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Telehealthcare systems are nowadays becoming a massive daily helping kit for elderly and disabled people. By using the Kinect sensors, remote monitoring has become easy. Also, the sensors' data are useful for the further improvement of the device. In this paper, we have discussed our newly developed “Eldo-care” system. This system is designed for t...
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Context 1
... As seen in Fig. 2, conventional 10-20 EEG systems gather information from various regions of the brain. After collecting the information, it transmits electrical impulses to the brain via a brain-computer interface. two additional features-facial appearance and audial-are similarly employed to monitor subject activity to identify disabilities (Fig. ...
Citations
... The Histogram of Oriented Gradients (HOG) is an extensively utilized feature for the detection of objects, and it offers a suitable trade-off among detection precision and difficulty when compared with other richer features [16]. Before the deep learning period [20][21][22][23], the object identification pipeline was parted into three stages: proposal creation, feature vector extraction, as well as region categorization. The major intention of the first stage was to look for regions in the image that might contain items. ...
Object detection has gradually developed into a popular research area by reason of the widespread use of Remote Sensing Images (RSIs) in both military and civil domains. However, complex background and problems with small items are the two biggest obstacles in object detection. Deep learning techniques have a significant advantage for object detection over conventional techniques that rely on manually derived characteristics, and they have received a lot of attention. Due to the wide range of objects and complex visual backgrounds in RSIs, current deep learning algorithms in the area of RSI object detection leave much to be desired. For this case, algorithms need to be specifically optimized. In this paper, a novel optimization‐driven Enhanced Faster Region Convolutional Neural Network (FRCNN) is proposed for object detection. This approach has five modules, namely pre‐processing, data augmentation, segmentation, feature extraction, and object detection. The detection process is performed using Enhanced FRCNN and it is trained by the proposed Gazelle White Shark Optimization Algorithm (GWSOA). Extensive experiments in a high‐resolution RSI data set have exposed the efficacy of the proposed approach. The proposed model achieved a better accuracy of 97.31%, Mean Average Precision (MAP) of 97.85%, precision of 97.76%, recall of 96.16%, F‐score of 95.78% and an error rate of 2.69. image
... Machine learning (ML) schemes compensate for human shortcomings in managing the large volume of flowing information effectively, in which a small number of errors might lead to the difference between life and death. Deep learning applications have recently added value in computer vision, medicine (Lustberg et al. 2018;Chang et al. 2018;Laghari et al. 2024bLaghari et al. , 2023bFan et al. 2019;Saeed et al. 2024;Das et al. 2023;Wang et al. 2018), finance, entertainment, the Internet of Things (IoT), security, and pattern recognition. A significant aspect of this accomplishment is that deep learning algorithms can extract high-level features directly from the data. ...
Nowadays, cancer is one of the fatal diseases with an increasing death toll globally. Plenteous types of treatments have emerged to remove tumors. So far, auto-detection of trivial-size tumors is quite challenging for image-guided radiation therapy (IGRT) systems. Computer vision-based techniques are being applied with imaged-guided radiation therapy in the case of tiny tumor treatment, comparatively with more precision and accuracy. These techniques are objectively designed to assist the IGRT team in diagnosing it accurately and improving outcome predictions. This research paper proposes a deep reinforcement learning-based scheme to detect tiny size tumors autonomously. Using this technique, an image-guided radiation therapy system, like cyber-knife, may learn to detect tumors without any human in- intervention. A composite technique of deep Q-Network and Genetic Algorithm (GA), Open AI, and ROS frameworks are used as building blocks; with 2-D simulation, prominent results are produced. Around 95% of detection accuracy is obtained by using this proposed solution, which gradually converges optimality by learning reward. The previous state of art achieves more than 93% accuracy. The promised outcome endorses the idea that deep reinforcement learning with evolutionary algorithms may enable IGRT systems to detect tumors autonomously with more precision and accuracy.
... In the literature survey, diverse technologies are employed for healthcare applications. "Eldo-Care" [9] integrates EEG and Kinect sensors to enable telehealth care for disabled and elderly individuals, offering remote monitoring and assistance. [10] conducted a study employing various machine learning algorithms to Alzheimer's disease, Parkinson's disease along with anxiety, and also stress. ...
Early diagnosis is essential for prompt care and intervention of neurode-generative disorders including Parkinson’s and Alzheimer’s. In this study, we propose a novel approach utilizing multiband nonlinear EEG analysis for the early diagnosis of these diseases. By extracting and analyzing brain signals across multiple
frequency bands, along with nonlinear features, we aim to uncover subtle but significant alterations indicative of disease onset. Our findings suggest promising potential for accurately detecting Alzheimer’s and Parkinson’s diseases at an early stage,
paving the way for improved prognosis and targeted therapeutic interventions. The graph indicates that BPNN achieved the highest accuracy among these algorithms, achieving 95.5%. The system bridges gap in healthcare, offering timely diagnoses for
Alzheimer’s and Parkinson’s, fostering a more inclusive and proactive social support system.
... Li et al. [21] introduced a fiber-optic wearable wristband that captures precise pulse waveforms and derives pulse transit time to estimate blood pressure in real time. Das et al. [22] developed a comprehensive system for assessing psychological and cognitive states. Their platform records brain activity through electroencephalography, tracks the full body movements of elderly people with motion sensors, and recognizes gestures and facial expressions. ...
... Table 2 summarizes the data types utilized in the study described in home health care for the elderly. [20] ✓ [21] ✓ [22] ✓ ✓ [24] ✓ ✓ [25] ✓ [9] ✓ ✓ ✓ [26] ✓ [29] ✓ ✓ ✓ [30] ✓ [32] ✓ ✓ [34] ✓ ✓ ✓ ✓ ✓ [35] ✓ ✓ ✓ [37] ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ [10] ✓ ✓ ✓ ✓ ✓ [40] ✓ ✓ ✓ [41] ✓ ✓ ✓ [42] ✓ ✓ ✓ [44] ✓ ✓ ...
The rapid growth of China’s aging population has made elderly care a pressing social issue. Due to an imperfect pension system, limited uptake of institutional care, and uneven regional economic development, most elderly people in China still rely on home-based care. Elderly people living at home are usually cared for by their family, partners, caregivers, or themselves. However, this often fails to meet their complex health, safety, and emotional needs. Artificial intelligence may provide promising solutions to improve home care experiences and address the multifaceted health and lifestyle challenges faced by homebound elderly people. This review explores the applications of artificial intelligence in home-based care from four main perspectives: home health care, home safety and security, smart life assistants, and psychological care and emotional support. We systematically searched PubMed, IEEE Xplore, CNKI, and Scopus databases, integrated the latest research published between 2015 and 2024, focused on peer-reviewed, practice-oriented research, and reviewed relevant technology development paths and the current status of the field. Unlike previous studies that focused on physiological monitoring, this study is the first to systematically and comprehensively evaluate the role of artificial intelligence in improving the convenience of daily life and mental health support for elderly people at home. By comprehensively reviewing and analyzing the basic principles and application background of artificial intelligence technology in this field, we summarize the current technical and ethical challenges and propose future research directions. This study aims to help readers gain a deeper understanding of the current status and emerging trends of artificial intelligence-enabled home-based elderly care, thereby providing valuable insights for continued innovation and application in this rapidly developing field.
... Finally, to predict the quality score, a fully connected layer (FC) [49] [50] is used to map the extracted features X 4 to a specific image quality score. The calculation of the image quality score for each image is conducted as follows: ...
Underwater Image Quality Assessment (UIQA)
plays an important role in assess the effectiveness of Underwater
Image Enhancement (UIE) algorithms or to evaluate the
quality of underwater images. However, accurate UIQA that
are consistent with human perception remains challenging. This
dilemma on one hand is attributed to the lack of real human
visual perception UIQA data, and on the other hand that the
quality feature representation used by existing UIQA algorithms
are inconsistent with human perceptions. To address these issues,
we introduce a Large scale Underwater Image Quality Dataset
(LUIQD), and propose an UIQA network named as Perception-
Aware Underwater image Quality Assessment Network (PAUQANet).
Specifically, the LUIQD includes 64,180 real and enhance
underwater images covering a wide range of scenes, target and
imaging conditions, with their perceptual quality scores. Based on
the analysis of the mechanisms of human perception, we further
design the data-driven PAUQA-Net that integrates an efficient
convolutional attention vision Transformer to extract multi-scale
features by a multi-path structure. Considering the specificity of
human perception of underwater images, color and sharpness
features from the chrominance and luminance domains are
extracted and fused with local and global images features for joint
feature interaction. Extensive experiments conduted on LUIQD
and other datasets demonstrate that the proposed PAUQA-Net
achieves superior assessment performance compared with the
most popular UIQA and IQA methods. The code and dataset
can be found in https://github.com/CatchACat083/PAUQA.
... Molecular alignment based on key features facilitated the identification of representative conformers, providing insights into their structural properties and interactions with UCP1. These findings contribute to the understanding of UCP1 modulation and its potential role in metabolic regulation (Cavalieri et al. 2022;Das et al. 2023). ...
Uncoupling protein 1 (UCP1) plays a crucial role in thermogenesis and energy homeostasis, making it a promising therapeutic target for obesity and metabolic disorders. This study employs computational approaches, including molecular docking and molecular dynamics (MD) simulations, to identify potential UCP1 activators from natural compounds and other chemical libraries. Virtual screening of FDA-approved drugs, experimental compounds, and traditional Chinese medicine (TCM) revealed several high-affinity ligands, with Venetoclax and PD006556 exhibiting the strongest binding affinities. Among natural compounds, Epigallocatechin gallate (EGCG) showed the most favorable interaction with UCP1, supported by stable RMSD profiles and strong binding energy calculations using MM-PBSA. The ADMET analysis evaluated the pharmacokinetic and toxicity characteristics of these compounds, revealing that despite its significant solvation penalty, EGCG exhibited favorable bioavailability. The findings suggest that EGCG and PD006556 are potent UCP1 activators, offering potential therapeutic avenues for obesity treatment. This study underscores the utility of computational methods in accelerating drug discovery and provides a foundation for future experimental validation and therapeutic development.
... By substituting α u (z) i j from Equation (2) (the probability equation), we have: (19) Now, by substituting the value of α u (z) i j into the original objective function, the objective equation for optimal values of u (z) i j will be: ...
Medical image analysis often faces challenges due to noise, which can obscure crucial diagnostic information and hinder precise segmentation. Traditional denoising methods often fail to effectively suppress noise while preserving image details, resulting in blurred or overly smoothed outputs. To address this, we propose an improved fuzzy clustering algorithm that introduces an innovative integration of fast bilateral filtering and adaptive parameter tuning, offering superior noise reduction and enhanced medical image segmentation accuracy. Our method introduces a novel combination of fast bilateral filtering and an enhanced fuzzy C-means (FCM) algorithm, which effectively balances noise suppression and detail preservation, outperforming existing methods in both accuracy and efficiency. The fast bilateral filter efficiently preserves edge details while reducing spatial and local intensity variations, serving as a robust preprocessing step that mitigates noise-induced clustering errors. Additionally, we introduce an innovative strategy that calculates the absolute difference between the original and filtered images to enhance clustering accuracy in noisy environments. To improve convergence speed and computational efficiency, we refine the FCM objective function by incorporating a logarithmic summation of membership degrees from previous iterations, reducing iteration counts and accelerating convergence. Finally, we apply sharpening and median filtering techniques to refine segmentation outputs and enhance detail clarity. Experimental results on benchmark medical images demonstrate that our proposed method achieves superior noise suppression, improved segmentation accuracy, and faster convergence compared to conventional FCM and recent denoising techniques.
... Self-care is a general strategy used in the treatment process for individuals, but its specific applications must be tailored to each person's unique conditions, as they are not the same for diverse contexts and cultures. 51 Patients living in developed and high-income countries, such as the United States, rely on healthcare providers and primary health services to monitor and manage their symptoms and diseases. 23,52 However, in countries such as Iran, medical treatment is the primary focus of concern. ...
Background
Coronary artery disease (CAD) is a major cause of death worldwide. Uptake of self-care behaviors by patients with CAD could reduce hospital costs and negative social effects. Self-care among patients with CAD has not been studied in Iran.
Objective
In the present study, we assessed self-care levels and related factors in patients with CAD in Iran.
Methods
In this cross-sectional study, 250 patients hospitalized in Razi hospital in Birjand city between 2023 and 2024 were selected. Data were collected by demographic questionnaire and the Inventory of Self-care in Coronary Heart Disease. Bivariate analyses and multiple linear regression were used to analyze the data.
Results
Study participants were mostly male (62%), with elementary education (43.6%). The highest and the lowest scores were related to self-care maintenance (62.26 ± 15.14) and monitoring (45.66 ± 23.46), respectively. Smoking ( P < .001), employment status ( P = .020), hospitalization frequency, once ( P = .003) or twice ( P = .015), and emergency visits ( P = .008) were predictors of self-care maintenance. Gender ( P = .010), emergency visits ( P = .016), and comorbidities (diabetes, hyperlipidemia, and high blood pressure) or risk factors (2: P = .016; and 3: P = .003) were strong predictors of better self-care monitoring. Employment status ( P = .007), disease duration ( P = .011), and hospitalization frequency ( P = .033) were significant determinants of better self-care management.
Conclusions
Demographic characteristics and social context influence self-care levels of patients with CAD living in Iran. Therefore, person-oriented and gender-focused interventions may empower these patients to engage in self-care. We recommend development and testing of such interventions in Iran.
... This manuscript examines remote healthcare systems designed not only for the elderly but also for other vulnerable populations. The study presented in [25] introduces a system that integrates Kinect sensors with ECG, EEG, EMG, and PPG sensors to enable the comprehensive monitoring of brain activity, body movement, and physiological signals. This approach aims to enhance cognitive rehabilitation and remote patient monitoring, addressing the growing need for advanced technological solutions in managing neurological disorders. ...
... A pioneering telehealthcare system has also been designed to monitor the psychoneurological conditions of elderly and disabled individuals. The system combines Kinect sensors with ECG, EEG, EMG, and PPG sensors for the comprehensive monitoring of brain activity, body movement, and physiological signals [25]. A novel health detection system utilizing millimeter-wave radar technology integrates non-contact and contact mechanisms. ...
... Consequently, both systems have limitations tied to specific monitoring scenarios. Additionally, for comprehensive health monitoring, we have the pioneering telehealthcare system in [25], the millimeter-wave radar health detection system in [32], and the home robotics system based on cyber-physical systems in [33]. These systems share common strengths, such as real-time data analysis and visualization through cloud-based platforms. ...
The ever-evolving landscape of healthcare demands innovative solutions, particularly in light of the global health crisis of 2020 and the aging global population. Technological advancements and new approaches in remote health monitoring systems have helped to bridge the gap for vulnerable individuals such as older adults. This review explores methods for the analysis of physiological signals using remote and intelligent systems and mobile and web-based applications, mostly linked to wearable devices, focusing primarily on the elderly population. The main objective is to identify crucial advancements in the development or integration of technology applied to addressing challenges of this magnitude. The research is structured following the PRISMA-ScR guidelines. The search strategy was implemented in databases such as the ACM Digital Library, IEEE Xplore, PubMed, Science Direct, Scopus, and Springer Link. A total of 411 articles were collected, and inclusion and exclusion criteria were applied to focus on studies published between 2020 and 2024. Ultimately, 100 articles from 35 countries were selected for data extraction. The findings reveal significant progress in remote monitoring technologies but emphasize the need for rigorous validation to ensure accuracy and reliability across diverse populations. To develop robust systems that provide equitable and high-quality healthcare, it is essential to address critical challenges such as data privacy, security, accessibility, and ethical considerations.
... ➢ Electromyography (EEG) sensors: Similarly, electroencephalography (EEG) sensors are integral to BCI systems, facilitating direct communication between the brain and external devices. They offer various applications including neurofeedback training for cognitive rehabilitation, assistance in telerehabilitation, and interaction with gaming or virtual reality environments [59][60][61]. ...
... Joint torque [62][63][64] EEG-electrical activity of the brain [59][60][61] EEG-electrical activity of the brain [59] Joint angle [26,33] EMG-muscle activity [55,56] EMG-muscle activity [57] Joint stiffness [65] Range of motion [26] Range of motion [27,66] Pressure distribution [67,68] Maximum isometric force [42] Ground interaction force [41] Force feedback [66] Muscle contraction length [53] Muscle contraction length [46] Muscle activation [58] Muscle activation [57] Angle for specific motions (abduction/adduction; rotation; pronation/supination; flexion extension) [26] Inclination angle [28] Angular velocity [26] Angular velocity [26] Linear acceleration [19] Linear acceleration [19] Angular acceleration [29] Angular acceleration [66] Bioengineering 2025, 12, 287 7 o parameters measured on the robotic structures used in rehabilitation, and paramet measured on both the upper and lower limbs during motion, as can be seen in Figur This classification aims to offer an overview of the targeted signals by physicians in c rent procedures. Table 1. ...
... Lower Limb Joint torque [62][63][64] EEG-electrical activity of the brain [59][60][61] EEG-electrical activity of the brain [5 Joint angle [26,33] EMG-muscle activity [55,56] EMG-muscle activity [57] Joint stiffness [65] Range of motion [26] Range of motion [27,66] Pressure distribution [67,68] Maximum isometric force [42] Ground interaction force [41] Force feedback [66] Muscle contraction length [53] Muscle contraction length [46] Muscle activation [58] Muscle activation [57] Angle for specific motions (abduction/adduction; rotation; pronation/supination; flexion extension) [26] Inclination angle [28] Angular velocity [26] Angular velocity [26] Linear acceleration [19] Linear acceleration [19] Angular acceleration [29] Angular acceleration [66] ...
Neurological diseases leading to motor deficits constitute significant challenges to healthcare systems. Despite technological advancements in data acquisition, sensor development, data processing, and virtual reality (VR), a suitable framework for patient-centered neuromotor robot-assisted rehabilitation using collective sensor information does not exist. An extensive literature review was achieved based on 124 scientific publications regarding different types of sensors and the usage of the bio-signals they measure for neuromotor robot-assisted rehabilitation. A comprehensive classification of sensors was proposed, distinguishing between specific and non-specific parameters. The classification criteria address essential factors such as the type of sensors, the data they measure, their usability, ergonomics, and their overall impact on personalized treatment. In addition, a framework designed to collect and utilize relevant data for the optimal rehabilitation process efficiently is proposed. The proposed classifications aim to identify a set of key variables that can be used as a building block for a dynamic framework tailored for personalized treatments, thereby enhancing the effectiveness of patient-centered procedures in rehabilitation.
