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Sensors (Basel, Switzerland) Jun 2024This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to...
This study explored an indoor system for tracking multiple humans and detecting falls, employing three Millimeter-Wave radars from Texas Instruments. Compared to wearables and camera methods, Millimeter-Wave radar is not plagued by mobility inconveniences, lighting conditions, or privacy issues. We conducted an initial evaluation of radar characteristics, covering aspects such as interference between radars and coverage area. Then, we established a real-time framework to integrate signals received from these radars, allowing us to track the position and body status of human targets non-intrusively. Additionally, we introduced innovative strategies, including dynamic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering based on signal SNR levels, a probability matrix for enhanced target tracking, target status prediction for fall detection, and a feedback loop for noise reduction. We conducted an extensive evaluation using over 300 min of data, which equated to approximately 360,000 frames. Our prototype system exhibited a remarkable performance, achieving a precision of 98.9% for tracking a single target and 96.5% and 94.0% for tracking two and three targets in human-tracking scenarios, respectively. Moreover, in the field of human fall detection, the system demonstrates a high accuracy rate of 96.3%, underscoring its effectiveness in distinguishing falls from other statuses.
PubMed: 38894451
DOI: 10.3390/s24113660 -
Sensors (Basel, Switzerland) Jun 2024The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing data from local hospitals. However, privacy concerns can impede data...
The growth of IoT healthcare is aimed at providing efficient services to patients by utilizing data from local hospitals. However, privacy concerns can impede data sharing among third parties. Federated learning offers a solution by enabling the training of neural networks while maintaining the privacy of the data. To integrate federated learning into IoT healthcare, hospitals must be part of the network to jointly train a global central model on the server. Local hospitals can train the global model using their patient datasets and send the trained localized models to the server. These localized models are then aggregated to enhance the global model training process. The aggregation of local models dramatically influences the performance of global training, mainly due to the heterogeneous nature of patient data. Existing solutions to address this issue are iterative, slow, and susceptible to convergence. We propose two novel approaches that form groups efficiently and assign the aggregation weightage considering essential parameters vital for global training. Specifically, our method utilizes an autoencoder to extract features and learn the divergence between the latent representations of patient data to form groups, facilitating more efficient handling of heterogeneity. Additionally, we propose another novel aggregation process that utilizes several factors, including extracted features of patient data, to maximize performance further. Our proposed approaches for group formation and aggregation weighting outperform existing conventional methods. Notably, significant results are obtained, one of which shows that our proposed method achieves 20.8% higher accuracy and 7% lower loss reduction compared to the conventional methods.
Topics: Humans; Neural Networks, Computer; Internet of Things; Delivery of Health Care; Algorithms; Machine Learning
PubMed: 38894422
DOI: 10.3390/s24113632 -
Sensors (Basel, Switzerland) May 2024To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and...
To provide diverse in-home services like elderly care, versatile activity recognition technology is essential. Radio-based methods, including WiFi CSI, RFID, and backscatter communication, are preferred due to their minimal privacy intrusion, reduced physical burden, and low maintenance costs. However, these methods face challenges, including environmental dependence, proximity limitations between the device and the user, and untested accuracy amidst various radio obstacles such as furniture, appliances, walls, and other radio waves. In this paper, we propose a frequency-shift backscatter tag-based in-home activity recognition method and test its feasibility in a near-real residential setting. Consisting of simple components such as antennas and switches, these tags facilitate ultra-low power consumption and demonstrate robustness against environmental noise because a context corresponding to a tag can be obtained by only observing frequency shifts. We implemented a sensing system consisting of SD-WiFi, a software-defined WiFi AP, and physical switches on backscatter tags tailored for detecting the movements of daily objects. Our experiments demonstrate that frequency shifts by tags can be detected within a 2 m range with 72% accuracy under the line of sight (LoS) conditions and achieve a 96.0% accuracy (F-score) in recognizing seven typical daily living activities with an appropriate receiver/transmitter layout. Furthermore, in an additional experiment, we confirmed that increasing the number of overlaying packets enables frequency shift-detection even without LoS at distances of 3-5 m.
Topics: Activities of Daily Living; Humans; Wireless Technology; Radio Waves; Radio Frequency Identification Device
PubMed: 38894070
DOI: 10.3390/s24113277 -
Journal of Clinical Medicine Jun 2024Thoracic aortopathy includes conditions like aortic aneurysms and dissections, posing significant management challenges. In India, care delivery is complicated by...
Thoracic aortopathy includes conditions like aortic aneurysms and dissections, posing significant management challenges. In India, care delivery is complicated by geographic vastness, financial constraints, and healthcare resource disparities. Telemedicine and digital health technologies offer promising solutions. A comprehensive review of literature and clinical experiences was conducted to explore the implementation of remote care strategies for thoracic aortopathy in India. The review included studies from 2000 to 2023 and insights from cardiothoracic specialists. Remote care benefits include improved access to specialized expertise, enhanced patient engagement, and optimized resource utilization. Telemedicine enables consultations without travel, and remote monitoring facilitates early intervention. However, challenges like technology integration, digital literacy, patient engagement, privacy concerns, and regulatory compliance need addressing. Telemedicine offers significant advantages but requires overcoming challenges to ensure effective, secure care. Careful planning for technology integration, patient education, robust privacy measures, and supportive regulatory policies are essential. Addressing these issues can bridge the healthcare access gap and improve outcomes in India's diverse landscape.
PubMed: 38893041
DOI: 10.3390/jcm13113327 -
Genome Biology Jun 2024
PubMed: 38890766
DOI: 10.1186/s13059-024-03311-w -
BMC Psychiatry Jun 2024School truancy, deliberately skipping school without permission, is a complex issue with far-reaching consequences for individual students, education systems, and entire...
BACKGROUND
School truancy, deliberately skipping school without permission, is a complex issue with far-reaching consequences for individual students, education systems, and entire communities. While this phenomenon is not unique to Sierra Leone, the specific context of the post-conflict nation raises concerns about its potential impact on the country's fragile rebuilding process. This study examines the prevalence and predictors of school truancy among adolescents in Sierra Leone.
METHODS
The study analysed the cross-sectional 2017 Global School-based Health Survey (GSHS) data in Sierra Leone, a nationally representative survey conducted among adolescents aged 10-19 years using a multistage sampling methodology. A weighted sample of 2,769 adolescents in Sierra Leone was included in the study. A multivariable binary regression analysis was used to examine the predictors of school truancy among adolescents. The regression results were presented using an adjusted odds ratio (AOR) with 95% confidence intervals (CI).
RESULTS
The prevalence of school truancy was 35% among adolescents in Sierra Leone. Adolescents who use alcohol (AOR = 2.28, 95% CI = 1.45, 3.58) and who have ever had sexual intercourse (AOR = 1.67, 95% CI = 1.10, 2.53) had higher odds of being associated with school truancy. Adolescents who planned suicide (AOR = 0.58, 95% CI = 0.36, 0.93) and whose parents did not intrude on their privacy (AOR = 0.66, 95% CI = 0.45, 0.97) had lower odds of being associated with school truancy.
CONCLUSION
School truancy is a critical issue in Sierra Leone, demanding multi-pronged interventions at policy and practice levels. Addressing underlying causes like alcohol use, sexual behaviour, planned suicide, and parent's intrusion of privacy is crucial. Key strategies include fostering positive school environments, providing mental health support, and improving parent-child communication.
Topics: Humans; Sierra Leone; Adolescent; Female; Male; Prevalence; Cross-Sectional Studies; Child; Health Surveys; Schools; Young Adult; Students; Absenteeism; Adolescent Behavior
PubMed: 38890639
DOI: 10.1186/s12888-024-05888-9 -
Scientific Reports Jun 2024Smartphone sensors have gained considerable traction in Human Activity Recognition (HAR), drawing attention for their diverse applications. Accelerometer data monitoring...
Smartphone sensors have gained considerable traction in Human Activity Recognition (HAR), drawing attention for their diverse applications. Accelerometer data monitoring holds promise in understanding students' physical activities, fostering healthier lifestyles. This technology tracks exercise routines, sedentary behavior, and overall fitness levels, potentially encouraging better habits, preempting health issues, and bolstering students' well-being. Traditionally, HAR involved analyzing signals linked to physical activities using handcrafted features. However, recent years have witnessed the integration of deep learning into HAR tasks, leveraging digital physiological signals from smartwatches and learning features automatically from raw sensory data. The Long Short-Term Memory (LSTM) network stands out as a potent algorithm for analyzing physiological signals, promising improved accuracy and scalability in automated signal analysis. In this article, we propose a feature analysis framework for recognizing student activity and monitoring health based on smartphone accelerometer data through an edge computing platform. Our objective is to boost HAR performance by accounting for the dynamic nature of human behavior. Nonetheless, the current LSTM network's presetting of hidden units and initial learning rate relies on prior knowledge, potentially leading to suboptimal states. To counter this, we employ Bidirectional LSTM (BiLSTM), enhancing sequence processing models. Furthermore, Bayesian optimization aids in fine-tuning the BiLSTM model architecture. Through fivefold cross-validation on training and testing datasets, our model showcases a classification accuracy of 97.5% on the tested dataset. Moreover, edge computing offers real-time processing, reduced latency, enhanced privacy, bandwidth efficiency, offline capabilities, energy efficiency, personalization, and scalability. Extensive experimental results validate that our proposed approach surpasses state-of-the-art methodologies in recognizing human activities and monitoring health based on smartphone accelerometer data.
Topics: Humans; Smartphone; Accelerometry; Students; Exercise; Deep Learning; Algorithms; Monitoring, Physiologic
PubMed: 38890409
DOI: 10.1038/s41598-024-63934-8 -
International Journal of Medical... Jun 2024The Communication and Tracing App HIV (COMTRAC-HIV) project is developing a mobile health (mHealth) app for integrated care of HIV patients in Germany. The complexity of...
BACKGROUND
The Communication and Tracing App HIV (COMTRAC-HIV) project is developing a mobile health (mHealth) app for integrated care of HIV patients in Germany. The complexity of HIV treatment and continuous care necessitates the need for tailored mHealth solutions. This qualitative study explores design solutions and a prototype to enhance the app's functionality and effectiveness.
METHODS
A total of eight HIV patients and pre-exposure prophylaxis (PrEP) users, recruited at the HIV Center Frankfurt, participated in focus groups and thinking-aloud tests (TA test). In the focus groups, design solutions were discussed for user-interface clarity, leading to the development of an interactive prototype, the usability of which was evaluated with a TA test. Data collection involved video/audio recordings. Qualitative analysis was conducted using a deductive category system, and focused on app design and usage in focus groups, and layout, navigation, interaction, terminology, comprehension, feedback, and level of satisfaction in TA tests.
RESULTS
The app was commended for its simple, clear design, especially its medication reminders and health tracking features. Opinions on the symptom diary varied however, respondents noting it more suitable for HIV users than PrEP users. Privacy concerns suggest avoiding display of HIV-specific information. Suggested improvements include e.g. image uploads, drug interaction checks and prescription tracking. A total of 25 usability issues were identified in the TA test, with most found in the layout (n = 6), navigation (n = 5), interaction (n = 5), and terminology (n = 5) categories. Two examples are non-intuitive controls and illogical button placement. Despite these disadvantages, participants noted positive impressions (n = 5) in the satisfaction category.
CONCLUSION
The study emphasizes the need for patient-centered design in mobile HIV care solutions, highlighting to the app's user-friendliness and potential to enhance care. Further research is necessary to refine the app's functionality and to align it with clinical and patients' privacy needs.
PubMed: 38889535
DOI: 10.1016/j.ijmedinf.2024.105524 -
PLOS Global Public Health 2024Men in sub-Saharan Africa are less likely to accept HIV testing and link to HIV care than women. We conducted a trial to investigate the impact of conditional financial...
Men in sub-Saharan Africa are less likely to accept HIV testing and link to HIV care than women. We conducted a trial to investigate the impact of conditional financial incentives and a decision support application, called EPIC-HIV, on HIV testing and linkage to care. We report the findings of the trial process evaluation to explore whether the interventions were delivered as intended, identify mechanisms of impact and any contextual factors that may have impacted the trial outcomes. Between August 2018 and March 2019, we conducted in-depth interviews and focus group discussions with trial participants (n = 31) and staff (n = 14) to examine views on the implementation process, participant responses to the interventions and the external factors that may have impacted the implementation and outcomes of the study. Interviews were audio-recorded, transcribed, and translated where necessary, and thematically analyzed using ATLAS-ti and NVivo. Both interventions were perceived to be acceptable and useful by participants and implementers. EPIC-HIV proved challenging to implement as intended because it was difficult to ensure consistent use of earphones, and maintenance of privacy. Some participants struggled to navigate the EPIC-HIV app independently and select stories that appealed to them without support. Some participants stopped exploring the app before the end, resulting in an incomplete use of EPIC-HIV. While the financial incentive was implemented as intended, there were challenges with eligibility. The convenience and privacy of home testing influenced the uptake of HIV testing. Contextual barriers including fear of HIV stigma and disclosure if diagnosed with HIV, and expectations of poor treatment in clinics may have inhibited linkage to care. Financial incentives were relatively straightforward to implement and increased uptake of home-based rapid HIV testing but were not sufficient as a 'stand-alone' intervention. Barriers like fear of stigma should be addressed to facilitate linkage to care.
PubMed: 38889120
DOI: 10.1371/journal.pgph.0003364 -
Journal of Medical Internet Research Jun 2024Technological advances in robotics, artificial intelligence, cognitive algorithms, and internet-based coaches have contributed to the development of devices capable of... (Review)
Review
BACKGROUND
Technological advances in robotics, artificial intelligence, cognitive algorithms, and internet-based coaches have contributed to the development of devices capable of responding to some of the challenges resulting from demographic aging. Numerous studies have explored the use of robotic coaching solutions (RCSs) for supporting healthy behaviors in older adults and have shown their benefits regarding the quality of life and functional independence of older adults at home. However, the use of RCSs by individuals who are potentially vulnerable raises many ethical questions. Establishing an ethical framework to guide the development, use, and evaluation practices regarding RCSs for older adults seems highly pertinent.
OBJECTIVE
The objective of this paper was to highlight the ethical issues related to the use of RCSs for health care purposes among older adults and draft recommendations for researchers and health care professionals interested in using RCSs for older adults.
METHODS
We conducted a narrative review of the literature to identify publications including an analysis of the ethical dimension and recommendations regarding the use of RCSs for older adults. We used a qualitative analysis methodology inspired by a Health Technology Assessment model. We included all article types such as theoretical papers, research studies, and reviews dealing with ethical issues or recommendations for the implementation of these RCSs in a general population, particularly among older adults, in the health care sector and published after 2011 in either English or French. The review was performed between August and December 2021 using the PubMed, CINAHL, Embase, Scopus, Web of Science, IEEE Explore, SpringerLink, and PsycINFO databases. Selected publications were analyzed using the European Network of Health Technology Assessment Core Model (version 3.0) around 5 ethical topics: benefit-harm balance, autonomy, privacy, justice and equity, and legislation.
RESULTS
In the 25 publications analyzed, the most cited ethical concerns were the risk of accidents, lack of reliability, loss of control, risk of deception, risk of social isolation, data confidentiality, and liability in case of safety problems. Recommendations included collecting the opinion of target users, collecting their consent, and training professionals in the use of RCSs. Proper data management, anonymization, and encryption appeared to be essential to protect RCS users' personal data.
CONCLUSIONS
Our analysis supports the interest in using RCSs for older adults because of their potential contribution to individuals' quality of life and well-being. This analysis highlights many ethical issues linked to the use of RCSs for health-related goals. Future studies should consider the organizational consequences of the implementation of RCSs and the influence of cultural and socioeconomic specificities of the context of experimentation. We suggest implementing a scalable ethical and regulatory framework to accompany the development and implementation of RCSs for various aspects related to the technology, individual, or legal aspects.
Topics: Humans; Aged; Robotics; Mentoring; Quality of Life
PubMed: 38888953
DOI: 10.2196/48126