Brij B. Gupta^1,2,3,*, Akshat Gaurav⁴, Razaz Waheeb Attar⁵, Varsha Arya^6,7, Ahmed Alhomoud⁸, Kwok Tai Chui⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2689-2706, 2024, DOI:10.32604/cmes.2024.050825

Abstract This study introduces a long-short-term memory (LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes, focusing on the critical application of elderly fall detection. It balances the dataset using the Synthetic Minority Over-sampling Technique (SMOTE), effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification tasks. The proposed LSTM model is trained on the enriched dataset, capturing the temporal dependencies essential for anomaly recognition. The model demonstrated a significant improvement in anomaly detection, with an accuracy of 84%. The results, detailed in the comprehensive classification and confusion More >

YUANYUAN LIAO^1,2,3, XINSI LI^2,3, QIAN LI^2,3, YIZHONG WANG⁴, XIUJUN TAN^1,2,3, TING GONG^2,3,5,*

BIOCELL, Vol.48, No.6, pp. 873-887, 2024, DOI:10.32604/biocell.2024.050324

Abstract As we are facing an aging society, anti-aging strategies have been pursued to reduce the negative impacts of aging and increase the health span of human beings. Gut microbiota has become a key factor in the anti-aging process. Modulation of gut microbiota by fecal microbiota transplantation (FMT) to prevent frailty and unhealthy aging has been a hot topic of research. This narrative review summarizes the benefits of FMT for health span and lifespan, brains, eyes, productive systems, bones, and others. The mechanisms of FMT in improving healthy aging are discussed. The increased beneficial bacteria and More >

Yan Huang^1,*, Xiaoye Liao², Fen Cai³

International Journal of Mental Health Promotion, Vol.25, No.12, pp. 1313-1321, 2023, DOI:10.32604/ijmhp.2023.030558

Abstract Background: Elderly people with depression require special care and attention. However, nostalgia is a complex emotional situation for a person who recalls the missing past. To improve mental health, quality of life, and attitudes toward aging in institutional care, group nostalgia therapy can be a nursing intermediary for the elderly. This study aimed to analyze the effect of group nostalgia therapy on quality of life cognitive function in elderly patients with depression. Methods: A total of 89 participants were enrolled in this study, which was further categorized into a control (n = 40) and a… More >

Eun Kyoung Han, Hae Kyoung Son^*

International Journal of Mental Health Promotion, Vol.25, No.11, pp. 1197-1208, 2023, DOI:10.32604/ijmhp.2023.041560

Abstract Dementia prevalence has soared due to population aging. In Mild Cognitive Impairment (MCI) as a pre-dementia stage, sleep disturbances have raised much interest as a factor in a bidirectional relationship with cognitive decline. Thus, this study developed the Sleep and Cognition Enhancement Multimodal Intervention (SCEMI) based on Lazarus’ multimodal approach and conducted a randomized controlled experiment to investigate the effects of the novel program on sleep and cognition in MCI elderly. The participants were 55 MCI elderly with sleep disturbances at two dementia care centers located in S-city, Gyeonggi-do, South Korea (n = 25 in… More >

Omar Reyad^1,2, Hazem Ibrahim Shehata^1,3, Mohamed Esmail Karar^1,4,*

CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1689-1700, 2023, DOI:10.32604/cmc.2023.039084

Abstract Falling is among the most harmful events older adults may encounter. With the continuous growth of the aging population in many societies, developing effective fall detection mechanisms empowered by machine learning technologies and easily integrable with existing healthcare systems becomes essential. This paper presents a new healthcare Internet of Health Things (IoHT) architecture built around an ensemble machine learning-based fall detection system (FDS) for older people. Compared to deep neural networks, the ensemble multi-stage random forest model allows the extraction of an optimal subset of fall detection features with minimal hyperparameters. The number of cascaded… More >

Lulu Yin^1,#, Kun Dong^1,#, Zhangqi Lai², Lin Wang^1,*

Molecular & Cellular Biomechanics, Vol.20, No.1, pp. 23-33, 2023, DOI:10.32604/mcb.2023.040788

Abstract Age-related loss of foot intrinsic muscle (FIM) strength may be associated with disability, falls, and inability to perform daily activities. Previous studies have determined the reliability of FIM strength testing and evaluated the relationship between FIM strength and corresponding muscle morphology in young adults. However, few studies have measured FIM strength in the older. Therefore, this study aimed to assess the intra- and inter-reliability of FIM strength tests and the relationship between FIM strength and FIM size in the older. A total of 61 participants aged 60–75 years were recruited, and 18 of them were… More > Graphic Abstract

Muath Jarrah¹, Hussam Al Hamadi^4,*, Ahmed Abu-Khadrah², Taher M. Ghazal^1,3

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 19-33, 2023, DOI:10.32604/cmc.2023.032775

Abstract The Internet of Medical Things (IoMT) enables digital devices to gather, infer, and broadcast health data via the cloud platform. The phenomenal growth of the IoMT is fueled by many factors, including the widespread and growing availability of wearables and the ever-decreasing cost of sensor-based technology. There is a growing interest in providing solutions for elderly people living assistance in a world where the population is rising rapidly. The IoMT is a novel reality transforming our daily lives. It can renovate modern healthcare by delivering a more personalized, protective, and collaborative approach to care. However, More >

Yuxin Wang^*, Haoyue Ma, Lan Zheng

International Journal of Mental Health Promotion, Vol.25, No.5, pp. 673-686, 2023, DOI:10.32604/ijmhp.2023.026394

Abstract This study empirically examines whether child characteristics mitigate the negative impact of widowhood on the elderly’s mental health using follow-up survey data from the China Health and Retirement Longitudinal Study (CHARLS). A total of 5,326 older adults aged 60 years and older are selected from three waves of panel data (2013, 2015, and 2018). The findings suggest that respondents who experienced widowhood exhibit an increase in depressive symptoms. However, the higher income of children and frequent face-to-face emotional interactions improve the mental health of the widowed elderly. Moreover, heterogeneity analyses show that the buffering effect… More >

Mohamed Esmail Karar^1,2,*, Omar Reyad^1,3, Hazem Ibrahim Shehata^1,4

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2377-2389, 2023, DOI:10.32604/csse.2023.032931

Abstract This article introduces a new medical internet of things (IoT) framework for intelligent fall detection system of senior people based on our proposed deep forest model. The cascade multi-layer structure of deep forest classifier allows to generate new features at each level with minimal hyperparameters compared to deep neural networks. Moreover, the optimal number of the deep forest layers is automatically estimated based on the early stopping criteria of validation accuracy value at each generated layer. The suggested forest classifier was successfully tested and evaluated using a public SmartFall dataset, which is acquired from three-axis… More >

Abeer Abdulaziz AlArfaj, Hanan Ahmed Hosni Mahmoud^*

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2527-2540, 2023, DOI:10.32604/iasc.2023.030168

Abstract The detection of heart disease is a problematic task in medical research. This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history. The massive advances in deep learning models pursue the development of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support. Therefore, in this paper, we propose a deep learning model for elderly patients to aid and enhance the diagnosis of heart disease. The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques. The… More >