Search Results (59)

Optical sensor technologies are reshaping obesity detection by enabling non-invasive, dynamic analysis of biomechanical and morphological biomarkers. This review synthesizes recent advances in three key areas: optical gait analysis, vision-based pose estimation, and depth-sensing voxel modeling. Gait analysis leverages optical sensor arrays and video systems to identify obesity-specific deviations, such as reduced stride length and asymmetric movement patterns. Pose estimation algorithms—including markerless frameworks like OpenPose and MediaPipe—track kinematic patterns indicative of postural imbalance and altered locomotor control. Human voxel modeling reconstructs 3D body composition metrics, such as waist–hip ratio, through infrared-depth sensing, offering precise, contactless anthropometry. Despite their potential, challenges persist in sensor robustness under uncontrolled environments, algorithmic biases in diverse populations, and scalability for widespread deployment in existing health workflows. Emerging solutions such as federated learning and edge computing aim to address these limitations by enabling multimodal data harmonization and portable, real-time analytics. Future priorities involve standardizing validation protocols to ensure reproducibility, optimizing cost-efficacy for scalable deployment, and integrating optical systems with wearable technologies for holistic health monitoring. By shifting obesity diagnostics from static metrics to dynamic, multidimensional profiling, optical sensing paves the way for scalable public health interventions and personalized care strategies. Full article

Hemoglobin plays a critical role in diagnosing various medical conditions, including infections, trauma, hemolytic disorders, and Mediterranean anemia, which is particularly prevalent in Mediterranean populations. Conventional measurement methods require blood sampling and laboratory analysis, which are often time-consuming and impractical during emergency situations with limited medical infrastructure. Although portable oximeters enable non-invasive hemoglobin estimation, they still require physical contact, posing limitations for individuals with circulatory or dermatological conditions. Additionally, reliance on disposable probes increases operational costs. This study presents a non-contact and automated approach for estimating total hemoglobin levels from facial video data using three-dimensional regression models. A dataset was compiled from 279 volunteers, with synchronized acquisition of facial video and hemoglobin values using a commercial pulse oximeter. After preprocessing, the dataset was divided into training, validation, and test subsets. Three 3D convolutional regression models, including 3D CNN, channel attention-enhanced 3D CNN, and residual 3D CNN, were trained, and the most successful model was implemented in a graphical interface. Among these, the residual model achieved the most favorable performance on the test set, yielding an RMSE of 1.06, an MAE of 0.85, and a Pearson correlation coefficient of 0.73. This study offers a novel contribution by enabling contactless hemoglobin estimation from facial video using 3D CNN-based regression techniques. Full article

Background: Traditional neonatal assessments rely on anthropometric measures such as weight, body size, and head circumference. However, recent studies suggest that objective movement quantification may serve as a complementary clinical indicator of development in preterm infants. Methods: This study evaluates non-invasive computer vision-based quantification of neonatal movement using contactless pose tracking based on computer vision. We analyzed approximately 800,000 postural data points from ten preterm infants to identify reliable algorithms, optimal recording duration, and whether whole-body or regional tracking is sufficient. Results: Our findings show that 30 s video segments are adequate for consistent motion quantification. Optical flow methods produced inconsistent results, while distance-based algorithms—particularly Chebyshev and Minkowski—offered greater stability, with coefficients of variation of 5.46% and 6.40% in whole-body analysis. Additionally, Minkowski and Mahalanobis metrics applied to the lower body yielded results similar to full-body tracking, with minimal differences of 0.89% and 1%. Conclusions: The results demonstrate that neonatal movement can be quantified objectively and without physical contact using computer vision techniques and reliable computational methods. This approach may serve as a complementary clinical indicator of neonatal progression, alongside conventional measures such as weight and size, with applications in continuous monitoring and early clinical decision-making for preterm infants. Full article

Artistic masterpieces are mostly collected in museums located in the center of urban areas, which are prone to heavy traffic. Traffic-induced vibrations can represent a significant hazard for museum objects, due to the repeated nature of the excitation and the brittle, pre-damaged condition of the artifacts. This is the case of the Sarcophagus of the Spouses, displayed at the National Etruscan Museum of Villa Giulia in Rome. Vibrations on the floor of the room are measured by means of velocimeters, highlighting substantial vertical amplitudes and recommending the design of an isolation system. For its design, the dynamic identification of the statue is essential, but the use of contact or laser sensors is ruled out. Therefore, a recent technique that magnifies the micromovements present in digital videos is used and the procedure is validated with respect to constructions where the dynamic identification was available in the literature. In the case of the Sarcophagus, identified frequencies are satisfactorily compared with those of a finite element model. The recognition of the dynamic characteristics shows the method’s potential while using inexpensive devices. Because costs for cultural heritage protection are usually very high, this simple and contactless dynamic identification technique represents an important step forward. Full article

We provide new insights into the performance of camera-based heart and respiration rate extraction and evaluate its usability for replacing spot checks conducted in the general ward. A study was performed comprising of 36 ICU patients recorded for a total time of 699 h. The 5 beats/minute agreement between camera and ECG-based heart rate measurements was 81.5%, with a coverage of 81.9%, where the largest gap between measurements was 239 min. The challenges encountered in heart rate monitoring were limited visibility of the patient’s face and irregular heart rates, which led to poor agreement between camera- and ECG-based heart rate measurements. To prevent non-breathing motion from causing error in respiration rate extraction, we developed a metric which was used to detect non-breathing motion. The 3 breaths/minute agreement between the camera- and contact-based respiration rate measurements was 91.1%, with a coverage of 59.1%, where the largest gap between measurements was 114 min. Encountered challenges were the morphology of the respiration signal and irregular breathing. While a few challenges need to be overcome, the results show promise for the usability of camera-based heart and respiration rate monitoring as a replacement for spot checks of these vital parameters conducted in the general ward. Full article

Remote photoplethysmography is a technology that estimates heart rate by detecting changes in blood volume induced by heartbeats and the resulting changes in skin color through imaging. This technique is fundamental for the noncontact acquisition of physiological signals from the human body. Despite the notable progress in remote-photoplethysmography algorithms for estimating heart rate from facial videos, challenges remain in accurately assessing heart rate during cardiovascular exercises such as treadmill or elliptical workouts. To address these issues, research has been conducted in various fields. For example, an understanding of optics can help solve these issues. Careful design of video production is also crucial. Approaches in computer vision and deep learning with neural networks can also be applied. We focused on developing a practical approach to improve heart rate estimation algorithms under constrained conditions. To address the limitations of motion blur during high-motion activities, we introduced a novel motion-based algorithm. While existing methods like CHROM, LGI, OMIT, and POS incorporate correction processes, they have shown limited success in environments with significant motion. By analyzing treadmill data, we identified a relationship between motion changes and heart rate. With an initial heart rate provided, our algorithm achieved over a 15 bpm improvement in mean absolute error and root mean squared error compared to existing methods, along with more than double the Pearson correlation. We hope this research contributes to advancements in healthcare and monitoring. Full article

Fatigue management is critical for high-risk professions such as pilots, firefighters, and healthcare workers, where physical and mental exhaustion can lead to catastrophic accidents and loss of life. Traditional fatigue assessment methods, including surveys and physiological measurements, are limited in real-time monitoring and user convenience. To address these issues, this study introduces a novel contactless fatigue level diagnosis system leveraging multimodal sensor data, including video, thermal imaging, and audio. The system integrates non-contact biometric data collection with an AI-driven classification model capable of diagnosing fatigue levels on a 1 to 5 scale with an average accuracy of 89%. Key features include real-time feedback, adaptive retraining for personalized accuracy improvement, and compatibility with high-stress environments. Experimental results demonstrate that retraining with user feedback enhances classification accuracy by 11 percentage points. The system’s hardware is validated for robustness under diverse operational conditions, including temperature and electromagnetic compliance. This innovation provides a practical solution for improving operational safety and performance in critical sectors by enabling precise, non-invasive, and efficient fatigue monitoring. Full article

Continuous respiration monitoring is an important tool in assessing the patient’s health and diagnosing pulmonary, cardiovascular, and sleep-related breathing disorders. Various techniques and devices, both contact and contactless, can be used to monitor respiration. Each of these techniques can provide different types of information with varying accuracy. Thermal cameras have become a focal point in research due to their contactless nature, affordability, and the type of data they provide, i.e., information on respiration motion and respiration flow. Several studies have demonstrated the feasibility of this technology and developed robust algorithms to extract important information from thermal camera videos. This paper describes the current state-of-the-art in respiration monitoring using thermal cameras, dividing the system into acquiring data, defining and tracking the region of interest, and extracting the breathing signal and respiration rate. The approaches taken to address the various challenges, the limitations of these methods, and possible applications are discussed. Full article

Remote photoplethysmography (rPPG) refers to a non-contact technique that measures heart rate through analyzing the subtle signal changes of facial blood flow captured by video sensors. It is widely used in contactless medical monitoring, remote health management, and activity monitoring, providing a more convenient and non-invasive way to monitor heart health. However, factors such as ambient light variations, facial movements, and differences in light absorption and reflection pose challenges to deep learning-based methods. To solve these difficulties, we put forward a measurement network of heart rate based on multiscale features. In this study, we designed and implemented a dual-branch signal processing framework that combines static and dynamic features, proposing a novel and efficient method for feature fusion, enhancing the robustness and reliability of the signal. Furthermore, we proposed an alternate time-shift module to enhance the model’s temporal depth. To integrate the features extracted at different scales, we utilized a multiscale feature fusion method, enabling the model to accurately capture subtle changes in blood flow. We conducted cross-validation on three public datasets: UBFC-rPPG, PURE, and MMPD. The results demonstrate that MDAR not only ensures fast inference speed but also significantly improves performance. The two main indicators, MAE and MAPE, achieved improvements of at least 30.6% and 30.2%, respectively, surpassing state-of-the-art methods. These conclusions highlight the potential advantages of MDAR for practical applications. Full article

Using drones to obtain vital signs during mass-casualty incidents can be extremely helpful for first responders. Thanks to technological advancements, vital parameters can now be remotely assessed rapidly and robustly. This motivates the development of an automated unmanned aerial system (UAS) for patient triage, combining methods for the automated detection of respiratory-related movements and automatic classification of body movements and body poses with an already published algorithm for drone-based heart rate estimation. A novel UAS-based triage algorithm using UAS-assessed vital parameters is proposed alongside a robust UAS-based respiratory rate assessment and pose classification algorithm. A pilot concept study involving 15 subjects and 30 vital sign measurements under outdoor conditions shows that with our approach, an overall triage classification accuracy of 89% and an $F_1$ score of 0.94 can be achieved, demonstrating its basic feasibility. Full article

Digital photogrammetry has attracted widespread attention in the field of geotechnical and geological surveys due to its low-cost, ease of use, and contactless mode. In this work, with the purpose of studying the progressive block surficial detachments of a landslide, we developed a monitoring system based on fixed multi-view time-lapse cameras. Thanks to a newly developed photogrammetric algorithm based on the comparison of photo sequences through a structural similarity metric and the computation of the disparity map of two convergent views, we can quickly detect the occurrence of collapse events, determine their location, and calculate the collapse volume. With the field data obtained at the Perarolo landslide site (Belluno Province, Italy), we conducted preliminary tests of the effectiveness of the algorithm and its accuracy in the volume calculation. The method of quickly and automatically obtaining the collapse information proposed in this paper can extend the potential of landslide monitoring systems based on videos or photo sequence and it will be of great significance for further research on the link between the frequency of collapse events and the driving factors. Full article

Monitoring human activities, such as walking, falling, and jumping, provides valuable information for personalized health assistants. Existing solutions require the user to carry/wear certain smart devices to capture motion/audio data, use a high-definition camera to record video data, or deploy dedicated devices to collect wireless data. However, none of these solutions are widely adopted for reasons such as discomfort, privacy, and overheads. Therefore, an effective solution to provide non-intrusive, secure, and low-cost human activity monitoring is needed. In this study, we developed a contactless human activity monitoring system that utilizes channel state information (CSI) of the existing ubiquitous WiFi signals. Specifically, we deployed a low-cost commercial off-the-shelf (COTS) router as a transmitter and reused a desktop equipped with an Intel WiFi Link 5300 NIC as a receiver, allowing us to obtain CSI data that recorded human activities. To remove the outliers and ambient noise existing in raw CSI signals, an integrated filter consisting of Hampel, wavelet, and moving average filters was designed. Then, a new metric based on kurtosis and standard deviation was designed to obtain an optimal set of subcarriers that is sensitive to all target activities from the candidate 30 subcarriers. Finally, we selected a group of features, including time- and frequency-domain features, and trained a classification model to recognize different indoor human activities. Our experimental results demonstrate that the proposed system can achieve a mean accuracy of above 93%, even in the face of a long sensing distance. Full article

The development of contactless methods to assess the degree of personal hygiene in elderly people is crucial for detecting frailty and providing early intervention to prevent complete loss of autonomy, cognitive impairment, and hospitalisation. The unobtrusive nature of the technology is essential in the context of maintaining good quality of life. The use of cameras and edge computing with sensors provides a way of monitoring subjects without interrupting their normal routines, and has the advantages of local data processing and improved privacy. This work describes the development an intelligent system that takes the RGB frames of a video as input to classify the occurrence of brushing teeth, washing hands, and fixing hair. No action activity is considered. The RGB frames are first processed by two Mediapipe algorithms to extract body keypoints related to the pose and hands, which represent the features to be classified. The optimal feature extractor results from the most complex Mediapipe pose estimator combined with the most complex hand keypoint regressor, which achieves the best performance even when operating at one frame per second. The final classifier is a Light Gradient Boosting Machine classifier that achieves more than 94% weighted F1-score under conditions of one frame per second and observation times of seven seconds or more. When the observation window is enlarged to ten seconds, the F1-scores for each class oscillate between 94.66% and 96.35%. Full article

Blood oxygen saturation (SpO₂) is an essential physiological parameter for evaluating a person’s health. While conventional SpO₂ measurement devices like pulse oximeters require skin contact, advanced computer vision technology can enable remote SpO₂ monitoring through a regular camera without skin contact. In this paper, we propose novel deep learning models to measure SpO₂ remotely from facial videos and evaluate them using a public benchmark database, VIPL-HR. We utilize a spatial–temporal representation to encode SpO₂ information recorded by conventional RGB cameras and directly pass it into selected convolutional neural networks to predict SpO₂. The best deep learning model achieves 1.274% in mean absolute error and 1.71% in root mean squared error, which exceed the international standard of 4% for an approved pulse oximeter. Our results significantly outperform the conventional analytical Ratio of Ratios model for contactless SpO₂ measurement. Results of sensitivity analyses of the influence of spatial–temporal representation color spaces, subject scenarios, acquisition devices, and SpO₂ ranges on the model performance are reported with explainability analyses to provide more insights for this emerging research field. Full article

The adoption of wireless sensor networks has brought a significant breakthrough in structural health monitoring, providing an effective alternative to the challenges associated with traditional cable-based sensors. In recent years, a growing interest in developing contactless, vision-based vibration sensors like video cameras has led to advancements, potentially alleviating the previously mentioned drawbacks. In this study, a video of a vibrating frame is converted into a set of frames, so that local phase information can be extracted. The motion matrix is then derived from the phase information; since the number of measuring points is usually greater than the number of the excited modes of the system, the problem can become over-determined. Therefore, by applying dimensionality reduction techniques, the dimension of the motion matrix is significantly reduced. Finally, by exploiting an output-only identification technique, modal parameters are computed. The proposed approach is proven to accurately identify the structural frequencies and mode shapes. Full article