Improved YOLOv5 Network for High-Precision Three-Dimensional Positioning and Attitude Measurement of Container Spreaders in Automated Quayside Cranes.

For automated quayside container cranes, accurate measurement of the three-dimensional positioning and attitude of the container spreader is crucial for the safe and efficient transfer of containers. This paper proposes a high-precision measurement method for the spreader's three-dimensional position and rotational angles based on a single vertically mounted fixed-focus visual camera. Firstly, an image preprocessing method is proposed for complex port environments. The improved YOLOv5 network, enhanced with an attention mechanism, increases the detection accuracy of the spreader's keypoints and the container lock holes. Combined with image morphological processing methods, the three-dimensional position and rotational angle changes of the spreader are measured. Compared to traditional detection methods, the single-camera-based method for three-dimensional positioning and attitude measurement of the spreader employed in this paper achieves higher detection accuracy for spreader keypoints and lock holes in experiments and improves the operational speed of single operations in actual tests, making it a feasible measurement approach.

A Novel AI Approach for Assessing Stress Levels in Patients with Type 2 Diabetes Mellitus Based on the Acquisition of Physiological Parameters Acquired during Daily Life.

Stress is the inherent sensation of being unable to handle demands and occurrences. If not properly managed, stress can develop into a chronic condition, leading to the onset of additional chronic health issues, such as cardiovascular illnesses and diabetes. Various stress meters have been suggested in the past, along with diverse approaches for its estimation. However, in the case of more serious health issues, such as hypertension and diabetes, the results can be significantly improved. This study presents the design and implementation of a distributed wearable-sensor computing platform with multiple channels. The platform aims to estimate the stress levels in diabetes patients by utilizing a fuzzy logic algorithm that is based on the assessment of several physiological indicators. Additionally, a mobile application was created to monitor the users' stress levels and integrate data on their blood pressure and blood glucose levels. To obtain better performance metrics, validation experiments were carried out using a medical database containing data from 128 patients with chronic diabetes, and the initial results are presented in this study.

YOLOX-Ray: An Efficient Attention-Based Single-Staged Object Detector Tailored for Industrial Inspections.

Industrial inspection is crucial for maintaining quality and safety in industrial processes. Deep learning models have recently demonstrated promising results in such tasks. This paper proposes YOLOX-Ray, an efficient new deep learning architecture tailored for industrial inspection. YOLOX-Ray is based on the You Only Look Once (YOLO) object detection algorithms and integrates the SimAM attention mechanism for improved feature extraction in the Feature Pyramid Network (FPN) and Path Aggregation Network (PAN). Moreover, it also employs the Alpha-IoU cost function for enhanced small-scale object detection. YOLOX-Ray's performance was assessed in three case studies: hotspot detection, infrastructure crack detection and corrosion detection. The architecture outperforms all other configurations, achieving mAP50 values of 89%, 99.6% and 87.7%, respectively. For the most challenging metric, mAP50:95, the achieved values were 44.7%, 66.1% and 51.8%, respectively. A comparative analysis demonstrated the importance of combining the SimAM attention mechanism with Alpha-IoU loss function for optimal performance. In conclusion, YOLOX-Ray's ability to detect and to locate multi-scale objects in industrial environments presents new opportunities for effective, efficient and sustainable inspection processes across various industries, revolutionizing the field of industrial inspections.

IoT-Based Systems for Soil Nutrients Assessment in Horticulture.

Soil nutrients assessment has great importance in horticulture. Implementation of an information system for horticulture faces many challenges: (i) great spatial variability within farms (e.g., hilly topography); (ii) different soil properties (e.g., different water holding capacity, different content in sand, sit, clay, and soil organic matter, different pH, and different permeability) for different cultivated plants; (iii) different soil nutrient uptake by different cultivated plants; (iv) small size of monoculture; and (v) great variety of farm components, agroecological zone, and socio-economic factors. Advances in information and communication technologies enable creation of low cost, efficient information systems that would improve resources management and increase productivity and sustainability of horticultural farms. We present an information system based on different sensing capability, Internet of Things, and mobile application for horticultural farms. An overview on different techniques and technologies for soil fertility evaluation is also presented. The results obtained in a botanical garden that simulates the diversity of environment and plant diversity of a horticultural farm are discussed considering the challenges identified in the literature and field research. The study provides a theoretical basis and technical support for the development of technologies that enable horticultural farmers to improve resources management.

Neural Architecture Search for 1D CNNs-Different Approaches Tests and Measurements.

In the field of sensors, in areas such as industrial, clinical, or environment, it is common to find one dimensional (1D) formatted data (e.g., electrocardiogram, temperature, power consumption). A very promising technique for modelling this information is the use of One Dimensional Convolutional Neural Networks (1D CNN), which introduces a new challenge, namely how to define the best architecture for a 1D CNN. This manuscript addresses the concept of One Dimensional Neural Architecture Search (1D NAS), an approach that automates the search for the best combination of Neuronal Networks hyperparameters (model architecture), including both structural and training hyperparameters, for optimising 1D CNNs. This work includes the implementation of search processes for 1D CNN architectures based on five strategies: greedy, random, Bayesian, hyperband, and genetic approaches to perform, collect, and analyse the results obtained by each strategy scenario. For the analysis, we conducted 125 experiments, followed by a thorough evaluation from multiple perspectives, including the best-performing model in terms of accuracy, consistency, variability, total running time, and computational resource consumption. Finally, by presenting the optimised 1D CNN architecture, the results for the manuscript's research question (a real-life clinical case) were provided.

Physiological and Behavior Monitoring Systems for Smart Healthcare Environments: A Review.

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressed.

An Affordable Method for Evaluation of Ataxic Disorders Based on Electrooculography.

Ataxias are a group of neurodegenerative disorders characterized by cerebellar dysfunction that cause irregularities in the rate, rhythm, amplitude, and force of voluntary movements. The electrooculogram (EOG) may provide clues about ataxic disorders because most of these patients have difficulty with visual tracking and fixing their gaze. Using electrodes, EOG records the biopotentials generated by eye movements. In this paper, three surface electrodes are placed around the eye socket, and the biopotentials generated by eye movements are acquired using a commercial bioamplifier device. Next, the signals are sent to the computer to be digitally processed to extract the rate of saccades as well as the delay and deviation of the gaze in response to a stimulus. These features are analysed in a novel software application designed to help physicians in evaluating ataxia. After applying several tests to both healthy and ataxia-affected patients, differences in EOG results were found. The evaluation of the reliability of the designed software application is made according to three metrics: sensitivity, specificity, and accuracy. The results indicate the proposed system's viability as an affordable method for evaluation of ataxic disorders.

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments.

The present work tries to fill part of the gap regarding the pilots' emotions and their bio-reactions during some flight procedures such as, takeoff, climbing, cruising, descent, initial approach, final approach and landing. A sensing architecture and a set of experiments were developed, associating it to several simulated flights ( N f l i g h t s = 13 ) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator ( N p i l o t s = 8 ). It is shown that it is possible to recognize emotions from different pilots in flight, combining their present and previous emotions. The cardiac system based on Heart Rate (HR), Galvanic Skin Response (GSR) and Electroencephalography (EEG), were used to extract emotions, as well as the intensities of emotions detected from the pilot face. We also considered five main emotions: happy, sad, angry, surprise and scared. The emotion recognition is based on Artificial Neural Networks and Deep Learning techniques. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were the main methods used to measure the quality of the regression output models. The tests of the produced output models showed that the lowest recognition errors were reached when all data were considered or when the GSR datasets were omitted from the model training. It also showed that the emotion surprised was the easiest to recognize, having a mean RMSE of 0.13 and mean MAE of 0.01; while the emotion sad was the hardest to recognize, having a mean RMSE of 0.82 and mean MAE of 0.08. When we considered only the higher emotion intensities by time, the most matches accuracies were between 55% and 100%.

Monitoring Walker Assistive Devices: A Novel Approach Based on Load Cells and Optical Distance Measurements.

This paper presents a measurement system intended to monitor the usage of walker assistive devices. The goal is to guide the user in the correct use of the device in order to prevent risky situations and maximize comfort. Two risk indicators are defined: one related to force unbalance and the other related to motor incoordination. Force unbalance is measured by load cells attached to the walker legs, while motor incoordination is estimated by synchronizing force measurements with distance data provided by an optical sensor. The measurement system is equipped with a Bluetooth link that enables local supervision on a computer or tablet. Calibration and experimental results are included in the paper.

Development of a Computer Writing System Based on EOG.

The development of a novel computer writing system based on eye movements is introduced herein. A system of these characteristics requires the consideration of three subsystems: (1) A hardware device for the acquisition and transmission of the signals generated by eye movement to the computer; (2) A software application that allows, among other functions, data processing in order to minimize noise and classify signals; and (3) A graphical interface that allows the user to write text easily on the computer screen using eye movements only. This work analyzes these three subsystems and proposes innovative and low cost solutions for each one of them. This computer writing system was tested with 20 users and its efficiency was compared to a traditional virtual keyboard. The results have shown an important reduction in the time spent on writing, which can be very useful, especially for people with severe motor disorders.

The Association between Childhood Obesity and Cardiovascular Changes in 10 Years Using Special Data Science Analysis.

The increasing prevalence of overweight and obesity is a worldwide problem, with several well-known consequences that might start to develop early in life during childhood. The present research based on data from children that have been followed since birth in a previously established cohort study (Generation XXI, Porto, Portugal), taking advantage of State-of-the-Art (SoA) data science techniques and methods, including Neural Architecture Search (NAS), explainable Artificial Intelligence (XAI), and Deep Learning (DL), aimed to explore the hidden value of data, namely on electrocardiogram (ECG) records performed during follow-up visits. The combination of these techniques allowed us to clarify subtle cardiovascular changes already present at 10 years of age, which are evident from ECG analysis and probably induced by the presence of obesity. The proposed novel combination of new methodologies and techniques is discussed, as well as their applicability in other health domains.

Is motor learning of stroke patients in non-immersive virtual environment influenced by laterality of injury? A preliminary study.

BACKGROUND: Stroke is the leading cause of long-term disability in adults, causing residual sensorimotor deficits in many survivors. Patients may have different impairments according to laterality of injury, as well as different responses to some therapies. OBJECTIVE: This preliminary study sought to investigate motor learning in rehabilitation of stroke patients with non-immersive virtual environment by process (electroencephalography) and product (performance) measures in stroke patients with left and right cerebral hemispheres damage. METHODS: The study included 10 chronic stroke patients; 5 with left brain injury (LI), mean age 48.8 years (±4.76), and 5 with right brain injury (RI), mean age 52 years (±10.93). Patients were evaluated for electroencephalographic activity (alpha and beta frequencies) and performance (absolute error) in a darts game on XBOX Kinect (Microsoft®). Then they underwent a virtual darts game training task, 12 sessions for 4 weeks (acquisition stage). After training, they were revaluated (long-term retention). RESULTS: RI group increased alpha power and decreased beta in ipsilesional areas, increased activation on left hemisphere and decreased the absolute error of performance; LI group increased right hemisphere activation and did not decrease the absolute error. CONCLUSIONS: Patients with right brain injury reduce neural effort and errors after virtual darts training, which did not happen to patients with left brain injury. Therefore, the laterality of lesion should be considered in studies that use virtual reality for stroke rehabilitation.

Fear or Humour in anti-smoking campaigns? Impact on perceived effectiveness and support for tobacco control Policies.

Several anti-smoking campaigns have been used for decades to reduce smoking consumption. However, so far, there is no consensus regarding the effectiveness of inducing distinct emotions in reducing smoke consumption. This study tested the effects of two types of anti-smoking ads, inducing fear or humor, on emotions, perceived effectiveness, support for tobacco control policies, urges to smoke, and susceptibility to smoke. Participants (N = 108; 54 smokers) of both genders were randomly assigned to one of the two following emotion ads condition: fear (N = 52) or humor (N = 56). During exposure, the continuous flow of their emotions by self-report and physiologically was collected. Measures of ads impact on emotions, perceived effectiveness, urges and susceptibility to smoking, and support for tobacco policies were applied after exposure. The results have shown that fear ads were perceived as more effective and reduced the urges to smoke in smokers. Non-smokers were more supportive of tobacco control policies. In conclusion, this study showed that fear campaigns can reduce the urge to smoke among smokers and are perceived to be more effective. This perceived effectiveness can be partially explained by feelings of fear, regardless the other emotions it also triggers, and of the smoking status.

Why Do We Watch? The Role of Emotion Gratifications and Individual Differences in Predicting Rewatchability and Movie Recommendation.

BACKGROUND: The present study's main aim was to determine the predictors of movie rewatchability and recommendations. METHODS: Using a sample of 318 participants, we first tested the structure of a gratification scale from watching a movie. Then, we examined the role of age, need for cognition, need for affect, extraversion, and emotional gratifications, in predicting individuals' interest in rewatching the movie and in making recommendations. RESULTS: As in the original proposal of the emotional gratification scale, the following dimensions were identified: fun, thrill, empathic sadness, release of emotions, social sharing, contemplative experiences, and character engagement, with acceptable model fit and reliability, convergent and divergent validity. Social sharing, contemplate experiences, need for affect and age were significant predictors of movie recommendation; whereas social sharing, thrill, extraversion, and age contributed most to explaining rewatching interest. CONCLUSION: This study highlights the importance of considering distinct gratifications and individual differences in predicting rewatching and movie recommendation.

Insole optical fiber Bragg grating sensors network for dynamic vertical force monitoring.

In an era of unprecedented progress in technology and increase in population age, continuous and close monitoring of elder citizens and patients is becoming more of a necessity than a luxury. Contributing toward this field and enhancing the life quality of elder citizens and patients with disabilities, this work presents the design and implementation of a noninvasive platform and insole fiber Bragg grating sensors network to monitor the vertical ground reaction forces distribution induced in the foot plantar surface during gait and body center of mass displacements. The acquired measurements are a reliable indication of the accuracy and consistency of the proposed solution in monitoring and mapping the vertical forces active on the foot plantar sole, with a sensitivity up to 11.06 ?? pm / N . The acquired measurements can be used to infer the foot structure and health condition, in addition to anomalies related to spine function and other pathologies (e.g., related to diabetes); also its application in rehabilitation robotics field can dramatically reduce the computational burden of exoskeletons' control strategy. The proposed technology has the advantages of optical fiber sensing (robustness, noninvasiveness, accuracy, and electromagnetic insensitivity) to surpass all drawbacks verified in traditionally used sensing systems (fragility, instability, and inconsistent feedback).

Wearable sensor network to study laterality of brain functions.

In the last decade researches on laterality of brain functions have been reinvigorated. New models of lateralization of brain functions were proposed and new methods for understanding mechanisms of asymmetry between right and left brain functions were described. We design a system to study laterality of motor and autonomic nervous system based on wearable sensors network. A mobile application was developed for analysis of upper and lower limbs movements, cardiac and respiratory function. The functionalities and experience gained with deployment of the system are described.

Fear or Humour in anti-smoking campaigns? Impact on perceived effectiveness and support for tobacco control Policies / Medo ou Humor em campanhas antitabágicas? O impacto na perceção de eficácia e apoio a políticas antitabágicas

Abstract Several anti-smoking campaigns have been used for decades to reduce smoking consumption. However, so far, there is no consensus regarding the effectiveness of inducing distinct emotions in reducing smoke consumption. This study tested the effects of two types of anti-smoking ads, inducing fear or humor, on emotions, perceived effectiveness, support for tobacco control policies, urges to smoke, and susceptibility to smoke. Participants (N = 108; 54 smokers) of both genders were randomly assigned to one of the two following emotion ads condition: fear (N = 52) or humor (N = 56). During exposure, the continuous flow of their emotions by self-report and physiologically was collected. Measures of ads impact on emotions, perceived effectiveness, urges and susceptibility to smoking, and support for tobacco policies were applied after exposure. The results have shown that fear ads were perceived as more effective and reduced the urges to smoke in smokers. Non-smokers were more supportive of tobacco control policies. In conclusion, this study showed that fear campaigns can reduce the urge to smoke among smokers and are perceived to be more effective. This perceived effectiveness can be partially explained by feelings of fear, regardless the other emotions it also triggers, and of the smoking status.

Resumo Várias campanhas antitabágicas são usadas para reduzir o consumo de tabaco. No entanto, até ao momento não existe um consenso sobre a eficácia da indução de emoções específicas nestas campanhas. Este estudo testou os efeitos de dois tipos de campanhas antitabágicas, induzindo Medo ou Humor, nas emoções, na perceção de eficácia das campanhas, no apoio a políticas antitabágicas, no desejo de fumar, e na suscetibilidade para fumar. Os participantes (N = 108; 54 fumadores), de ambos os sexos, foram aleatoriamente distribuídos para uma das seguintes campanhas indutoras de emoções: medo (N = 52) ou humor (N = 56). Durante a exposição, registou-se o fluxo contínuo das emoções autorreportadas e as respostas fisiológicas. Após a exposição avaliou-se o impacto das campanhas nas emoções, na perceção de eficácia, nas políticas antitabágicas, no desejo e na suscetibilidade para fumar. Os resultados evidenciaram que as campanhas indutoras de medo foram percecionadas como mais eficazes e reduziram o desejo de fumar em fumadores. Políticas antitabágicas foram mais apoiadas por não fumadores. Futuramente deverá considerar-se que induzir diferentes emoções em campanhas antitabágicas pode ter efeitos distintos a nível afetivo e cognitivo, com possível relevância para a mudança comportamental.

Comparative analysis of two systems for unobtrusive heart signal acquisition and characterization.

In this paper we describe and compared method of heart rate estimation from cardiac signal acquired with EMFIT, FMCW Doppler radar and Finapres based technology, in the same context, and briefly investigated their similarities and differences. Study of processing of acquired cardiac signal for accurate peak detection using Wavelet Transform is also described. The results suggest good reliability of the two implemented unobtrusive systems for heart rate estimation.

Cardio-respiratory and daily activity monitor based on FMCW Doppler radar embedded in a wheelchair.

Unobtrusive monitoring of the cardio-respiratory and daily activity for wheelchair users became nowadays an important challenge, considering population aging phenomena and the increasing of the elderly with chronic diseases that affect their motion capabilities. This work reports the utilization of FMCW (frequency modulated continuous wave) Doppler radar sensors embedded in a manual wheelchair to measure the cardiac and respiratory activities and the physical activity of the wheelchair user. Another radar sensor is included in the system in order to quantify the motor activity through the wheelchair traveled distance, when the user performs the manual operation of the wheelchair. A conditioning circuit including active filters and a microcontroller based primary processing module was designed and implemented to deliver the information through Bluetooth communication protocol to an Android OS tablet computer. The main capabilities of the software developed using Android SDK and Java were the signal processing of Doppler radar measurement channel signals, graphical user interface, data storage and Wi-Fi data synchronization with remote physiological and physical activity database.

New insight into arrhythmia onset using HRV and BPV analysis.

In this paper Heart Rate Variability (HRV) and Blood Pressure Variability (BPV) were analyzed before the onset of cardiac arrhythmia in order to derive markers for short-term forecasting. The (a) coherence between systolic blood pressure (SBP) and cardiac oscillations in low-frequency (LF) and high-frequency (HF) band; (b) fluctuations of phase; (c) HRV and BPV as a LF power and HF power in frequency and time-frequency domain; (d) transfer function analysis of cardiovascular signals were analyzed. Arrhythmia was preceded by: a) lower coherence; b) increase in fluctuations of phase between signals; c) higher spectral energy associated with respiratory frequency in blood pressure signal; d) raise of sympathetic outflow to the heart; e) decreased HRV. Cardiac arrhythmia was characterized mainly by an increase in LF power of blood pressure, cardiac signal and transfer function. During self-termination of arrhythmia a larger increased in total BPV and HRV was recorded. These results suggest that important information about both neuronal cardiovascular control and risk for spontaneous arrhythmia can be provided by combined analysis of frequency, phase, and time-frequency analysis of blood pressure and cardiac oscillation.