Industrial, Collaborative and Mobile Robotics in Latin America: Review of Mechatronic Technologies for Advanced Automation

Mechatronics and Robotics (MaR) have recently gained importance in product development and manufacturing settings and applications. Therefore, the Center for Space Emerging Technologies (C-SET) has managed an international multi-disciplinary study to present, historically, the first Latin American general review of industrial, collaborative, and mobile robotics, with the support of North American and European researchers and institutions. The methodology is developed by considering literature extracted from Scopus, Web of Science, and Aerospace Research Central and adding reports written by companies and government organizations. This describes the state-of-the-art of MaR until the year 2023 in the 3 Sub-Regions: North America, Central America, and South America, having achieved important results related to the academy, industry, government, and entrepreneurship; thus, the statistics shown in this manuscript are unique. Also, this article explores the potential for further work and advantages described by robotic companies such as ABB, KUKA, and Mecademic and the use of the Robot Operating System (ROS) in order to promote research, development, and innovation. In addition, the integration with industry 4.0 and digital manufacturing, architecture and construction, aerospace, smart agriculture, artificial intelligence, and computational social science (human-robot interaction) is analyzed to show the promising features of these growing tech areas, considering the improvements to increase production, manufacturing, and education in the Region. Finally, regarding the information presented, Latin America is considered an important location for investments to increase production and product development, taking into account the further proposal for the creation of the LATAM Consortium for Advanced Robotics and Mechatronics, which could support and work on roboethics and education/R+D+I law and regulations in the Region. Doi: 10.28991/ESJ-2023-07-04-025 Full Text: PD

Human motion detection using an earphone type wearable device and its application to health care （イヤホン型ウェアラブルデバイスを用いた身体動作検出とヘルスケアへの応用）

信州大学(Shinshu university)博士（工学）Thesis黒澤　真美. Human motion detection using an earphone type wearable device and its application to health care　（イヤホン型ウェアラブルデバイスを用いた身体動作検出とヘルスケアへの応用）. 信州大学, 2020, 博士論文. 博士（工学）, 甲第720号, 令和02年03月20日授与.doctoral thesi

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Automatic Control and Routing of Marine Vessels

Due to the intensive development of the global economy, many problems are constantly emerging connected to the safety of ships’ motion in the context of increasing marine traffic. These problems seem to be especially significant for the further development of marine transportation services, with the need to considerably increase their efficiency and reliability. One of the most commonly used approaches to ensuring safety and efficiency is the wide implementation of various automated systems for guidance and control, including such popular systems as marine autopilots, dynamic positioning systems, speed control systems, automatic routing installations, etc. This Special Issue focuses on various problems related to the analysis, design, modelling, and operation of the aforementioned systems. It covers such actual problems as tracking control, path following control, ship weather routing, course keeping control, control of autonomous underwater vehicles, ship collision avoidance. These problems are investigated using methods such as neural networks, sliding mode control, genetic algorithms, L2-gain approach, optimal damping concept, fuzzy logic and others. This Special Issue is intended to present and discuss significant contemporary problems in the areas of automatic control and the routing of marine vessels

Wearable Movement Sensors for Rehabilitation: From Technology to Clinical Practice

This Special Issue shows a range of potential opportunities for the application of wearable movement sensors in motor rehabilitation. However, the papers surely do not cover the whole field of physical behavior monitoring in motor rehabilitation. Most studies in this Special Issue focused on the technical validation of wearable sensors and the development of algorithms. Clinical validation studies, studies applying wearable sensors for the monitoring of physical behavior in daily life conditions, and papers about the implementation of wearable sensors in motor rehabilitation are under-represented in this Special Issue. Studies investigating the usability and feasibility of wearable movement sensors in clinical populations were lacking. We encourage researchers to investigate the usability, acceptance, feasibility, reliability, and clinical validity of wearable sensors in clinical populations to facilitate the application of wearable movement sensors in motor rehabilitation

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models