Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices

The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.

Preface of the 4th International Conference on Electrical, Electronics, Informatics, and Vocational Education (ICE-ELINVO) 2021

ICE-ELINVO 2021 is the fourth international conference on Electrical, Electronics, Informatics, and Vocational Education organized by the Department of Electrical Engineering Education in cooperation with the Department of Electronics and Informatics Education, Faculty of Engineering, Universitas Negeri Yogyakarta. The 4th ICE-ELINVO 2021 was held virtually on October 5th, 2021 due to the Covid-10 pandemic and some of its restrictions issues. The main purpose of this conference is to bring together the professional practices, industrial experts, researchers, vocational teachers, academia, and decision-makers including the government to a unified communication forum for sharing their recent research and developmental activities in Electrical, Electronics, Informatics, Mechatronics, and Vocational Education. This year’s conference theme is the “INNOVATION ON RESEARCH IN ELECTRICAL, ELECTRONICS, INFORMATICS, MECHATRONICS, AND VOCATIONAL EDUCATION IN SUPPORTING SUSTAINABLE SOCIETIES”. In the new normal era, developments and innovations in technology are more advanced with a variety of sophistication that can facilitate almost all of our work. Industry 4.0 technologies such as mobile internet, automation of knowledge work, the internet of things, cloud technology, advanced robotics, artificial intelligence, embedded systems, and autonomous vehicles, provide new opportunities for humans to support intelligent societies. This conference is initiated to contribute cutting-edge ideas to technology growth.

Organization of walking of the lower-extremity exoskeleton using the control of the supporting foot

Introduction. The development of robotics in many advanced countries has raised various industries to a high level. The demand for robots increases the share of their use in production tasks, mainly in the motor-vehicle and electronics industries. Advanced robotics can increase productivity in many industries by 30%, while reducing labor costs. Automation of technological processes of electronics production has a positive impact on the use of robots. Robots are used in construction, logistics, oil and gas, aerospace, plant engineering and construction, mining, healthcare, etc. The authors consider robots from the point of view of their application in medicine for the rehabilitation of musculoskeletal patients. This paper describes the mechanisms for controlling the feet and the center of mass of a humanoid robot.Materials and Methods. The authors chose the simplest algorithm for searching for the law of motion control of a humanoid robot. The robot movement was presented as a reverse pendulum. Using the large kinematic redundancy of walking robots, we have developed a way to control the robot in such a way as to bring the dynamics of its movement to the reverse pendulum as close as possible. At the same time, the problem of determining the generalized coordinates is considered, at which a given position and orientation of the transferred foot and a given position of the projection of the center of mass (CM) of the robot onto the reference surface are provided.Results. The authors have developed a digitalized automatic control scheme for the movement of the feet and the center of mass of the human exoskeleton, which will largely reduce the load on a sick person.Discussion and Conclusions. When discussing the results, comparing the data of the tables obtained during the calculation, the following conclusion was made. The scheme for controlling the feet movement of a human exoskeleton developed by the authors is most effective when designing an automatic scheme for controlling the movement of the feet and the center of mass of a human exoskeleton using digital technology, which will largely reduce the load on a sick person.

Advanced Robotics as a Support in Healthcare Organizational Response. A COVID-19 Pandemic case

The use of robotics is becoming widespread in healthcare. However, little is known about how robotics can affect the relationship with patients in epidemic emergency response or how it impacts clinicians in their organization and work. As a hospital responding to the consequences of the COVID-19 pandemic “ASST dei Sette Laghi” (A7L) in Varese, Italy, had to react quickly to protect its staff from infection while coping with high budgetary pressure as prices of Personal Protection Equipment (PPE) increased rapidly. In response, it introduced six semi-autonomous robots to mediate interactions between staff and patients. Thanks to the cooperation of multiple departments, A7L implemented the solution in less than 10 weeks. It reduced risks to staff and outlay for PPE. However, the characteristics of the robots affected their perception by healthcare staff. This case study reviews critical issues faced by A7L in introducing these devices and recommendations for the path forward.

A Suite of Robotic Solutions for Nuclear Waste Decommissioning

Dealing safely with nuclear waste is an imperative for the nuclear industry. Increasingly, robots are being developed to carry out complex tasks such as perceiving, grasping, cutting, and manipulating waste. Radioactive material can be sorted, and either stored safely or disposed of appropriately, entirely through the actions of remotely controlled robots. Radiological characterisation is also critical during the decommissioning of nuclear facilities. It involves the detection and labelling of radiation levels, waste materials, and contaminants, as well as determining other related parameters (e.g., thermal and chemical), with the data visualised as 3D scene models. This paper overviews work by researchers at the QMUL Centre for Advanced Robotics (ARQ), a partner in the UK EPSRC National Centre for Nuclear Robotics (NCNR), a consortium working on the development of radiation-hardened robots fit to handle nuclear waste. Three areas of nuclear-related research are covered here: human–robot interfaces for remote operations, sensor delivery, and intelligent robotic manipulation.

A Survey on AI-Driven Digital Twins in Industry 4.0: Smart Manufacturing and Advanced Robotics

Digital twin (DT) and artificial intelligence (AI) technologies have grown rapidly in recent years and are considered by both academia and industry to be key enablers for Industry 4.0. As a digital replica of a physical entity, the basis of DT is the infrastructure and data, the core is the algorithm and model, and the application is the software and service. The grounding of DT and AI in industrial sectors is even more dependent on the systematic and in-depth integration of domain-specific expertise. This survey comprehensively reviews over 300 manuscripts on AI-driven DT technologies of Industry 4.0 used over the past five years and summarizes their general developments and the current state of AI-integration in the fields of smart manufacturing and advanced robotics. These cover conventional sophisticated metal machining and industrial automation as well as emerging techniques, such as 3D printing and human–robot interaction/cooperation. Furthermore, advantages of AI-driven DTs in the context of sustainable development are elaborated. Practical challenges and development prospects of AI-driven DTs are discussed with a respective focus on different levels. A route for AI-integration in multiscale/fidelity DTs with multiscale/fidelity data sources in Industry 4.0 is outlined.

Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists.

Technology-Driven Supply Chain Management with OR Applications in Industrial 4.0 Era

In industrial 4.0 era, many advanced technologies, such as blockchain, advanced robotics, additive manufacturing, the Internet of Things, machine learning, and artificial intelligence, have received considerable attention in industry and academia. In real practice, such technologies have been implemented by firms, and they play an important role in increasing the operational efficiency of the firms as well as the whole supply chain through Operational Research (OR) methods. Thus, it is critical to consider the impacts of advanced technologies on supply chain management with OR applications. This special issue explores new practices and applications of technology-driven supply chain management in Industry 4.0. This editorial note summarizes the discussions on applications of different technologies in supply chains. Moreover, we list important future research directions based on contributed papers.