Search Results (70)

In the context of global demographic aging, falls among the elderly remain a major public health concern, often leading to injury, hospitalization, and loss of autonomy. This study proposes a real-time fall detection system that combines a modern computer vision model, YOLOv11 with integrated pose estimation, and an Artificial Intelligence (AI)-based voice assistant designed to reduce false alarms and improve intervention efficiency and reliability. The system continuously monitors human posture via video input, detects fall events based on body dynamics and keypoint analysis, and initiates a voice-based interaction to assess the user’s condition. Depending on the user’s verbal response or the absence thereof, the system determines whether to trigger an emergency alert to caregivers or family members. All processing, including speech recognition and response generation, is performed locally to preserve user privacy and ensure low-latency performance. The approach is designed to support independent living for older adults. Evaluation of 200 simulated video sequences acquired by the development team demonstrated high precision and recall, along with a decrease in false positives when incorporating voice-based confirmation. In addition, the system was also evaluated on an external dataset to assess its robustness. Our results highlight the system’s reliability and scalability for real-world in-home elderly monitoring applications. Full article

The challenges and increasing number of elderly individuals requiring remote monitoring at home highlight the need for technological innovations. This study devised an eHealth mobile application designed to detect abnormal movement behavior and alert caregivers when a lack of movement is detected for an abnormal period. By utilizing the built-in accelerometer of a conventional mobile phone, an application was developed to accurately record movement patterns and identify active and idle states. Fuzzy logic, an artificial intelligence (AI)-inspired paradigm particularly effective for real-time reasoning under uncertainty, was integrated to analyze activity data and generate timely alerts, ensuring rapid response in emergencies. The approach reduced development costs while leveraging the widespread familiarity with mobile phones, facilitating easy adoption. The approach involved collecting real-time accelerometry data, analyzing movement patterns using fuzzy logic-based inferencing, and implementing a rule-based decision system to classify user activity and detect inactivity. This pilot study primarily validated the devised fuzzy logic method and the functional prototype of the mobile application, demonstrating its potential to leverage universal smartphone accelerometers for accessible remote monitoring. Using fuzzy logic, temporal and behavioral symmetry in movement patterns were adapted to detect asymmetric anomalies, e.g., abnormal inactivity or falls. The study is particularly relevant considering lonely individuals found deceased in their homes long after dying. By providing real-time monitoring and proactive alerts, this eHealth solution offers a scalable, cost-effective approach to improving elderly care, enhancing safety, and reducing the risk of unnoticed deaths through fuzzy logic. Full article

Despite significant progress in fall detection systems, many of the proposed algorithms remain difficult to implement in real-world applications. A common limitation is the lack of location awareness, especially in outdoor scenarios where accurately determining the fall location is crucial for a timely emergency response. Moreover, the complexity of many existing algorithms poses a challenge for deployment on edge devices, such as wearable systems, which are constrained by limited computational resources and battery life. As a result, these solutions are often impractical for long-term, continuous use in practical settings. To address the aforementioned issues, we developed a portable, wearable device that integrates a microcontroller (MCU), an inertial sensor, and a chip module featuring Global Positioning System (GPS) and Narrowband Internet of Things (NB-IoT) technologies. A low-complexity algorithm based on a finite-state machine was employed to detect fall events, enabling the module to meet the requirements for long-term outdoor use. The proposed algorithm is capable of filtering out eight types of daily activities—running, walking, sitting, ascending stairs, descending stairs, stepping, jumping, and rapid sitting—while detecting four types of falls: forward, backward, left, and right. In case a fall event is detected, the device immediately transmits a fall alert and GPS coordinates to a designated server via NB-IoT. The server then forwards the alert to a specified communication application. Experimental tests demonstrated the system’s effectiveness in outdoor environments. A total of 6750 samples were collected from fifteen test participants, including 6000 daily activity samples and 750 fall events. The system achieved an average sensitivity of 97.9%, an average specificity of 99.9%, and an overall accuracy of 99.7%. The implementation of this system provides enhanced safety assurance for elderly individuals during outdoor activities. Full article

Progressive global aging has increased the number of elderly individuals living alone. The consequent rise in fall accidents has worsened physical injuries, reduced the quality of life, and increased medical expenses. Existing wearable fall-detection devices may cause discomfort, and camera-based systems raise privacy concerns. Here, we propose a non-contact fall-detection system that integrates 4D imaging radar sensors with artificial intelligence (AI) technology to detect falls through real-time monitoring and visualization using a web-based dashboard and Unity engine-based avatar, along with immediate alerts. The system eliminates the need for uncomfortable wearable devices and mitigates the privacy issues associated with cameras. The radar sensors generate Point Cloud data (the spatial coordinates, velocity, Doppler power, and time), which allow analysis of the body position and movement. A CNN model classifies postures into standing, sitting, and lying, while changes in the speed and position distinguish falling actions from lying-down actions. The Point Cloud data were normalized and organized using zero padding and k-means clustering to improve the learning efficiency. The model achieved 98.66% accuracy in posture classification and 95% in fall detection. This study demonstrates the effectiveness of the proposed fall detection approach and suggests future directions in multi-sensor integration for indoor applications. Full article

The paper presents a study of an intelligent system for personnel positioning, transport, and equipment monitoring in the mining industry using convolutional neural network (CNN) and OpenPose technology. The proposed framework operates through a three-stage pipeline: OpenPose-based skeleton extraction from surveillance video streams, capturing 18 key body joints at 30fps; multimodal feature fusion, combining skeletal key points and proximity sensor data to achieve environmental context awareness and obtain relevant feature values; and hierarchical pose alert, using attention-enhanced bidirectional LSTM (trained on 5000 annotated fall instances) for fall warning. The experiment conducted demonstrated that the combined use of the aforementioned technologies allows the system to determine the location and behavior of personnel, calculate the distance to hazardous areas in real time, and analyze personnel postures to identify possible risks such as falls or immobility. The system’s capacity to track the location of vehicles and equipment enhances operational efficiency, thereby mitigating the risk of accidents. Additionally, the system provides real-time alerts, identifying abnormal behavior, equipment malfunctions, and safety hazards, thus promoting enhanced mine management efficiency, improved safe working conditions, and a reduction in accidents. Full article

Background: Potentially inappropriate medications (PIMs) and potential prescription omissions (PPOs) have been widely explored, but few studies focused on patients aged 75 years and over. This study was planned to explore the demographic and clinical characteristics of the older patients admitted to Units for Integrated Continuous Care, and to assess the prevalence and potential predictors of PIMs and PPOs. Methods: An observational, retrospective, and multicenter study was performed on 135 patients aged 75 years or older (i.e., 75–84 years and ≥85 years). PIMs and PPOs were investigated by applying the Screening Tool of Older People’s Prescriptions (STOPP) and Screening Tool to Alert to Right Treatment (START) criteria. Results: The oldest-old patients (≥85 years) were less likely to come from a hospital, had fewer daily medications and a lower number of oral doses, but they presented a higher Charlson Comorbidity Index, were more dependent on activities of daily living, and were less obese than those aged 75–84 years. Results showed a high prevalence of PIMs and PPOs in both age groups. The more common PIMs and PPOs were the same in both age groups. The oldest-old patients who suffered falls were more likely to have a prescription omission of vitamin D supplements. The PIM index was not significantly different between age groups but was higher in the oldest-old group. Conclusions: Patients with a higher number of prescriptions had a higher risk of PIMs. Regarding PPOs, male gender and fall risk were predictors in the youngest group, while the number of comorbidities was significantly associated with PPOs in the oldest group. This study supports the usefulness of the STOPP/START criteria to identify PIMs and PPOs in these patients, but more research is required to determine the potential adverse outcomes of PIMs and PPOs and their clinical and economic consequences. Full article

Background: Vestibular hypofunction occurs in 29.5% of older adults with benign paroxysmal positional vertigo (BPPV), but its impact on postural control, well-being and frailty was not studied before. This study compared the well-being, frailty and postural control between older adults with BPPV and vestibular hypofunction (oaBPPV+), and older adults with only BPPV (oaBPPV). Methods: Thirty-one older adults (≥65 years old) diagnosed with BPPV were recruited. Unilateral vestibular hypofunction was defined as a >25% caloric asymmetry, and bilateral vestibular hypofunction as a total response <6°/s per ear, using bithermal caloric irrigations. The oaBPPV+ group was compared to the oaBPPV group using the measures of well-being (Dizziness Handicap Inventory, Falls Efficacy Scale and 15-item Geriatric Depression Scale), frailty (Modified Fried Criteria), and postural control (timed chair stand test, mini-Balance Evaluation Systems test and Clinical Test of Sensory Interaction on Balance (CTSIB)). Falls and the number of repositioning maneuvers were documented. Significance level was set at α = 0.05. Results: Unilateral vestibular hypofunction was present in 32% of participants, mainly in females (p = 0.04). Bilateral vestibular hypofunction was not found. The oaBPPV+ group (n = 10, mean age 72.5 (4.5)) experienced more comorbidities (p = 0.02) than the oaBPPV group (n = 21, mean age 72.6 (4.9)). Groups did not differ regarding dizziness symptoms (p = 0.46), fear of falling (p = 0.44), depression (p = 0.48), falls (p = 0.08) or frailty (p = 0.36). However, the oaBPPV+ group showed significantly worse postural control under vestibular-dependent conditions (p < 0.001). Conclusions: Despite equally impaired well-being and frailty, the oaBPPV+ group showed greater sensory orientation deficits. Clinicians and researchers should be alert for co-existing vestibular hypofunction in older adults with BPPV, since this may exacerbate their already impaired postural control more than only BPPV. Full article

The aging of society is a global concern nowadays. Falls and fall-related injuries can influence the elderly’s daily living, including physical damage, psychological effects, and financial problems. A reliable fall detection system can trigger an alert immediately when a fall event happens to reduce the adverse effects of falls. Notably, the wrist-based fall detection system provides the most acceptable placement for the elderly; however, the performance is the worst due to the complicated hand movement modeling. Many works recently implemented deep learning technology on wrist-based fall detection systems to address the worst, but class imbalance and data scarcity issues occur. In this study, we analyze different data augmentation methodologies to enhance the performance of wrist-based fall detection systems using deep learning technology. Based on the results, the conditional diffusion model is an ideal data augmentation approach, which improves the F1 score by 6.58% when trained with only 25% of the actual data, and the synthetic data maintains a high quality. Full article

According to South Korea’s Ministry of Employment and Labor, approximately 25,000 construction workers suffered from various injuries between 2015 and 2019. Additionally, about 500 fatalities occur annually, and multiple studies are being conducted to prevent these accidents and quickly identify their occurrence to secure the golden time for the injured. Recently, AI-based video analysis systems for detecting safety accidents have been introduced. However, these systems are limited to areas where CCTV is installed, and in locations like construction sites, numerous blind spots exist due to the limitations of CCTV coverage. To address this issue, there is active research on the use of MEMS (micro-electromechanical systems) sensors to detect abnormal conditions in workers. In particular, methods such as using accelerometers and gyroscopes within MEMS sensors to acquire data based on workers’ angles, utilizing three-axis accelerometers and barometric pressure sensors to improve the accuracy of fall detection systems, and measuring the wearer’s gait using the x-, y-, and z-axis data from accelerometers and gyroscopes are being studied. However, most methods involve use of MEMS sensors embedded in smartphones, typically attaching the sensors to one or two specific body parts. Therefore, in this study, we developed a novel miniaturized IMU (inertial measurement unit) sensor that can be simultaneously attached to multiple body parts of construction workers (head, body, hands, and legs). The sensor integrates accelerometers, gyroscopes, and barometric pressure sensors to measure various worker movements in real time (e.g., walking, jumping, standing, and working at heights). Additionally, incorporating PPG (photoplethysmography), body temperature, and acoustic sensors, enables the comprehensive observation of both physiological signals and environmental changes. The collected sensor data are preprocessed using Kalman and extended Kalman filters, among others, and an algorithm was proposed to evaluate workers’ safety status and update health-related data in real time. Experimental results demonstrated that the proposed IMU sensor can classify work activities with over 90% accuracy even at a low sampling rate of 15 Hz. Furthermore, by integrating internal filtering, communication modules, and server connectivity within an application, we established a cyber–physical system (CPS), enabling real-time monitoring and immediate alert transmission to safety managers. Through this approach, we verified improved performance in terms of miniaturization, measurement accuracy, and server integration compared to existing commercial sensors. Full article

In this study, we build an empirical model to estimate pigments in the South American Lake Villarrica. We use data from Dirección General de Aguas de Chile during the period of 1989–2024 to analyze the behavior of limnological parameters and trophic condition in the lake. Four seasonal linear regression models were developed by us, using a set of water quality variables that explain the values of phycocyanin pigment in Lake Villarrica. In the first case, we related chlorophyll-a (Chl-a) to phycocyanin, expecting to find a direct relationship between both variables, but this was not fulfilled for all seasons of the year. In the second case, in addition to Chl-a, we included water temperature, since this parameter has a great influence on the algal photosynthesis process, and we obtained better results. We discovered a typical seasonal variability given by temperature fluctuations in Lake Villarrica, where in the spring, summer, and autumn seasons, conditions are favorable for algal blooms, while in winter, the natural seasonal conditions do not allow increases in algal productivity. For a third case, we included the turbidity variable along with the variables mentioned above and the statistical performance metrics of the models improved significantly, obtaining R² values of up to 0.90 in the case of the model for the fall season and a mean squared error (MSE) of 0.04 µg/L. In the last case used, we added the variable dissolved organic matter (MOD), and the models showed a slight improvement in their performance. These models may be applicable to other lakes with harmful algal blooms in order to alert the community to the potential toxicity of these events. Full article

This study aims to enhance elderly fall detection systems by using the YOLO (You Only Look Once) object detection algorithm with pose estimation, improving both accuracy and efficiency. Utilizing YOLOv7-W6-Pose’s robust real-time object detection and pose estimation capabilities, the proposed system can effectively identify falls in video feeds by using a webcam and process them in real-time on a high-performance computer equipped with a GPU to accelerate object detection and pose estimation algorithms. YOLO’s single-stage detection mechanism enables quick processing and analysis of video frames, while pose estimation refines this process by analyzing body positions and movements to accurately distinguish falls from other activities. Initial validation was conducted using several free videos sourced online, depicting various types of falls. To ensure real-time applicability, additional tests were conducted with videos recorded live using a webcam, simulating dynamic and unpredictable conditions. The experimental results demonstrate significant advancements in detection accuracy and robustness compared to traditional methods. Furthermore, the approach ensures data privacy by processing only skeletal points derived from pose estimation, with no personal data stored. This approach, integrated into the NeuroPredict platform developed by our team, advances fall detection technology, supporting better care and safety for older adults. Full article

Falls pose a significant threat to the elderly population, often resulting in severe health complications such as fractures and other adverse outcomes, which can drastically lower their quality of life. The early detection of fall risks is crucial in mitigating the impact of such events. Various technologies have been developed to address this issue, including alert systems that notify users of imminent risks due to environmental factors or physiological changes. However, accurately detecting and distinguishing between normal activities, imminent fall risks, and actual falls remains a challenge. This study proposes a machine learning approach using the XGBoost algorithm to improve the fall detection accuracy among the elderly. A dataset comprising 2039 samples of data on the proximity to objects, spatial location changes, heart rate, blood oxygen saturation (SpO₂), blood sugar levels, and pressure applied by the user, categorized into normal, imminent fall risk, and fall classes, was utilized to train and test the model. The model was trained on 70% of the data, with 30% allocated for testing. Hyperparameter optimization was performed using a randomized search with cross-validation. Previous studies have reported an accuracy of 0.9667 for the same dataset. In contrast, this study achieved an accuracy of 1.0, demonstrating a significant improvement in the overall performance compared to earlier work. The confusion matrix demonstrates the model’s ability to distinguish between all three classes with no false positives. Additionally, sensitivity tests were conducted by varying the training sample sizes and randomizing the data splits, confirming the model’s robustness in different conditions. These results show that the proposed method was able to correctly sort all the samples in the training and tests, outperforming previous studies in detecting fall-related events, reducing the likelihood of false alarms, and enhancing resource allocation for elderly care. Full article

The rapid adoption of Unmanned Aerial Vehicles (UAVs) in the construction industry has revolutionized safety, surveying, quality monitoring, and maintenance assessment. UAVs are increasingly used to prevent accidents caused by falls from heights or being struck by falling objects by ensuring workers comply with safety protocols. This study focuses on leveraging UAV technology to enhance labor safety by monitoring the use of personal protective equipment, particularly helmets, among construction workers. The developed UAV system utilizes the tensorflow technique and an alert system to detect and identify workers not wearing helmets. Employing the high-precision, high-speed, and widely applicable Faster R-CNN method, the UAV can accurately detect construction workers with and without helmets in real-time across various site conditions. This proactive approach ensures immediate feedback and intervention, significantly reducing the risk of injuries and fatalities. Additionally, the implementation of UAVs minimizes the workload of site supervisors by automating safety inspections and monitoring, allowing for more efficient and continuous oversight. The experimental results indicate that the UAV system’s high precision, recall, and processing capabilities make it a reliable and cost-effective solution for improving construction site safety. The precision, mAP, and FPS of the developed system with the R-CNN are 93.1%, 58.45%, and 27 FPS. This study demonstrates the potential of UAV technology to enhance safety compliance, protect workers, and improve the overall quality of safety management in the construction industry. Full article

Background/Objectives: Numerous European countries, including Romania, are facing the concern of rapid ageing of their populations. Moreover, Romania’s life expectancy ranks among the lowest in the European Union. In light of this, it is imperative that the assessment of medication-related harm be given national priority in order to secure and enhance pharmacotherapy and the medical act. In this study, we sought to describe and evaluate the under-prescribing practices among the Romanian elderly population. Methods: We conducted a cross-sectional study in urban areas of two counties in Western Romania (Timis and Arad) from November 2017 to February 2019. We collected chronic electronic prescriptions issued for elderly patients (>65 years old) with chronic conditions. The medication was prescribed by generalist or specialist physicians for periods ranging between 30 and 90 days. To assess inappropriate prescribing behaviours, a multidisciplinary team of specialists applied the Screening Tool of Older Persons’ Prescriptions/Screening Tool to Alert to Right Treatment (STOPP/START) v.2 criteria to the collected prescriptions. Results: Within the 1498 prescriptions included in the study, 57% were issued to females, the mean age was 74.1 ± 6.95, and the average number of medicines per prescription was 4.7 ± 1.51. The STOPP criteria most commonly identified were the (1) long treatment duration (23.6%) and (2) prescription of neuroleptics (14.6%) or zopiclone (14.0%) as medications that increase the risk of falls. According to START criteria, the following medicines were under-prescribed: (1) statins (47.4%), (2) beta-blockers (24.5%), (3) antiresorptive therapy (10.0%), and (4) β2-agonists and muscarinic antagonists for chronic obstructive pulmonary disease (COPD) (4.5%). Within our study group, the prevalence of potentially inappropriate medications was 18.58%, whereas the prevalence of potential prescribing omissions was 49.2%. Conclusions: To decrease medication-related harm and morbid-mortality, and to increase the quality of life for elderly people in Romania, immediate actions are needed from national authorities. These actions include reinforcing primary care services, providing periodic training for physicians, implementing medication review services by pharmacists, and utilising electronic health records at their full capacity. Full article

Early attention to individuals who suffer falls is a critical aspect when determining the consequences of such accidents, which are among the leading causes of mortality and disability in older adults. For this reason and considering the high number of older adults living alone, the development of automatic fall alerting systems has garnered significant research attention over the past decade. A key element for deploying a fall detection system (FDS) based on wearables is the wireless transmission method employed to transmit the medical alarms. In this regard, the vast majority of prototypes in the related literature utilize short-range technologies, such as Bluetooth, which must be complemented by the existence of a gateway device (e.g., a smartphone). In other studies, standards like Wi-Fi or 3G communications are proposed, which offer greater range but come with high power consumption, which can be unsuitable for most wearables, and higher service fees. In addition, they require reliable radio coverage, which is not always guaranteed in all application scenarios. An interesting alternative to these standards is Low Power Wide Area Network (LPWAN) technologies, which minimize both energy consumption and hardware costs while maximizing transmission range. This article provides a comprehensive search and review of that works in the literature that have implemented and evaluated wearable FDSs utilizing LPWAN interfaces to transmit alarms. The review systematically examines these proposals, considering various operational aspects and identifying key areas that have not yet been adequately addressed for the viable implementation of such detectors. Full article