Search Results (20)

Sleep position recognition plays a crucial role in diagnosing and managing various health conditions, such as sleep apnea, pressure ulcers, and musculoskeletal disorders. Accurate monitoring of body posture during sleep can provide valuable insights for clinicians and support the development of intelligent healthcare systems. This research presents a comparative analysis of sleep position recognition using two distinct approaches: image-based deep learning and accelerometer-based classification. There are five classes: prone, supine, right side, left side, and wake up. For the image-based method, the Visual Geometry Group 16 (VGG16) convolutional neural network was fine-tuned with data augmentation strategies including rotation, reflection, scaling, and translation to enhance model generalization. The image-based model achieved an overall accuracy of 93.49%, with perfect precision and recall for “right side” and “wakeup” positions, but slightly lower performance for “left side” and “supine” classes. In contrast, the accelerometer-based method employed a feedforward neural network trained on features extracted from segmented accelerometer data, such as signal sum, standard deviation, maximum, and spike count. This method yielded superior performance, reaching an accuracy exceeding 99.8% across most sleep positions. The “wake up” position was particularly easy to detect due to the absence of body movements such as heartbeat or respiration when the person is no longer in bed. The results demonstrate that while image-based models are effective, accelerometer-based classification offers higher precision and robustness, particularly in real-time and privacy-sensitive scenarios. Further comparisons of the system characteristics, data size, and training time are also carried out to offer crucial insights for selecting the appropriate technology in clinical, in-home, or embedded healthcare monitoring applications. Full article

Objectives: Feeding and eating disorders (FED) represent a major public health issue and are the second leading cause of death among psychiatric conditions in children and adolescents. Psychopathological comorbidities play a significant role in the onset and persistence of FED, yet research on their underlying structure remains limited. This study explores the psychiatric comorbidities associated with FED, focusing on common etiopathogenetic factors and their clinical implications. Methods: Data were retrospectively collected from the Italian Regional Center for FED in the Emilia-Romagna Region between June 2023 and April 2024. Diagnoses were assigned following DSM-5 criteria using the Italian version of the semi-structured K-SADS-PL diagnostic interview. Principal component analysis (PCA) was performed to identify latent psychological dimensions underlying FED psychopathology, retaining five components based on the scree plot. Additionally, an analysis of covariance (ANCOVA) was conducted to examine differences in factor scores across FED subtypes, while adjusting for potential confounders. Results: Seventy-two participants were included (mean age: 14.6 years; mean BMI: 18.3 kg/m²; male-to-female ratio: 1:8). Diagnoses were distributed as follows: 63.9% anorexia nervosa (AN), 13.9% other specified feeding and eating disorder (OSFED), 6.9% avoidant restrictive food intake disorder (ARFID), 4.2% binge eating disorder (BED), 4.2% unspecified feeding and eating disorder (UFED), and 2.7% bulimia nervosa (BN). All participants met the criteria for at least one psychiatric comorbidity. Identified psychopathological clusters include the following: (1) mood disorders (66.5%); (2) anxiety disorders (87.5%); (3) obsessive–compulsive and related disorders (47.2%); (4) neurodevelopmental disorders, i.e., attention-deficit/hyperactivity disorder (ADHD) (30.5%); (5) disruptive and impulse-control disorders (13.9%); and (6) psychotic symptoms (40.3%). No instances of tic or elimination disorders were detected. Conduct disorder was more prevalent among UFED, BED, and BN patients compared to other FED (p = 0.005), and moderate/severe ADHD was associated with higher body mass index (BMI) (p = 0.035). PCA revealed distinct psychological dimensions underlying FED, while ANCOVA indicated significant differences in factor scores across FED subtypes, supporting the presence of shared transdiagnostic mechanisms. Conclusions: This study highlights the complex interplay between FED and psychiatric comorbidities, emphasizing the need for early intervention and personalized treatment approaches. The dimensional structure identified through PCA suggests that common psychopathological factors may drive FED development, and ANCOVA findings support their differential expression across FED types. Future research should further investigate these transdiagnostic mechanisms to optimize clinical care. Full article

Background/Objectives: Binge Eating Disorders are severe mental and physical health conditions, closely linked to lifestyle habits. The aims are to describe the prevalence of Binge Eating Disorders and their correlation with nutritional habits and lifestyle factors within the Spanish population. Methods: A descriptive, cross-sectional design was employed. Using non-probabilistic snowball sampling, an electronic survey was released. A total of 22,181 Spanish adults were evaluated, excluding those with any pathology or limitation at the time of survey response that could potentially affect their diet, such as hospitalization or confinement. The validated Nutritional and Social Healthy Habits (NutSo-HH) scale was used to collect data on nutrition, lifestyle, health habits, and socio-demographic variables. Descriptive and inferential statistics were used. Non-parametric tests were applied due to non-normal distribution. Results: Of the 22,181 sample subject (80.8% female), a total number of 260 individuals reported Binge Eating Disorder. The prevalence of Binge Eating Disorder was higher in women than in men (239 vs. 21 respectfully; 91.9%). Individuals with Binge Eating Disorder exhibited poorer nutritional indices (p < 0.001), higher consumption of ultra-processed and fast food (p < 0.001), sugary soft drinks (p = 0.01), and worse sleep quality (p < 0.001). Although time dedicated to physical activity was not different, individuals with Binge Eating Disorder were more sedentary and had lower health status (p = 0.11 for sport practice). Behavioral regulation plays a key role in managing BED, highlighting the need for personalized intervention strategies. Conclusions: Binge Eating Disorders are associated with lifestyle and health habits and worse quality of life. These data can help design public health programs for early detection and effective treatment. Full article

The idea of introducing a robot into an Ambient Assisted Living (AAL) environment to provide additional services beyond those provided by the environment itself has been explored in numerous projects. Moreover, new opportunities can arise from this symbiosis, which usually requires both systems to share the knowledge (and not just the data) they capture from the context. Thus, by using knowledge extracted from the raw data captured by the sensors deployed in the environment, the robot can know where the person is and whether he/she should perform some physical exercise, as well as whether he/she should move a chair away to allow the robot to successfully complete a task. This paper describes the design of an Ambient Assisted Living system where an IoT scheme and robot coexist as independent but connected elements, forming a cyber-physical system-of-systems architecture. The IoT environment includes cameras to monitor the person’s activity and physical position (lying down, sitting…), as well as non-invasive sensors to monitor the person’s heart or breathing rate while lying in bed or sitting in the living room. Although this manuscript focuses on how both systems handle and share the knowledge they possess about the context, a couple of example use cases are included. In the first case, the environment provides the robot with information about the positions of objects in the environment, which allows the robot to augment the metric map it uses to navigate, detecting situations that prevent it from moving to a target. If there is a person nearby, the robot will approach them to ask them to move a chair or open a door. In the second case, even more use is made of the robot’s ability to interact with the person. When the IoT system detects that the person has fallen to the ground, it passes this information to the robot so that it can go to the person, talk to them, and ask for external help if necessary. Full article

This paper works on detecting a person in bed for sleep routine and sleep pattern monitoring based on the Micro-Electro-Mechanical Systems (MEMS) accelerometer and Internet of Things (IoT) embedded system board. This work provides sleep information, patient assessment, and elderly care for patients who live alone via tele-distance to doctors or family members. About 216,000 pieces of acceleration data were collected, including three classes: no person in bed, a static laying position, and a moving state for Artificial Intelligence (AI) application. Six well-known Machine-Learning (ML) algorithms were evaluated with precision, recall, F1-score, and accuracy in the workstation before implementing in the STM32-microcontroller for real-time state classification. The four best algorithms were selected to be programmed into the IoT board and applied for real-time testing. The results demonstrate the high accuracy of the ML performance, more than 99%, and the Classification and Regression Tree algorithm is among the best models with a light code size of 1583 bytes. The smart bed information is sent to the IoT dashboard of Node-RED via a Message Queuing Telemetry broker (MQTT). Full article

In-bed posture classification has attracted considerable research interest and has significant potential to enhance healthcare applications. Recent works generally use approaches based on pressure maps, machine learning algorithms and focused mainly on finding solutions to obtain high accuracy in posture classification. Typically, these solutions use different datasets with varying numbers of sensors and classify the four main postures (supine, prone, left-facing, and right-facing) or, in some cases, include some variants of those main postures. Following this, this article has three main objectives: fine-grained detection of postures of bedridden people, identifying a large number of postures, including small variations—consideration of 28 different postures will help to better identify the actual position of the bedridden person with a higher accuracy. The number of different postures in this approach is considerably higher than the of those used in any other related work; analyze the impact of pressure map resolution on the posture classification accuracy, which has also not been addressed in other studies; and use the PoPu dataset, a dataset that includes pressure maps from 60 participants and 28 different postures. The dataset was analyzed using five distinct ML algorithms (k-nearest neighbors, linear support vector machines, decision tree, random forest, and multi-layer perceptron). This study’s findings show that the used algorithms achieve high accuracy in 4-posture classification (up to 99% in the case of MLP) using the PoPu dataset, with lower accuracies when attempting the finer-grained 28-posture classification approach (up to 68% in the case of random forest). The results indicate that using ML algorithms for finer-grained applications is possible to specify the patient’s exact position to some degree since the parent posture is still accurately classified. Furthermore, reducing the resolution of the pressure maps seems to affect the classifiers only slightly, which suggests that for applications that do not need finer-granularity, a lower resolution might suffice. Full article

In recent years, we have seen a growing wave in the integration of IoT (Internet of Things) technologies into society. This has created new opportunities, but at the same time given rise to several critical issues, creating new challenges that need to be addressed. One of the main challenges is the security and privacy of information that is processed by IoT devices in our daily lives. Users are, most of the time, unaware of IoT devices’ personal information collection and transmission activities that affect their security and privacy. In this work, we propose a solution that aims to increase the privacy and security of data in IoT devices, through a system that controls the IoT device’s communication on the network. This system is based on two basic and simple principles. First, the IoT device manufacturer declares their device’s data collection intentions. Second, the user declares their own preferences of what is permitted to the IoT device. The design of the system includes tools capable of analyzing packets sent by IoT devices and applying network traffic control rules. The objective is to allow the declaration and verification of communication intentions of IoT devices and control the communication of such devices to detect potential security and privacy violations. We have created a test-bed to validate the developed solution, based on virtual machines, and we concluded that our system has little impact on how the overall system performed. Full article

Falls are critical events among the elderly living alone in their rooms and can have intense consequences, such as the elderly person being left to lie for a long time after the fall. Elderly falling is one of the serious healthcare issues that have been investigated by researchers for over a decade, and several techniques and methods have been proposed to detect fall events. To overcome and mitigate elderly fall issues, such as being left to lie for a long time after a fall, this project presents a low-cost, motion-based technique for detecting all events. In this study, we used IRA-E700ST0 pyroelectric infrared sensors (PIR) that are mounted on walls around or near the patient bed in a horizontal field of view to detect regular motions and patient fall events; we used PIR sensors along with Arduino Uno to detect patient falls and save the collected data in Arduino SD for classification. For data collection, 20 persons contributed as patients performing fall events. When a patient or elderly person falls, a signal of different intensity (high) is produced, which certainly differs from the signals generated due to normal motion. A set of parameters was extracted from the signals generated by the PIR sensors during falling and regular motions to build the dataset. When the system detects a fall event and turns on the green signal, an alarm is generated, and a message is sent to inform the family members or caregivers of the individual. Furthermore, we classified the elderly fall event dataset using five machine learning (ML) classifiers, namely: random forest (RF), decision tree (DT), support vector machine (SVM), naïve Bayes (NB), and AdaBoost (AB). Our result reveals that the RF and AB algorithms achieved almost 99% accuracy in elderly fall-d\detection. Full article

Bed is often the personal care unit in hospitals, nursing homes, and individuals’ homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users’ bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents’ on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients’ sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers’ main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users’ personalized sleep-related data. Full article

Background: With the worldwide outbreak of the COVID-19 pandemic, an important question about virus transmission via contaminated surfaces is arising; therefore, research is needed to prove the persistence of viable viruses on surfaces. The purpose of the study was to determine the level of surface contamination with SARS-CoV-2 in a university clinical center. Methods: A study of environmental viral contamination in the rooms of an acute COVID department was performed. Rapid qualitative antigen tests, real-time RT-PCR, and virus isolation in cell cultures were used for virus detection. Results: None of the taken samples were antigen positive. The SARS-CoV-2 RNA was detected in 10% of samples: one positive sample in an empty room after cleaning and disinfection; nine positive samples in occupied rooms. No viable virus was recovered on cell cultures. Conclusions: In our research, the rapid antigen tests did not prove to be effective for environmental samples, but we were able to detect SARS-CoV-2 RNA in 10% of samples using the RT-PCR method. The highest proportion of PCR-positive samples was from unused items in occupied multi-bed rooms. No viable virus was detected, therefore, infection by surface transmission is unlikely, but it remains prudent to maintain strict hand and environmental hygiene and the use of personal protective equipment. Full article

In the context of behavior recognition, the emerging bed-exit monitoring system demands a rapid deployment in the ward to support mobility and personalization. Mobility means the system can be installed and removed as required without construction; personalization indicates human body tracking is limited to the bed region so that only the target is monitored. To satisfy the above-mentioned requirements, the behavior recognition system aims to: (1) operate in a small-size device, typically an embedded system; (2) process a series of images with narrow fields of view (NFV) to detect bed-related behaviors. In general, wide-range images are preferred to obtain a good recognition performance for diverse behaviors, while NFV images are used with abrupt activities and therefore fit single-purpose applications. This paper develops an NFV-based behavior recognition system with low complexity to realize a bed-exit monitoring application on embedded systems. To achieve effectiveness and low complexity, a queueing-based behavior classification is proposed to keep memories of object tracking information and a specific behavior can be identified from continuous object movement. The experimental results show that the developed system can recognize three bed behaviors, namely off bed, on bed and return, for NFV images with accuracy rates of 95~100%. Full article

Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy. Full article

The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector (Origin version, Beijing, China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person’s perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design. Full article

Evidence varies as to how far aerosols spread from individuals infected with SARS-CoV-2 in hospital rooms. We investigated the presence of aerosols containing SARS-CoV-2 inside of dedicated COVID-19 patient rooms. Three National Institute for Occupational Safety and Health BC 251 two-stage cyclone samplers were set up in each patient room for a six-hour sampling period. Samplers were place on tripods, which each held two samplers at various heights above the floor. Extracted samples underwent reverse transcription polymerase chain reaction for selected gene regions of the SARS-CoV-2 virus nucleocapsid. Patient medical data were compared between participants in rooms where virus-containing aerosols were detected and those where they were not. Of 576 aerosols samples collected from 19 different rooms across 32 participants, 3% (19) were positive for SARS-CoV-2, the majority from near the head and foot of the bed. Seven of the positive samples were collected inside a single patient room. No significant differences in participant clinical characteristics were found between patients in rooms with positive and negative aerosol samples. SARS-CoV-2 viral aerosols were detected from the patient rooms of nine participants (28%). These findings provide reassurance that personal protective equipment that was recommended for this virus is appropriate given its spread in hospital rooms. Full article

Recognizing Activities of Daily Living (ADL) or detecting falls in domestic environments require monitoring the movements and positions of a person. Several approaches use wearable devices or cameras, especially for fall detection, but they are considered intrusive by many users. To support such activities in an unobtrusive way, ambient-based solutions are available (e.g., based on PIRs, contact sensors, etc.). In this paper, we focus on the problem of sitting detection exploiting only unobtrusive sensors. In fact, sitting detection can be useful to understand the position of the user in many activities of the daily routines. While identifying sitting/lying on a sofa or bed is reasonably simple with pressure sensors, detecting whether a person is sitting on a chair is an open problem due to the natural chair position volatility. This paper proposes a reliable, not invasive and energetically sustainable system that can be used on chairs already present in the home. In particular, the proposed solution fuses the data of an accelerometer and a capacitive coupling sensor to understand if a person is sitting or not, discriminating the case of objects left on the chair. The results obtained in a real environment setting show an accuracy of 98.6% and a precision of 95%. Full article