This book gathers the Proceedings of the 6th International Conference on Robot Intelligence Technology and Applications (RITA 2018). Reflecting the conference's main theme, "Robotics and Machine Intelligence: Building Blocks for Industry 4.0," it features relevant and current research investigations into various aspects of these building blocks. The areas covered include: Instrumentation and Control, Automation, Autonomous Systems, Biomechatronics and Rehabilitation Engineering, Intelligent Systems, Machine Learning, Robotics, Sensors and Actuators, and Machine Vision, as well as Signal and Image Processing. A valuable asset, the book offers researchers and practitioners a timely overview of the latest advances in robot intelligence technology and its applications.

This book gathers the Proceedings of the 6th International Conference on Robot Intelligence Technology and Applications (RITA 2018). Reflecting the conference's main theme, "Robotics and Machine Intelligence: Building Blocks for Industry 4.0," it features relevant and current research investigations into various aspects of these building blocks. The areas covered include: Instrumentation and Control, Automation, Autonomous Systems, Biomechatronics and Rehabilitation Engineering, Intelligent Systems, Machine Learning, Robotics, Sensors and Actuators, and Machine Vision, as well as Signal and Image Processing. A valuable asset, the book offers researchers and practitioners a timely overview of the latest advances in robot intelligence technology and its applications.

This brief highlights the association of different performance variables that influences archery performance and the employment of different machine learning algorithms in the identification of potential archers. The sport of archery is often associated with a myriad of performance indicators namely bio-physiological, psychological, anthropometric as well as physical fitness. Traditionally, the determination of potential archers is carried out by means of conventional statistical techniques. Nonetheless, such methods often fall short in associating non-linear relationships between the variables. This book explores the notion of machine learning that is capable of mitigating the aforesaid issue. This book is valuable for coaches and managers in identifying potential archers during talent identification programs.

This short book provides a concise study of the mechanics of head impact in a soccer heading manoeuvre. It describes the development and validation of finite element models of soccer ball and human head, as well as the simulation of brain dynamics after ball-to-head impact. In addition, it also presents a computational study of the efficacy of wearing protective headgear in mitigating the risk of concussion due to heading in soccer.

This book highlights the fundamental association between aquaculture and engineering in classifying fish hunger behaviour by means of machine learning techniques. Understanding the underlying factors that affect fish growth is essential, since they have implications for higher productivity in fish farms. Computer vision and machine learning techniques make it possible to quantify the subjective perception of hunger behaviour and so allow food to be provided as necessary. The book analyses the conceptual framework of motion tracking, feeding schedule and prediction classifiers in order to classify the hunger state, and proposes a system comprising an automated feeder system, image-processing module, as well as machine learning classifiers. Furthermore, the system substitutes conventional, complex modelling techniques with a robust, artificial intelligence approach. The findings presented are of interest to researchers, fish farmers, and aquaculture technologist wanting to gain insights into the productivity of fish and fish behaviour.