Search Results (47)

Honey bees play a vital role in ecosystem stability, and the need to monitor colony health has driven the development of IoT-based systems in beekeeping, with recent studies exploring both empirical and machine learning approaches to detect and analyze key hive conditions. In this study, we present an IoT-based system that leverages sensors to record and analyze the acoustic signals produced within a beehive. The captured audio data is transmitted to the cloud, where it is converted into mel-spectrogram representations for analysis. We explore multiple data pre-processing strategies and machine learning (ML) models, assessing their effectiveness in classifying queenless states. To evaluate model generalization, we apply transfer learning (TL) techniques across datasets collected from different hives. Additionally, we implement the feature extraction process and deploy the pre-trained ML model on a deep edge IoT device (Arduino Zero). We examine both memory consumption and execution time. The results indicate that the selected feature extraction method and ML model, which were identified through extensive experimentation, are sufficiently lightweight to operate within the device’s memory constraints. Furthermore, the execution time confirms the feasibility of real-time queenless state detection in edge-based applications. Full article

Background/Objectives: Substance use disorders, particularly opioid addiction, continue to pose a major global health and toxicological challenge. Morphine dependence represents a significant problem in both clinical practice and preclinical research, particularly in modeling the pharmacodynamics of withdrawal. Rodent models remain indispensable for investigating the neurotoxicological effects of chronic opioid exposure and withdrawal. However, conventional behavioral assessments rely on manual observation, limiting objectivity, reproducibility, and scalability—critical constraints in modern drug toxicity evaluation. This study introduces MWB_Analyzer, an automated and high-throughput system designed to quantitatively and objectively assess morphine withdrawal behaviors in rats. The goal is to enhance toxicological assessments of CNS-active substances through robust, scalable behavioral phenotyping. Methods: MWB_Analyzer integrates optimized multi-angle video capture, real-time signal processing, and machine learning-driven behavioral classification. An improved YOLO-based architecture was developed for the accurate detection and categorization of withdrawal-associated behaviors in video frames, while a parallel pipeline processed audio signals. The system incorporates behavior-specific duration thresholds to isolate pharmacologically and toxicologically relevant behavioral events. Experimental animals were assigned to high-dose, low-dose, and control groups. Withdrawal was induced and monitored under standardized toxicological protocols. Results: MWB_Analyzer achieved over 95% reduction in redundant frame processing, markedly improving computational efficiency. It demonstrated high classification accuracy: >94% for video-based behaviors (93% on edge devices) and >92% for audio-based events. The use of behavioral thresholds enabled sensitive differentiation between dosage groups, revealing clear dose–response relationships and supporting its application in neuropharmacological and neurotoxicological profiling. Conclusions: MWB_Analyzer offers a robust, reproducible, and objective platform for the automated evaluation of opioid withdrawal syndromes in rodent models. It enhances throughput, precision, and standardization in addiction research. Importantly, this tool supports toxicological investigations of CNS drug effects, preclinical pharmacokinetic and pharmacodynamic evaluations, drug safety profiling, and regulatory assessment of novel opioid and CNS-active therapeutics. Full article

Background/Objectives: The COVID-19 pandemic affected frontline workers’ mental health, including healthcare workers, firefighters, and police officers, increasing the need for effective interventions. This study focuses on the pandemic’s psychological impact, perceived stress, depression/anxiety symptoms, and resilience, examining if a brief virtual reality (VR)–based relaxation session could reduce psychological symptoms. Methods: In this preliminary study with data collected in 2025 from frontline workers who had served during the acute phase of the COVID-19 pandemic, 54 frontline workers completed a baseline assessment of the perceived psychological impact of COVID-19 pandemic, general perceived well-being, perceived stress (PSS-4), anxiety/depression (PHQ-4) and resilience (RS-25). Each participant then engaged in a 10-min immersive VR relaxation session featuring a calming 360° nature environment with audio guidance, after which questionnaires were re-administered. Paired samples t-tests and repeated-measures ANOVA evaluated pre-/post-session differences, and a hierarchical multiple linear regression model tested predictors of the change in stress. Results: Pre-session results showed moderate perceived stress and resilience and low depression/anxiety. Occupation groups varied in baseline stress, mostly reporting negative pandemic psychological effects. After VR, significantly perceived well-being increased, and stress decreased, whereas depression/anxiety changes were nonsignificant. Repeated-measures ANOVA revealed a main effect of time on stress (p = 0.003) without occupation-by-time interaction (p = 0.246), indicating all occupational groups benefited similarly from the VR session. Hierarchical regression indicated baseline depression and higher perceived pandemic-related harm independently predicted greater stress reduction, whereas resilience and baseline anxiety showed no statistically significant results. Conclusions: A single VR relaxation session lowered perceived stress among frontline workers, particularly those reporting higher baseline depression or pandemic-related burden. Limitations include the absence of a control group. Results support VR-based interventions as feasible, rapidly deployable tools for high-stress settings. Future research should assess longer-term outcomes, compare VR to alternative interventions, and consider multi-session protocols. Full article

Background/Objectives: Midwives are crucial in addressing complex women’s health issues, such as infertility, breastfeeding challenges, and neonatal health. An elective pilot course, “Educational, Counseling, and Therapeutic Approaches in Midwifery”, was designed to enhance midwifery students’ knowledge and skills in communication, counseling, and therapeutic skills via e-learning approaches. Methods: A mixed-methods approach was employed, combining pre- and post-testing to assess students’ skill development. In addition, guided reflective discussions were based on video and audio recordings of pre-prepared role-playing scenarios. Students worked in pairs, alternating roles as midwives and patients, to engage in real-life situations. During the reflective discussions, students critically analyzed their experiences of the consultation process, identifying their strengths and weaknesses, and reflecting on what went well and what could be improved in future interactions. Results: The initial findings revealed that students were overconfident in their skills, but through role-playing and reflective discussions, they recognized gaps in their knowledge and developed a deeper understanding of essential competencies. Conclusions: The elective pilot course proved effective in enhancing students’ knowledge and skills as counselors. These results emphasize the importance of integrating structured e-learning and educational strategies into midwifery training to improve care and health outcomes. Full article

This study investigates the ability of well-known deep learning models, such as ResNet and EfficientNet, to perform audio-based infant cry detection. By comparing the performance of different machine learning algorithms, this study seeks to determine the most effective approach for the detection of infant crying, enhancing the functionality of baby monitoring systems and contributing to a more advanced understanding of audio-based deep learning applications. Understanding and accurately detecting a baby’s cries is crucial for ensuring their safety and well-being, a concern shared by new and expecting parents worldwide. Despite advancements in child health, as noted by UNICEF’s 2022 report of the lowest ever recorded child mortality rate, there is still room for technological improvement. This paper presents a comprehensive evaluation of deep learning models for infant cry detection, analyzing the performance of various architectures on spectrogram and MFCC feature representations. A key focus is the comparison between pretrained and non-pretrained models, assessing their ability to generalize across diverse audio environments. Through extensive experimentation, ResNet50 and DenseNet trained on spectrograms emerged as the most effective architectures, significantly outperforming other models in classification accuracy. Additionally, the study investigates the impact of feature extraction techniques, dataset augmentation, and model fine-tuning, providing deeper insights into the role of representation learning in audio classification. The findings contribute to the growing field of audio-based deep learning applications, offering a detailed comparative study of model architectures, feature representations, and training strategies for infant cry detection. Full article

Mental health assessment is typically carried out via a series of conversation sessions with medical professionals, where the overall aim is the diagnosis of mental illnesses and well-being evaluation. Despite its arguable socioeconomic significance, national health systems fail to meet the increased demand for such services that has been observed in recent years. To assist and accelerate the diagnosis process, this work proposes an AI-based tool able to provide interpretable predictions by automatically processing the recorded speech signals. An explainability-by-design approach is followed, where audio descriptors related to the problem at hand form the feature vector (Mel-scaled spectrum summarization, Teager operator and periodicity description), while modeling is based on Hidden Markov Models adapted from an ergodic universal one following a suitably designed data selection scheme. After extensive and thorough experiments adopting a standardized protocol on a publicly available dataset, we report significantly higher results with respect to the state of the art. In addition, an ablation study was carried out, providing a comprehensive analysis of the relevance of each system component. Last but not least, the proposed solution not only provides excellent performance, but its operation and predictions are transparent and interpretable, laying out the path to close the usability gap existing between such systems and medical personnel. Full article

The COVID-19 pandemic highlighted the role of urban parks as green spaces in mitigating social isolation and supporting public mental health. Research in this area is limited due to the lack of large-scale datasets. Moreover, timely studies are indeed necessary under pandemic conditions. This study employs quantitative methods to analyze the temporal and spatial changes in social interaction in 160 urban parks before, during, and after the COVID-19 pandemic, and assesses their correlation with the built environment. Social media data from the Dianping platform were collected for this purpose. A two-step analytical approach was employed: first, machine learning-based keyword analysis identified review data related to social interaction, leading to the construction of two indicators: social interaction intensity and social interaction recovery rate. Second, we applied regression models to explore the correlation between the two indicators in urban parks and 18 characteristics of the built environment. The built environment characteristics associated with social interaction intensity varied across different periods, with seven factors, including natural landscapes, perceptual experience, building density, and road intersections, showing significant correlations with the recovery of social interaction capabilities in the post-pandemic era. Based on these findings, it is recommended that urban planners consider integrating more flexible design element, such as adding greenery and enriching the audio-visual experience for visitors. Furthermore, enhancing the quality and accessibility of park amenities can foster social interaction, thereby contributing to public health resilience in future crises. This research recommends that urban park design should not only support communities’ immediate needs but also prepare for unforeseen challenges. Full article

Background: Artificial intelligence (AI) has been increasingly employed in healthcare across diverse domains, including medical imaging, personalized diagnostics, therapeutic interventions, and predictive analytics using electronic health records. Its integration is particularly impactful in critical care, where AI has demonstrated the potential to enhance patient outcomes. This systematic review critically evaluates the current applications of AI within the domain of critical care nursing. Methods: This systematic review is registered with PROSPERO (CRD42024545955) and was conducted in accordance with PRISMA guidelines. Comprehensive searches were performed across MEDLINE/PubMed, SCOPUS, CINAHL, and Web of Science. Results: The initial review identified 1364 articles, of which 24 studies met the inclusion criteria. These studies employed diverse AI techniques, including classical models (e.g., logistic regression), machine learning approaches (e.g., support vector machines, random forests), deep learning architectures (e.g., neural networks), and generative AI tools (e.g., ChatGPT). The analyzed health outcomes encompassed postoperative complications, ICU admissions and discharges, triage assessments, pressure injuries, sepsis, delirium, and predictions of adverse events or critical vital signs. Most studies relied on structured data from electronic medical records, such as vital signs and laboratory results, supplemented by unstructured data, including nursing notes and patient histories; two studies also integrated audio data. Conclusion: AI demonstrates significant potential in nursing, facilitating the use of clinical practice data for research and decision-making. The choice of AI techniques varies based on the specific objectives and requirements of the model. However, the heterogeneity of the studies included in this review limits the ability to draw definitive conclusions about the effectiveness of AI applications in critical care nursing. Future research should focus on more robust, interventional studies to assess the impact of AI on nursing-sensitive outcomes. Additionally, exploring a broader range of health outcomes and AI applications in critical care will be crucial for advancing AI integration in nursing practices. Full article

The need for safe and healthy air quality has become critical as urbanization and industrialization increase, leading to health risks and environmental concerns. Gas leaks, particularly of gases like carbon monoxide, methane, and liquefied petroleum gas (LPG), pose significant dangers due to their flammability and toxicity. LPG, widely used in residential and industrial settings, is especially hazardous because it is colorless, odorless, and highly flammable, making undetected leaks an explosion risk. To mitigate these dangers, modern gas detection systems employ sensors, microcontrollers, and real-time monitoring to quickly identify dangerous gas levels. This study introduces an IoT-based system designed for comprehensive environmental monitoring, with a focus on detecting LPG and butane leaks. Using sensors like the MQ6 for gas detection, MQ135 for air quality, and DHT11 for temperature and humidity, the system, managed by an Arduino Mega, collects data and sends these to the ThingSpeak platform for analysis and visualization. In cases of elevated gas levels, it triggers an alarm and notifies the user through IFTTT. Additionally, the system includes a microphone and a CNN model for analyzing audio data, enabling a thorough environmental assessment by identifying specific sounds related to ongoing activities, reaching an accuracy of 96%. Full article

Background/Objectives: Cognition plays a major role in prosthetic rehabilitation success. The ability to identify patients who may have difficulty understanding and adapting to the rehabilitation process is beneficial for clinicians and patients to allow for targeted and appropriate therapy. The research aim was to codesign a process that facilitates routine cognitive screening into the amputee inpatient journey. Methods: A convenience sample of sixteen medical and allied health practitioners from one local health district undertook a codesign process over 10 months from March to November 2023. A combination of virtual and face-to-face data collection occurred. Each of the codesign meetings was audio recorded, following which transcription occurred. Transcripts were reviewed using thematic analysis-based techniques to capture themes and consensus within the group. Results: Two pathways were established for use within one local health district, allowing clinicians to measure the cognition of patients in both inpatient and outpatient settings either before or after they underwent amputation. The newly established pathways provide step-by-step guidance for clinicians, such as how to address contraindicators for testing and providing guidance for subsequent neuropsychological testing. The Montreal Cognitive Assessment (MoCA), both paper based and electronic based, was selected as the cognitive screening tool for implementation. Conclusions: Utilizing codesign as a method for generating a cognitive screening pathway for amputees was successful. The pathways generated should be reviewed for suitability for application in other health settings. Full article

MHealth strategies have been used in various health areas, and mobile apps have been used in the context of health self-management. They can be considered an adjuvant intervention in oral health literacy, mainly for people with special health needs. Thus, the aim of this study was to identify the improvement of oral health literacy in patients with special needs when using digital platforms. A systematic literature review, based on the Joanna Briggs Institute (JBI) guidelines, was the main research method employed in this study. A search was undertaken in PubMed/MEDLINE and Cochrane Central Register of Controlled Trials (CENTRAL) databases, according to the relevant Mesh descriptors, their synonyms, and free terms (Entry Terms). Studies published between the years 2012 and 2023 were included. Two researchers independently assessed the quality of the included studies by completing the Newcastle–Ottawa Quality Assessment Scale questionnaire. The analysis corpus comprised 5 articles among the 402 articles selected after applying the inclusion/exclusion criteria (k = 0.97). The evidence from the considered articles is consensual regarding the effectiveness of using new technologies and innovations in promoting oral health literacy in patients with special health needs. The interventions were based on using the Illustration Reinforcement Communication System, inspired by the Picture Exchange Communication System, Nintendo^® Wii™ TV, virtual reality, smartphones, with software applications to read messages sent, Audio Tactile Performance technique, and Art package. One study had a low-quality assessment, and four had a high quality. The evidence from the articles included in this systematic review is consistent regarding the effectiveness of using new technologies and innovations in promoting oral health literacy in patients with special health needs. Full article

Objective: This qualitative study sought to explore patient experiences with technologies used in the Community Health in a Virtual Environment (Co-HIVE) pilot trial. Technology is becoming increasingly prevalent in mental healthcare, and user acceptance is critical for successful adoption and therefore clinical impact. The Co-HIVE pilot trialled a model of care whereby community-dwelling patients with symptoms of depression utilised virtual appointments and remote monitoring for the assessment and management of their condition, as an adjunct to routine care. Methods: Using a qualitative descriptive design, participants for this study were patients with symptoms of moderate to severe depression (based on the 9-item Patient Health Questionnaire, PHQ-9), who had completed the Co-HIVE pilot. Data was collected via semi-structured interviews that were audio-recorded, transcribed clean-verbatim, and thematically analysed using the Framework Method. Results: Ten participants completed the semi-structured interviews. Participants reported experiencing more personalised care, improved health knowledge and understanding, and greater self-care, enabled by the remote monitoring technology. Additionally, participants reported virtual appointments supported the clinician–patient relationship and improved access to mental health services. Conclusions: This experience of participants with the Co-HIVE pilot indicates there is a degree of acceptance of health technologies for use with community mental healthcare. This acceptance demonstrates opportunities to innovate existing mental health services by leveraging technology. Full article

Depression is a major psychological disorder with a growing impact worldwide. Traditional methods for detecting the risk of depression, predominantly reliant on psychiatric evaluations and self-assessment questionnaires, are often criticized for their inefficiency and lack of objectivity. Advancements in deep learning have paved the way for innovations in depression risk detection methods that fuse multimodal data. This paper introduces a novel framework, the Audio, Video, and Text Fusion-Three Branch Network (AVTF-TBN), designed to amalgamate auditory, visual, and textual cues for a comprehensive analysis of depression risk. Our approach encompasses three dedicated branches—Audio Branch, Video Branch, and Text Branch—each responsible for extracting salient features from the corresponding modality. These features are subsequently fused through a multimodal fusion (MMF) module, yielding a robust feature vector that feeds into a predictive modeling layer. To further our research, we devised an emotion elicitation paradigm based on two distinct tasks—reading and interviewing—implemented to gather a rich, sensor-based depression risk detection dataset. The sensory equipment, such as cameras, captures subtle facial expressions and vocal characteristics essential for our analysis. The research thoroughly investigates the data generated by varying emotional stimuli and evaluates the contribution of different tasks to emotion evocation. During the experiment, the AVTF-TBN model has the best performance when the data from the two tasks are simultaneously used for detection, where the F1 Score is 0.78, Precision is 0.76, and Recall is 0.81. Our experimental results confirm the validity of the paradigm and demonstrate the efficacy of the AVTF-TBN model in detecting depression risk, showcasing the crucial role of sensor-based data in mental health detection. Full article

Electronic patient portals represent a promising means of integrating mental health assessments into HIV care where anxiety and depression are highly prevalent. Patient attitudes toward portal-based mental health screening within HIV clinics have not been well described. The aim of this formative qualitative study is to characterize the patient-perceived facilitators and barriers to portal-based anxiety and depression screening within HIV care in order to inform implementation strategies for mental health screening. Twelve adult HIV clinic patients participated in semi-structured interviews that were audio recorded and transcribed. The transcripts were coded using constructs from the Consolidated Framework for Implementation Research and analyzed thematically to identify the barriers to and facilitators of portal-based anxiety and depression screening. Facilitators included an absence of alternative screening methods, an approachable design, perceived adaptability, high compatibility with HIV care, the potential for linkage to treatment, an increased self-awareness of mental health conditions, the ability to bundle screening with clinic visits, and communicating an action plan for results. The barriers included difficulty navigating the patient portal system, a lack of technical support, stigmatization from the healthcare system, care team response times, and the novelty of using patient portals for communication. The patients in the HIV clinic viewed the use of a portal-based anxiety and depression screening tool as highly compatible with routine HIV care. Technical difficulties, follow-up concerns, and a fear of stigmatization were commonly perceived as barriers to portal use. The results of this study can be used to inform implementation strategies when designing or incorporating portal-based mental health screening into other HIV care settings. Full article

Those with visual impairments, including complete blindness or partial sight loss, constitute a significant global population. According to estimates by the World Health Organization (WHO), there are at least 2.2 billion people worldwide who have near or distance vision disorders. Addressing their needs is crucial. Introducing a smart cane tailored for the blind can greatly improve their daily lives. This paper introduces a significant technical innovation, presenting a smart cane equipped with dual ultrasonic sensors for obstacle detection, catering to the visually impaired. The primary focus is on developing a versatile device capable of operating in diverse conditions, ensuring efficient obstacle alerts. The strategic placement of ultrasonic sensors facilitates the emission and measurement of high-frequency sound waves, calculating obstacle distances and assessing potential threats to the user. Addressing various obstacle types, two ultrasonic sensors handle overhead and ground-level barriers, ensuring precise warnings. With a detection range spanning 2 to 400 cm, the device provides timely information for user reaction. Dual alert methods, including vibrations and audio signals, offer flexibility to users, controlled through intuitive switches. Additionally, a Bluetooth-connected mobile app enhances functionality, activating audio alerts if the cane is misplaced or too distant. Cost-effective implementation enhances accessibility, supporting a broader user base. This innovative smart cane not only represents a technical achievement but also significantly improves the quality of life for visually impaired individuals, emphasizing the social impact of technology. The research underscores the importance of technological research in addressing societal challenges and highlights the need for solutions that positively impact vulnerable communities, shaping future directions in research and technological development. Full article