According to the World Health Organization (WHO), the population of people over 80 years of age is projected to triple by 2050, due to advances in quality of life, as well as continued progress in medicine and technology. With this context in mind, this article focuses on conducting a comprehensive study on conventional walkers, which represent a fundamental tool to improve the mobility of elderly people. The main objective of this study is to establish the foundations for the design of walkers, with the purpose of identifying the functional and end user requirements under the WalkIES project funded by IEEE’s Industrial Electronics Society (IES). In this initial phase, we seek to define the ergonomic design of a conventional walker, evaluating its advantages and disadvantages, in addition to establishing the standards that these devices must meet. Based on the results obtained, the specific characteristics of the WalkIES walker will be determined. Additionally, international manufacturing standards for walkers are examined and detailed, taking into account the specific features necessary to ensure the effectiveness and safety of these devices for elderly people. Special attention is paid to the individual characteristics of each component that makes up the walkers, with the aim of optimizing their functionality and comfort for the user. In summary, the study of walkers and the establishment of standards are essential to address the mobility needs of the elderly population and those who require assistance to walk, guaranteeing their safety, comfort, and quality of life, as well as to define the requirements of design and manufacturing for robotic walker WalkIES.

In this paper, a rapid field-programmable gate array (FPGA) prototyping of a general quaternion based Artificial Neural Network (qANN) algorithm is investigated. The design method involves the description of the qANN behaviour in C programming language and its hardware implementation, using VITIS High-Level Synthesis (HLS) tool. Simulations and FPGA implementation results and their analysis are presented in terms of performance (data accuracy) vs hardware resources used, and data throughput. The design method proves to be an attractive alternative to hardware description languages based design, opening the field of FPGA acceleration of complex algorithms to a larger audience.

The quaternion-based least mean square (QLMS) algorithm has found more and more applications since its design in 2009. However, its deployment on edge devices still does not meet the real-time constraints nor the complex design methodology requirements (e.g., parallel computations). We address these issues by proposing a reconfigurable hardware architecture that can be rapid field-programmable gate array (FPGA) implemented. For rigor, general formulae to determine the required number of hardware resources prior to implementation are derived. Analyses of the factors affecting the performance of QLMS are provided. Simulations and FPGA implementation reports of the proposed QLMS algorithm applied to real-world quaternion-based 3-D orientation data confirm the superiority of our approach over the original QLMS.

International audience

This paper presents a cross-disciplinary view of industrial electronics for building a sustainable society. After explaining efforts and challenges from human factors, professional education, electronic systems on chip, technology ethics and society, and standards, we suggest a methodology for cross-disciplinary technology integrating the above-mentioned fields, in which the technical committees in Cluster 4, Industrial Electronics Society, play an active part.

The hardware implementation of an artificial neural network (ANN) using field-programmable gate arrays (FPGAs) is a research field that has attracted much interest and attention. With the developments made, the programmer is now forced to face various challenges, such as the need to master various complex hardware-software development platforms, hardware description languages, and advanced ANN knowledge. Moreover, such an implementation is very time consuming. To address these challenges, this paper presents a novel neural design methodology using a holistic modeling approach. Based on the end-user programming concept, the presented solution empowers end users by means of abstracting the low-level hardware functionalities, streamlining the FPGA design process and supporting rapid ANN prototyping. A case study of an ANN as a pattern recognition module of an artificial olfaction system trained to identify four coffee brands is presented. The recognition rate versus training data features and data representation was analyzed extensively.

Urinary system disorders may be prevented by achieving a good body hydration level (BHL). This paper investigated a measurement platform set-up for detecting the shade of the (yellow) colour of urine and its integration with the IoT principles for providing ubiquitous services to the users (citizens). We propose a smart IoT-based holistic system that consists of a urine colour measurement component to be fitted in urinals. Based on RGB colour theory, the system was implemented using a myRio board, which interacts with a mobile application. Bio-samples (urine) gathered from volunteers were tested using this board and with a hand-held refractometer. The results showed that it is possible to infer the BHL by analysing the blue values of RGB colour and by comparison with the recommended urine colour chart. We believe that the proposed system promotes sustainable healthy lifestyle through its ubiquitous “on-the-go” self-test solution and raises BHL awareness in a “greener” and more “personal” way.

The PI algorithm has proven to be a popular and widely used control method, due to its relative simplicity and robustness. Despite this, the linear nature of the algorithm means it doesn't provide optimal control to non-linear systems. This paper presents a novel method of improving the performance of the PI controller using an ANFIS network to provide gain scheduling. This control scheme is applied to a Boost Converter circuit and simulated within the PSIM modelling environment. The simulation results indicate that using the ANFIS controller provides a fast system response with minimal errors even under dynamic operating conditions. The ANFIS controller is also shown to simplify the design flow in comparison to the popular Fuzzy-PI gain scheduling method.

The paper presents a novel neural design methodology based on the End User Programming concept. The proposed solution empowers end users, by means of abstracting the low-level hardware functionalities, to hardware implement Artificial Neural Networks (ANN) using field-programmable gate arrays (FPGA). The main outcomes include rapid ANN design and hardware implementation. A case study of an ANN as a pattern recognition module of an artificial olfaction system trained to identify four coffee brands is presented. An extended analysis has been carried out regarding the recognition rates versus data representation.

Monitoring the human hydration level (BHL) is a key factor in preventing urinary system disorders. The paper proposes a smart holistic system that comprises two components: (1) a urine colour measurement system that can be installed in restrooms, and (2) a mobile app that can be used to interact with the measurement unit, track the user's BHL, view statistical data and advice. The main prototype was built based on low-cost embedded Internet devices coupled with RGB sensors and light sources. Based on the RGB theory, the system was tested in a laboratory, using liquid mixed with food colour of different shades. Results were presented on the mobile app. The system was successfully implemented, as a ubiquitous "on-the-go" self- test solution for raising awareness in a "greener" and "user- friendly" way.

The aim of this paper is to review the state-of-the-art of Field Programmable Gate Array (FPGA) technologies and their contribution to industrial control applications. Authors start by addressing various research fields which can exploit the advantages of FPGAs. The features of these devices are then presented, followed by their corresponding design tools. To illustrate the benefits of using FPGAs in the case of complex control applications, a sensorless motor controller has been treated. This controller is based on the Extended Kalman Filter. Its development has been made according to a dedicated design methodology, which is also discussed. The use of FPGAs to implement artificial intelligence-based industrial controllers is then briefly reviewed. The final section presents two short case studies of Neural Network control systems designs targeting FPGAs.