HUMANISE: Human-Inspired Smart Management, towards a Healthy and Safe Industrial Collaborative Robotics.

The workplace is evolving towards scenarios where humans are acquiring a more active and dynamic role alongside increasingly intelligent machines. Moreover, the active population is ageing and consequently emerging risks could appear due to health disorders of workers, which requires intelligent intervention both for production management and workers' support. In this sense, the innovative and smart systems oriented towards monitoring and regulating workers' well-being will become essential. This work presents HUMANISE, a novel proposal of an intelligent system for risk management, oriented to workers suffering from disease conditions. The developed support system is based on Computer Vision, Machine Learning and Intelligent Agents. Results: The system was applied to a two-arm Cobot scenario during a Learning from Demonstration task for collaborative parts transportation, where risk management is critical. In this environment with a worker suffering from a mental disorder, safety is successfully controlled by means of human/robot coordination, and risk levels are managed through the integration of human/robot behaviour models and worker's models based on the workplace model of the World Health Organization. The results show a promising real-time support tool to coordinate and monitoring these scenarios by integrating workers' health information towards a successful risk management strategy for safe industrial Cobot environments.

EMD-based data augmentation method applied to handwriting data for the diagnosis of Essential Tremor using LSTM networks.

The increasing capacity of today's technology represents great advances in diagnosing diseases using standard procedures supported by computer science. Deep learning techniques are able to extract the characteristics of temporal signals to study their patterns and diagnose diseases such as essential tremor. However, these techniques require a large amount of data to train the neural network and achieve good results, and the more data the network has, the more accurate the final model implemented. In this work we propose the use of a data augmentation technique to improve the accuracy of a Long short-term memory system in the diagnosis of essential tremor. For this purpose, the multivariate Empirical Mode Decomposition method will be used to decompose the original temporal signals collected from control subjects and patients with essential tremor. The time series obtained from the decomposition, covering different frequency ranges, will be randomly shuffled and combined to generate new artificial samples for each group. Then, both the generated artificial samples and part of the real samples will be used to train the LSTM network, and the remaining original samples will be used to test the model. The experimental results demonstrate the capability of the proposed method, which is compared to a set of 10 different data augmentation methods, and in all cases outperforms all other methods. In the best case, the proposed method increases the accuracy of the classifier from 83.20% to almost 93% when artificial samples are generated, which is a promising result when only small databases are available.

Automatic Analysis of Archimedes' Spiral for Characterization of Genetic Essential Tremor Based on Shannon's Entropy and Fractal Dimension.

Among neural disorders related to movement, essential tremor has the highest prevalence; in fact, it is twenty times more common than Parkinson's disease. The drawing of the Archimedes' spiral is the gold standard test to distinguish between both pathologies. The aim of this paper is to select non-linear biomarkers based on the analysis of digital drawings. It belongs to a larger cross study for early diagnosis of essential tremor that also includes genetic information. The proposed automatic analysis system consists in a hybrid solution: Machine Learning paradigms and automatic selection of features based on statistical tests using medical criteria. Moreover, the selected biomarkers comprise not only commonly used linear features (static and dynamic), but also other non-linear ones: Shannon entropy and Fractal Dimension. The results are hopeful, and the developed tool can easily be adapted to users; and taking into account social and economic points of view, it could be very helpful in real complex environments.

Innovation evaluation model for macro-construction sector companies: A study in Spain.

The innovativeness of the traditional construction sector, composed of construction companies or contractors, is not one of its strong points. Likewise, its poor productivity in comparison with other sectors, such as manufacturing, has historically been criticized. Similar features are found in the Spanish traditional construction sector, which it has been described as not very innovative. However, certain characteristics of the sector may explain this behavior; the companies invest in R+D less than in other sectors and release fewer patents, so traditional innovation evaluation indicators do not reflect the true extent of its innovative activity. While previous research has focused on general innovation evaluation models, limited research has been done regarding innovation evaluation in the macro-construction sector, which includes, apart from the traditional construction companies or contractors, all companies related to the infrastructure life-cycle. Therefore, in this research an innovation evaluation model has been developed for macro-construction sector companies and is applied in the Spanish case. The model may be applied to the macro-construction sector companies in other countries, requiring the adaption of the model to the specific characteristics of the sector in that country, in consultation with a panel of experts at a national level.

Quality management and quality of care in nursing homes.

PURPOSE: The purpose of this paper is to study the appropriateness of applying "manufacturing sector" quality management strategies to residential care homes sector and to analyze its influence on the quality of care. DESIGN/METHODOLOGY/APPROACH: Observation and in-depth interviews were conducted with 41 Spanish care home top and middle managers, consultants and employees. FINDINGS: The quality management paradigm based on ISO 9001 has certain shortcomings in the elderly residential care home sector. There is a need to fit general quality management models to the sector's specific characteristics and to integrate generic quality management with specialized models. PRACTICAL IMPLICATIONS: Research findings should be noted by different agents involved in the process of improving services. ORIGINALITY/VALUE: Useful, up-to-date conceptual overview for different agents interested in the sector (managers, consultants, academics, etc.) as well as interesting evidence for reflection.