ME3CA - Monitoring environment exercise and emotion by a cognitive assistant

The elderly population has increased dramatically in today’s society. This fact implies the need to propose new policies of attention to this group but without increasing social spending. Currently, there is a need to promote the care of elderly people in their own homes, avoiding being transferred to saturated residences. Bearing this in mind, in recent years numerous approaches have tried to offer solutions in this sense using the continuous advances in new information and communication technologies. In this way, this article proposes the employment of a personal assistant to help the elderly in the development of their daily life activities. The proposed system, called ME3CA, is a cognitive assistant that involves users in rehabilitating exercise, consisting of a sensorization platform and different integrated decision-making mechanisms. The system tries to plan and recommend activities to older people trying to improve their physical activity. In addition, in the decision making process the assistant takes into account the emotions of the user. In this way, the system is more personalized and emotionally intelligent.- (undefined

ME3CA: A Cognitive Assistant for Physical Exercises that Monitors Emotions and the Environment

[EN] Recent studies show that the elderly population has increased considerably in European society in recent years. This fact has led the European Union and many countries to propose new policies for caring services directed to this group. The current trend is to promote the care of the elderly in their own homes, thus avoiding inverting resources on residences. With this in mind, there are now new solutions in this direction, which try to make use of the continuous advances in computer science. This paper tries to advance in this area by proposing the use of a personal assistant to help older people at home while carrying out their daily activities. The proposed personal assistant is called ME(3)CA, and can be described as a cognitive assistant that offers users a personalised exercise plan for their rehabilitation. The system consists of a sensorisation platform along with decision-making algorithms paired with emotion detection models. ME(3)CA detects the users' emotions, which are used in the decision-making process allowing for more precise suggestions and an accurate (and unbiased) knowledge about the users' opinion towards each exercise.This work was partly supported by the FCT-Fundacao para a Ciencia e Tecnologia through the Post-Doc scholarship SFRH/BPD/102696/2014 (A. Costa), the Generalitat Valenciana (PROMETEO/2018/002) and the Spanish Government (RTI2018-095390-B-C31).Rincon, J.; Araujo, A.; Novais, P.; Julian Inglada, VJ.; Carrascosa Casamayor, C. (2020). ME3CA: A Cognitive Assistant for Physical Exercises that Monitors Emotions and the Environment. Sensors. 20(3):1-14. https://doi.org/10.3390/s20030852S11420

Deployment of DeepTech AI Models in Engineering Solutions

Ponencia presentada en ICRAMAE-2k21, International Conference on Recent Advances in Mechanical and Automation Engineering, Vivekananda Global University, Jaipur, India, 29-30th November 2021[EN]Industrial Engineering is a branch of engineering that focuses on the design and operation of industrial processes. It involves the application of science to the construction of production systems. This field has undergone significant advancements over the last decades. In the last centuries, the emergence of different technologies has led to breakthroughs in engineering, making it possible to automate processes in industries. Steam, electricity, the internet, and now Artificial Intelligence technologies have all brought with them greater levels of automation to machinery, gradually decreasing human involvement in processes such as procurement, raw material handling, manufacturing and quality control

Intelligent models for recommendation

Seminario presentado en EMAI2021, West Bengal, India, 4/4/2021[EN]Information tools are one of the types of tools available in an effort to change consumers' perceptions, motivations, knowledge and standards. Accordingly, it is increasingly important for consumers to be able to make informed choices about the products they buy, especially in terms of sustainability. Together with the commitment of businesses and organizations to more responsible and sustainable processes and production, the implementation of the European Green Deal and the Sustainable Development Goals is an urgent challenge to all actors in society to contribute to changing the way we meet our needs

AI models for recommendation

Ponencia presentada en EMAI2021, West Bengal, India, 4/4/2021[EN]Today, the industries of all European countries face common challenges: improving resource efficiency, becoming more environmentally friendly, mitigating climate change, improving the digitization in all segments of the value chain and improving transparency and safety, providing consumers with detailed information and ensuring the safety and quality of the final product. Growing concerns about environmental and social issues are pushing the demands of stakeholders (customers, workers, shareholders, consumers, etc.) and the public towards more sustainable processes and products. Sustainability is closely linked to climate change: the introduction of sustainable measures, both by consumers and producers, is inherently a measure against climate change

Recommendation AI models: case studies

Seminario presentado en EMAI2021, West Bengal, India, 4/4/2021[EN] The targeted consumers can be not only individuals sensitive to environmental and sustainable consumption issues, but also communities, small businesses (e.g., local coffee shop, school, sports club) that share the same concerns as their customers or are just trying to better address their needs. In addition, this tool is designed to assist decision-makers in companies (e.g., supply chain and purchasing managers) as well as policy makers in assessing the overall sustainability of products. Likewise, the tool can provide valuable information to manufacturers who, based on the "sustainable market momentum" gained, could innovate their products and their approach to improving sustainability, thus differentiating themselves from the competitio

Text Similarity Between Concepts Extracted from Source Code and Documentation

Context: Constant evolution in software systems often results in its documentation losing sync with the content of the source code. The traceability research field has often helped in the past with the aim to recover links between code and documentation, when the two fell out of sync. Objective: The aim of this paper is to compare the concepts contained within the source code of a system with those extracted from its documentation, in order to detect how similar these two sets are. If vastly different, the difference between the two sets might indicate a considerable ageing of the documentation, and a need to update it. Methods: In this paper we reduce the source code of 50 software systems to a set of key terms, each containing the concepts of one of the systems sampled. At the same time, we reduce the documentation of each system to another set of key terms. We then use four different approaches for set comparison to detect how the sets are similar. Results: Using the well known Jaccard index as the benchmark for the comparisons, we have discovered that the cosine distance has excellent comparative powers, and depending on the pre-training of the machine learning model. In particular, the SpaCy and the FastText embeddings offer up to 80% and 90% similarity scores. Conclusion: For most of the sampled systems, the source code and the documentation tend to contain very similar concepts. Given the accuracy for one pre-trained model (e.g., FastText), it becomes also evident that a few systems show a measurable drift between the concepts contained in the documentation and in the source code.</p