Wrist and hand rehabilitation software platform based on leap motion controller

A software platform based on Leap Motion Controller (LMC) movements’ detection was developed. It allows measurements of clinically proved effective hand and finger exercises. The developed software allows representation of amplitude of each different movement, time interval for each movement, frequency of different movements, asymmetry of bilateral movements, standard deviation of signal amplitude, Poincaré plots. A serious game Collect Color Cube, was developed using Unity, C# scrips, and signals from LMC related to movements of the user’s hands and fingers.info:eu-repo/semantics/publishedVersio

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.info:eu-repo/semantics/publishedVersio

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.info:eu-repo/semantics/publishedVersio

High-performance analog front-end (AFE) for EOG systems

Electrooculography is a technique for measuring the corneo-retinal standing potential of the human eye. The resulting signal is called the electrooculogram (EOG). The primary applications are in ophthalmological diagnosis and in recording eye movements to develop simple human–machine interfaces (HCI). The electronic circuits for EOG signal conditioning are well known in the field of electronic instrumentation; however, the specific characteristics of the EOG signal make a careful electronic design necessary. This work is devoted to presenting the most important issues related to the design of an EOG analog front-end (AFE). In this respect, it is essential to analyze the possible sources of noise, interference, and motion artifacts and how to minimize their effects. Considering these issues, the complete design of an AFE for EOG systems is reported in this work.info:eu-repo/semantics/publishedVersio

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.info:eu-repo/semantics/publishedVersio

AI-based smart sensing and AR for gait rehabilitation assessment

Health monitoring is crucial in hospitals and rehabilitation centers. Challenges can affect the reliability and accuracy of health data. Human error, patient compliance concerns, time, money, technology, and environmental factors might cause these issues. In order to improve patient care, healthcare providers must address these challenges. We propose a non-intrusive smart sensing system that uses a SensFloor smart carpet and an inertial measurement unit (IMU) wearable sensor on the user’s back to monitor position and gait characteristics. Furthermore, we implemented machine learning (ML) algorithms to analyze the data collected from the SensFloor and IMU sensors. The system generates real-time data that are stored in the cloud and are accessible to physical therapists and patients. Additionally, the system’s real-time dashboards provide a comprehensive analysis of the user’s gait and balance, enabling personalized training plans with tailored exercises and better rehabilitation outcomes. Using non-invasive smart sensing technology, our proposed solution enables healthcare facilities to monitor patients’ health and enhance their physical rehabilitation plans.info:eu-repo/semantics/publishedVersio

Design of an artificial neural network and feature extraction to identify arrhythmias from ECG

This paper presents a design of an artificial neural network (ANN) and feature extraction methods to identify two types of arrhythmias in datasets obtained through electrocardiography (ECG) signals, namely arrhythmia dataset (AD) and supraventricular arrhythmia dataset (SAD). No special ANN toolkit was used; instead, each neuron and necessary calculus were modeled and individually programmed. Thus, four temporal-based features are used: heart rate (HR), R-peaks root mean square (R-RMS), RR-peaks variance (RR-VAR), and QSR-complex standard deviation (QSR-SD). The network architecture presents four neurons in the input layer, eight in hidden layer and an output layer with two neurons. The proposed classification method uses the MIT-BIH Dataset (Massachusetts Institute of Technology-Beth Israel Hospital) for training, validation and execution or test phases. Preliminary results show the high efficiency of the proposed ANN design and its classification method, reaching accuracies between 98.76% and 98.91%, when in the identification of NSRD and arrhythmic ECG; and accuracies of 86.37% (AD) and 76.35% (SAD), when analyzing only classifications between both arrhythmias.info:eu-repo/semantics/acceptedVersio

Condition monitoring system and faults detection for impedance bonds from railway infrastructure

Nowadays, sensors and condition monitoring systems are expanding rapidly and becoming cheaper. This contributes to increasing developments in condition monitoring in railway transport infrastructure. A condition monitoring system that uses an online device and sensors to acquire electrical parameters from railway infrastructure has been developed and applied for fault detection and diagnosis of impedance bonds. The impedance bond condition is monitored in real-time using current and temperature sensors, providing early warning if predefined thresholds are exceeded in terms of currents, imbalance currents, and temperatures. The proposed method and the developed monitoring device have been validated in the railway laboratory to confirm its capability to detect defects. The acquired parameters from impedance bonds are used to extract thermal stresses and technical conditions of this equipment. Experimental results and appropriate data analysis are included in the article.info:eu-repo/semantics/publishedVersio

UAV GNSS position corrections based on IoT™ communication protocol

With the increase of devices connected to the Internet, currently known as Internet of Things (IoT), it is important to use algorithms in data transmissions to achieve optimal results for a given application.The paper main goal is to gather Global Navigation Satellite System (GNSS) positions at maximum rate possible, process them in a Post-processed Kinematics (PPK) environment, in order to perform GNSS corrections when compared to real earth GNSS coordinates. This method translates as micro-adjustments of the Unmanned Aerial Vehicle (UAV) traveled path, achieving centimeter-level accuracy associated with GNSS actual position. These GNSS positions are collected by the UAV GNSS receiver, and then sent to a gateway by using LoRaWAN™ communications protocol.Experimental results were included in the paper.info:eu-repo/semantics/acceptedVersio

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.João Rala Cordeiro received support from Fundação para a Ciência e Tecnologia (PhD Research Scholarship reference 2020.07443.BD)