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.

Comparison of the External Load in Training Sessions and Official Matches in Female Football: A Case Report.

The objective of this study was to compare the external load of training sessions using as a reference an official competition match in women's football in order to find if the training sessions replicate the competition demands. Twenty-two semi-professional football players were analyzed during 17 weeks in the first phase of the competitive period of the 2020-2021 season of Spanish women's football. In addition to the competition (Official Matches, OM), four types of sessions were distinguished: strength or intensity (INT), endurance or extensity (EXT), velocity (VEL), and activation or pre-competitive (PREOM). The external load variables recorded were total distance (TD), high-speed running (HSR), sprint (Sprint), accelerations (ACC2), decelerations (DEC2), player load (PL), distance covered per minute (TDmin), high metabolic load distance (HMLD), and total impacts. The main results were that the external load demanded was different according to the type of session, being, in all cases, much lower than OM. The variables referring to the neuromuscular demands (ACC2 and DEC2) were higher in the INT sessions, the TD variable in the EXT sessions and the velocity variables (HSR and Sprint) in the VEL sessions. We can conclude that there was an alternating horizontal distribution of training loads within the competitive micro-cycle in women's football, although the order was not the usual one for tactical periodization.

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.

Analysis of Fine Motor Skills in Essential Tremor: Combining Neuroimaging and Handwriting Biomarkers for Early Management.

Essential tremor (ET) is a highly prevalent neurological disorder characterized by action-induced tremors involving the hand, voice, head, and/or face. Importantly, hand tremor is present in nearly all forms of ET, resulting in impaired fine motor skills and diminished quality of life. To advance early diagnostic approaches for ET, automated handwriting tasks and magnetic resonance imaging (MRI) offer an opportunity to develop early essential clinical biomarkers. In this study, we present a novel approach for the early clinical diagnosis and monitoring of ET based on integrating handwriting and neuroimaging analysis. We demonstrate how the analysis of fine motor skills, as measured by an automated Archimedes' spiral task, is correlated with neuroimaging biomarkers for ET. Together, we present a novel modeling approach that can serve as a complementary and promising support tool for the clinical diagnosis of ET and a large range of tremors.

Changes in Day/Night Activity in the 6-OHDA-Induced Experimental Model of Parkinson's Disease: Exploring Prodromal Biomarkers.

The search for experimental models mimicking an early stage of Parkinson's disease (PD) before motor manifestations is fundamental in order to explore early signs and get a better prognosis. Interestingly, our previous studies have indicated that 6-hydroxydopamine (6-OHDA) is a suitable model to induce an early degeneration of the nigrostriatal system without any gross motor impairment. Considering our previous findings, we aim to implement a novel system to monitor rats after intrastriatal injection of 6-OHDA to detect and analyze physiological changes underlying prodromal PD. Twenty male Sprague-Dawley rats were unilaterally injected with 6-OHDA (n = 10) or saline solution (n = 10) into the right striatum and placed in enriched environment cages where the activity was monitored. After 2 weeks, the amphetamine test was performed before the sacrifice. Immunohistochemistry was developed for the morphological evaluation and western blot analysis to assess molecular changes. Home-cage monitoring revealed behavioral changes in response to 6-OHDA administration including significant hyperactivity and hypoactivity during the light and dark phase, respectively, turning out in a change of the circadian timing. A preclinical stage of PD was functionally confirmed with the amphetamine test. Moreover, the loss of tyrosine hydroxylase expression was significantly correlated with the motor results, and 6-OHDA induced early proapoptotic events. Our findings provide evidence for a novel prodromal 6-OHDA model following a customized monitoring system that could give insights to detect non-motor deficits and molecular targets to test neuroprotective/neurorestorative agents.

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.

Advances on Automatic Speech Analysis for Early Detection of Alzheimer Disease: A Non-linear Multi-task Approach.

OBJECTIVE: Nowadays proper detection of cognitive impairment has become a challenge for the scientific community. Alzheimer's Disease (AD), the most common cause of dementia, has a high prevalence that is increasing at a fast pace towards epidemic level. In the not-so-distant future this fact could have a dramatic social and economic impact. In this scenario, an early and accurate diagnosis of AD could help to decrease its effects on patients, relatives and society. Over the last decades there have been useful advances not only in classic assessment techniques, but also in novel non-invasive screening methodologies. METHODS: Among these methods, automatic analysis of speech -one of the first damaged skills in AD patients- is a natural and useful low cost tool for diagnosis. RESULTS: In this paper a non-linear multi-task approach based on automatic speech analysis is presented. Three tasks with different language complexity levels are analyzed, and promising results that encourage a deeper assessment are obtained. Automatic classification was carried out by using classic Multilayer Perceptron (MLP) and Deep Learning by means of Convolutional Neural Networks (CNN) (biologically- inspired variants of MLPs) over the tasks with classic linear features, perceptual features, Castiglioni fractal dimension and Multiscale Permutation Entropy. CONCLUSION: Finally, the most relevant features are selected by means of the non-parametric Mann- Whitney U-test.

Discrete Cosine Transform for the Analysis of Essential Tremor.

Essential tremor (ET) is the most common movement disorder. In fact, its prevalence is about 20 times higher than that of Parkinson's disease. In addition, studies have shown that a high percentage of cases, between 50 and 70%, are estimated to be of genetic origin. The gold standard test for diagnosis, monitoring and to differentiate between both pathologies is based on the drawing of the Archimedes' spiral. Our major challenge is to develop the simplest system able to correctly classify Archimedes' spirals, therefore we will exclusively use the information of the x and y coordinates. This is the minimum information provided by any digitizing device. We explore the use of features from drawings related to the Discrete Cosine Transform as part of a wider cross-study for the diagnosis of essential tremor held at Biodonostia. We compare the performance of these features against other classic and already analyzed ones. We outperform previous results using a very simple system and a reduced set of features. Because the system is simple, it will be possible to implement it in a portable device (microcontroller), which will receive the x and y coordinates and will issue the classification result. This can be done in real time, and therefore without needing any extra job from the medical team. In future works these new drawing-biomarkers will be integrated with the ones obtained in the previous Biodonostia study. Undoubtedly, the use of this technology and user-friendly tools based on indirect measures could provide remarkable social and economic benefits.

Enhanced Flexibility and Reusability through State Machine-Based Architectures for Multisensor Intelligent Robotics.

This paper presents a state machine-based architecture, which enhances the flexibility and reusability of industrial robots, more concretely dual-arm multisensor robots. The proposed architecture, in addition to allowing absolute control of the execution, eases the programming of new applications by increasing the reusability of the developed modules. Through an easy-to-use graphical user interface, operators are able to create, modify, reuse and maintain industrial processes, increasing the flexibility of the cell. Moreover, the proposed approach is applied in a real use case in order to demonstrate its capabilities and feasibility in industrial environments. A comparative analysis is presented for evaluating the presented approach versus traditional robot programming techniques.

Kinematic and Pressure Features of Handwriting and Drawing: Preliminary Results Between Patients with Mild Cognitive Impairment, Alzheimer Disease and Healthy Controls.

BACKGROUND: Alzheimer's disease (AD) is the most common neurodegenerative dementia of old age, and the leading chronic disease contributor to disability and dependence among older people worldwide. Clinically, AD is characterized by a progressive cognitive decline that interferes with the ability to perform the activities of daily living. Handwriting and drawing are complex human activities that entail an intricate blend of cognitive, kinesthetic, and perceptual-motor features. OBJECTIVE: To compare the kinematic characteristics of handwriting and drawing between patients with AD, patients with mild cognitive impairment (MCI) and healthy controls. METHODS: We used a cross-sectional and observational design to assess the kinematic and pressure features of handwriting and drawing using a computerized system. Participants were asked to copy one sentence, write a dictated sentence and an own sentence, copy two and-three dimensions drawings, and to execute the clock drawing test. By means of discriminant analyses, we explored the value of several kinematic features in order to classify participants depending on their degree of cognitive functioning. RESULTS: The sample consisted of 52 participants (23 AD, 12 MCI, and 17 healthy controls) with a mean age of 69.7 years (SD=8.11). The degree of correct classification was largely dependent on the nature of the groups to be classified and the specific task, and ranged between 63.5% and 100%. Diagnostic accuracy based on kinematic measures showed higher specificity values for distinguishing between normal and impaired cognition (MCI and AD), and higher sensitivity was obtained when distinguishing between impaired cognition levels (MCI vs. AD). CONCLUSION: The kinematic features of writing and drawing procedures, rather than the final product, may be a useful and objective complement to the clinical assessment of patients with cognitive impairment.

Innovative diagnostic tools for early detection of Alzheimer's disease.

Current state-of-the-art diagnostic measures of Alzheimer's disease (AD) are invasive (cerebrospinal fluid analysis), expensive (neuroimaging) and time-consuming (neuropsychological assessment) and thus have limited accessibility as frontline screening and diagnostic tools for AD. Thus, there is an increasing need for additional noninvasive and/or cost-effective tools, allowing identification of subjects in the preclinical or early clinical stages of AD who could be suitable for further cognitive evaluation and dementia diagnostics. Implementation of such tests may facilitate early and potentially more effective therapeutic and preventative strategies for AD. Before applying them in clinical practice, these tools should be examined in ongoing large clinical trials. This review will summarize and highlight the most promising screening tools including neuropsychometric, clinical, blood, and neurophysiological tests.

The role of environmental biotechnology in exploring, exploiting, monitoring, preserving, protecting and decontaminating the marine environment.

In light of the Marine Strategy Framework Directive (MSFD) and the EU Thematic Strategy on the Sustainable Use of Natural Resources, environmental biotechnology could make significant contributions in the exploitation of marine resources and addressing key marine environmental problems. In this paper 14 propositions are presented focusing on (i) the contamination of the marine environment, and more particularly how to optimize the use of biotechnology-related tools and strategies for predicting and monitoring contamination and developing mitigation measures; (ii) the exploitation of the marine biological and genetic resources to progress with the sustainable, eco-compatible use of the maritime space (issues are very diversified and include, for example, waste treatment and recycling, anti-biofouling agents; bio-plastics); (iii) environmental/marine biotechnology as a driver for a sustainable economic growth.

On the selection of non-invasive methods based on speech analysis oriented to automatic Alzheimer disease diagnosis.

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.