts with the Variation Mode Decomposition (VMD) technique, which is used to improve EEG signals. The EEG signals are decomposed into different oscillatory modes by VMD, laying the groundwork for subsequent feature extraction. Feature extraction is a crucial component of the ODLR-EEGSM method. In this study, we use Stacked Sparse Auto Encoder (SSAE) models to identify significant patterns in the pre-processed EEG data. Our approach is based on the classification model using Deep Wavelet Neural Network (DWNN) optimized with Chaotic Dragonfly Algorithm (CDFA). CDFA optimizes the weight and bias values of the DWNN, significantly improving the classification accuracy of motor imagery. To evaluate the efficacy of the ODLR-EEGSM method, we use benchmark datasets to perform rigorous performance validation. The results show that our approach outperforms current methods in the classification of EEG motor imagery, confirming its promising performance. This study has the potential to make brain-computer interface applications in various fields more accurate and efficient, and pave the way for brain-controlled interactions with external systems and devices.

Knee osteoarthritis (KOA) is a degenerative joint disease predominantly affecting the elderly and is often managed through knee replacement surgeries. West Asians, who frequently engage in activities involving bending and kneeling during their prayers, tend to exhibit distinct bone anatomy compared to the Caucasian population. This research posits that patient-specific, customized knee implants with articular cartilages may lead to reduced post-surgical discomfort and a better implant fit compared to conventional standard implants. This study presents a novel concept and approach for the development of a customized total knee implant with articular cartilages, specifically tailored to simulate loading conditions using finite element analysis (FEA) for the Saudi Arabian population. The research analyses patient-specific customized knee implants with articular cartilages under both pre- and post-implant conditions using a finite element model (FEM). Computed tomography (CT) images of patients were utilized to create a solid model, which was then analysed under various constraints and conditions. The meshing process employed tetrahedral elements, converging with 76,197 nodes and 43,009 elements. The analysis was conducted under different body weights, specifically 75–80 kg and 100–105 kg. Two sets of force and moment were applied: the first with a force of 1000 N and a moment of 1.5 N-m, and the second with a force of 750 N and a moment of 0.8 N-m. The results indicated a 5% reduction in stress with implants designed for a 100 kg body weight, along with a significant reduction in ligament strain when compared to conventional knee joint stresses. This study offers a promising pathway toward reducing post-surgical discomfort. The proposed innovative solution has the potential to revolutionize total knee implant technology, offering enhanced functionality and improved patient outcomes for the Saudi Arabian population.