Development of an Intelligent System for the Monitoring and Diagnosis of the Well-Being.

Today, society is more aware of their well-being and health, making wearable devices a new and affordable way to track them continuously. Smartwatches allow access to daily vital physiological measurements, which help people to be aware of their health status. Even though these technologies allow the following of different health conditions, their application in health is still limited to the following physical parameters to allow physicians treatment and diagnosis. This paper presents LM Research, a smart monitoring system mainly composed of a web page, REST APIs, machine learning algorithms, psychological questionnaire, and smartwatches. The system introduces the continuous monitoring of the users' physical and mental indicators to prevent a wellness crisis; the mental indicators and the system's continuous feedback to the user could be, in the future, a tool for medical specialists treating well-being. For this purpose, it collects psychological parameters on smartwatches and mental health data using a psychological questionnaire to develop a supervised machine learning wellness model that predicts the wellness of smartwatch users. The full construction of the database and the technology employed for its development is presented. Moreover, six machine learning algorithms (Decision Tree, Random Forest, Naive Bayes, Neural Networks, Support Vector Machine, and K-nearest neighbor) were applied to the database to test which classifies better the information obtained by the proposed system. In order to integrate this algorithm into LM Research, Random Forest being the one with the higher accuracy of 88%.

Hand Movement Classification Using Burg Reflection Coefficients.

Classification of electromyographic signals has a wide range of applications, from clinical diagnosis of different muscular diseases to biomedical engineering, where their use as input for the control of prosthetic devices has become a hot topic of research. The challenge of classifying these signals relies on the accuracy of the proposed algorithm and the possibility of its implementation in hardware. This paper considers the problem of electromyography signal classification, solved with the proposed signal processing and feature extraction stages, with the focus lying on the signal model and time domain characteristics for better classification accuracy. The proposal considers a simple preprocessing technique that produces signals suitable for feature extraction and the Burg reflection coefficients to form learning and classification patterns. These coefficients yield a competitive classification rate compared to the time domain features used. Sometimes, the feature extraction from electromyographic signals has shown that the procedure can omit less useful traits for machine learning models. Using feature selection algorithms provides a higher classification performance with as few traits as possible. The algorithms achieved a high classification rate up to 100% with low pattern dimensionality, with other kinds of uncorrelated attributes for hand movement identification.

Machine Learning Algorithms Applied to Predict Autism Spectrum Disorder Based on Gut Microbiome Composition.

The application of machine learning (ML) techniques stands as a reliable method for aiding in the diagnosis of complex diseases. Recent studies have related the composition of the gut microbiota to the presence of autism spectrum disorder (ASD), but until now, the results have been mostly contradictory. This work proposes using machine learning to study the gut microbiome composition and its role in the early diagnosis of ASD. We applied support vector machines (SVMs), artificial neural networks (ANNs), and random forest (RF) algorithms to classify subjects as neurotypical (NT) or having ASD, using published data on gut microbiome composition. Naive Bayes, k-nearest neighbors, ensemble learning, logistic regression, linear regression, and decision trees were also trained and validated; however, the ones presented showed the best performance and interpretability. All the ML methods were developed using the SAS Viya software platform. The microbiome's composition was determined using 16S rRNA sequencing technology. The application of ML yielded a classification accuracy as high as 90%, with a sensitivity of 96.97% and specificity reaching 85.29%. In the case of the ANN model, no errors occurred when classifying NT subjects from the first dataset, indicating a significant classification outcome compared to traditional tests and data-based approaches. This approach was repeated with two datasets, one from the USA and the other from China, resulting in similar findings. The main predictors in the obtained models differ between the analyzed datasets. The most important predictors identified from the analyzed datasets are Bacteroides, Lachnospira, Anaerobutyricum, and Ruminococcus torques. Notably, among the predictors in each model, there is the presence of bacteria that are usually considered insignificant in the microbiome's composition due to their low relative abundance. This outcome reinforces the conventional understanding of the microbiome's influence on ASD development, where an imbalance in the composition of the microbiota can lead to disrupted host-microbiota homeostasis. Considering that several previous studies focused on the most abundant genera and neglected smaller (and frequently not statistically significant) microbial communities, the impact of such communities has been poorly analyzed. The ML-based models suggest that more research should focus on these less abundant microbes. A novel hypothesis explains the contradictory results in this field and advocates for more in-depth research to be conducted on variables that may not exhibit statistical significance. The obtained results seem to contribute to an explanation of the contradictory findings regarding ASD and its relation with gut microbiota composition. While some research correlates higher ratios of Bacillota/Bacteroidota, others find the opposite. These discrepancies are closely linked to the minority organisms in the microbiome's composition, which may differ between populations but share similar metabolic functions. Therefore, the ratios of Bacillota/Bacteroidota regarding ASD may not be determinants in the manifestation of ASD.

Trajectory tracking controller of a robotized arm with joint constraints, a direct adaptive gain with state limitations approach.

Motion restrictions in robotic devices may introduce complex requirements for any closed-loop control design, mainly when the robot joints must track reference trajectories that force the end-effector to perform planned motions. This study summarizes the comprehensive technical design of an adaptive state feedback controller for multi-link robotic manipulators that consider the effect of position and velocity restrictions on the tracking trajectory control approach. The proposed design is less conservative than other methods because of the explicit inclusion of state restrictions in the control gain dynamics. A logarithm barrier Lyapunov function class supports the design of the adaptive gain for the manipulator. Sufficient conditions based on a Riccati equation simplify the implementation of the adaptive controller with gains depending on the distance between the current state and the restriction sets. Numerical simulations show the advantages of the proposed controller with adaptive gains concerning a similar adaptive controller that does not consider the restrictions and a proportional-integral-derivative form. An implementation for the motion control of a robotic arm is presented to demonstrate the development by implementing the proposed gain, which confirms the suggested improvements enforced by the proposed controller. The performed comparison shows the advantages of the suggested adaptive gain control form, inducing better tracking of reference trajectories and smaller control energy applications.

Low limb prostheses and complex human prosthetic interaction: A systematic literature review.

A few years ago, powered prostheses triggered new technological advances in diverse areas such as mobility, comfort, and design, which have been essential to improving the quality of life of individuals with lower limb disability. The human body is a complex system involving mental and physical health, meaning a dependant relationship between its organs and lifestyle. The elements used in the design of these prostheses are critical and related to lower limb amputation level, user morphology and human-prosthetic interaction. Hence, several technologies have been employed to accomplish the end user's needs, for example, advanced materials, control systems, electronics, energy management, signal processing, and artificial intelligence. This paper presents a systematic literature review on such technologies, to identify the latest advances, challenges, and opportunities in developing lower limb prostheses with the analysis on the most significant papers. Powered prostheses for walking in different terrains were illustrated and examined, with the kind of movement the device should perform by considering the electronics, automatic control, and energy efficiency. Results show a lack of a specific and generalised structure to be followed by new developments, gaps in energy management and improved smoother patient interaction. Additionally, Human Prosthetic Interaction (HPI) is a term introduced in this paper since no other research has integrated this interaction in communication between the artificial limb and the end-user. The main goal of this paper is to provide, with the found evidence, a set of steps and components to be followed by new researchers and experts looking to improve knowledge in this field.

Nano and Technological Frontiers as a Sustainable Platform for Postharvest Preservation of Berry Fruits.

Berries are highly perishable and susceptible to spoilage, resulting in significant food and economic losses. The use of chemicals in traditional postharvest protection techniques can harm both human health and the environment. Consequently, there is an increasing interest in creating environmentally friendly solutions for postharvest protection. This article discusses various approaches, including the use of "green" chemical compounds such as ozone and peracetic acid, biocontrol agents, physical treatments, and modern technologies such as the use of nanostructures and molecular tools. The potential of these alternatives is evaluated in terms of their effect on microbial growth, nutritional value, and physicochemical and sensorial properties of the berries. Moreover, the development of nanotechnology, molecular biology, and artificial intelligence offers a wide range of opportunities to develop formulations using nanostructures, improving the functionality of the coatings by enhancing their physicochemical and antimicrobial properties and providing protection to bioactive compounds. Some challenges remain for their implementation into the food industry such as scale-up and regulatory policies. However, the use of sustainable postharvest protection methods can help to reduce the negative impacts of chemical treatments and improve the availability of safe and quality berries.

A machine learning approach for hypertension detection based on photoplethysmography and clinical data.

High blood pressure early screening remains a challenge due to the lack of symptoms associated with it. Accordingly, noninvasive methods based on photoplethysmography (PPG) or clinical data analysis and the training of machine learning techniques for hypertension detection have been proposed in the literature. Nevertheless, several challenges arise when analyzing PPG signals, such as the need for high-quality signals for morphological feature extraction from PPG related to high blood pressure. On the other hand, another popular approach is to use deep learning techniques to avoid the feature extraction process. Nonetheless, this method requires high computational power and behaves as a black-box approach, which impedes application in a medical context. In addition, considering only the socio-demographic and clinical data of the subject does not allow constant monitoring. This work proposes to use the wavelet scattering transform as a feature extraction technique to obtain features from PPG data and combine it with clinical data to detect early hypertension stages by applying Early and Late Fusion. This analysis showed that the PPG features derived from the wavelet scattering transform combined with a support vector machine can classify normotension and prehypertension with an accuracy of 71.42% and an F1-score of 76%. However, classifying normotension and prehypertension by considering both the features extracted from PPG signals through wavelet scattering transform and clinical variables such as age, body mass index, and heart rate by either Late Fusion or Early Fusion did not provide better performance than considering each data type separately in terms of accuracy and F1-score.

Lower limb prosthesis: Optimization by lattice and four-bar polycentric knee.

Diabetes has brought several health problems; one of the most common is the amputation of the lower limb, for which the development of low-cost lower limb prostheses has taken on an important role to allow people with these injuries to continue independently with their lives. This paper proposes developing a transfemoral prosthesis for a 47-year-old patient with a weight of 100kg and a height of 1.80m. The approach shows the kinematic model of the four-bar mechanism of the knee, following the Denavith-Hartenberg method, and the calculation of the knee angle curve and the gait with the help of OpenSim. Consequently, it is shown the design of the parts of the prosthesis done in Autodesk Fusion 360 and their optimization by a lattice in Creo software. Finally, the stress simulations in Ansys with the materials previously selected in CES EduPack are presented.

Adaptive identifier for uncertain complex nonlinear systems based on continuous neural networks.

This paper presents the design of a complex-valued differential neural network identifier for uncertain nonlinear systems defined in the complex domain. This design includes the construction of an adaptive algorithm to adjust the parameters included in the identifier. The algorithm is obtained based on a special class of controlled Lyapunov functions. The quality of the identification process is characterized using the practical stability framework. Indeed, the region where the identification error converges is derived by the same Lyapunov method. This zone is defined by the power of uncertainties and perturbations affecting the complex-valued uncertain dynamics. Moreover, this convergence zone is reduced to its lowest possible value using ideas related to the so-called ellipsoid methodology. Two simple but informative numerical examples are developed to show how the identifier proposed in this paper can be used to approximate uncertain nonlinear systems valued in the complex domain.