Farmers explore the capabilities for applications of Robotic Process Automation (RPA) with image processing, pattern recognition and machine learning, so its logical to ask where best to apply this technology for maximum effect. Machines can do automated tasks better, cheaper and faster. One can use the camera from the sky and measure things on the earth especially on cropland from drones and satellites. Over time, drones have increased in capabilities and fallen in cost, and their use has greatly expanded especially in complex terrain. High quality remote sensing with spectral imaging using drones makes them interesting for regular use in Precision Agriculture (PA). Drones are often used in agriculture in ways that were highly controversial only a short time ago even there are no unified legislation on drones usage in agriculture. In this paper we address problem of Remote Sensing (RS) technologies combined with Unmanned Aerial System (UAS) platforms to support and develop selected agriculture operations like map or sensor-based Variable Rate Application (VRA).

The construction industry is one of the most accident-prone sectors of the branch of industry. It is caused to construction site workers by numbers of danger factors which affect humans health and life. These danger factors may cause negative physical and mental effects by instant or long-term actions. Together with development and evolution of 3D technology in design, construction and use of buildings, new possibilities arise. In this article we will give an attempt to present study on this subject, with a use of exemplary factors and their impact on the environment. Supported by Virtual Reality (VR) simulation existing surroundings and analysis factors which have an influence on people will be presented.

The advancement of Internet of Things (IoT) has made it practical to discover, localize and pinpoint smart sensing devices based on the situational context, relevancy,and characteristics to query data intelligently, or conduct actions. Furthermore, the development of large-scale applications must deal with data collection and data sensing from a massive number of ubiquitous components, ultimately converging into 5G mobile networking. Additionally, IoT involves managing the expectations of Big Data sourced from many heterogeneous sources. This paper provides an overview of biomimetic methodologies, which represent a viable solution for large-scale data delivery through the aggregation of information with large-scale IoT technologies.

Many health professionals do not use correct person transfer techniques in their daily practice. This results in damage to the paraspinal musculature over time, resulting in lower back pain and injuries. In this work, we propose an approach for the accurate multimodal measurement of people lifting and related motion patterns for ergonomic education regarding the application of correct patient transfer techniques. Several examples of person lifting were recorded and processed through accurate instrumentation and the well-defined measurements of kinematics, kinetics, surface electromyography of muscles as well as multicamera video. This resulted in a complete measurement protocol and unique reference datasets of correct and incorrect lifting schemes for caregivers and patients. This understanding of multimodal motion patterns provides insights for further independent investigations.

Gait analysis based on observational methods is commonly used in everyday clinical practice. It is largely subjective and has a variety of limitations. In this publication we describe application of Recurrent Neural Networks (RNN) type Long Short-Term Memory (LSTM) for classification between healthy gait and gait with disease entity. This classification is based on person single step, beginning and ending with right foot strike on ground. The classification was carried out on reference motion database from Human Motion Laboratory (HML) of the Polish-Japanese Academy of Information Technology (PJAIT) containing a large set of samples. LSTM network checked in publication is a part of motion tagging system based on the EXTag concept.