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Wearable computing devices are those inserted into the user's personal space, being able to provide sensing, data processing and communication. The use of this technology is in continuous growth in the last years, especially in the context of Industry 4.0. The goal of this study is to identify the acceptance of wearable technologies in industrial environments, as well as the factors that influence users' acceptance and perception. The research method includes a survey composed by 32 questions, answered by 871 employees of Brazilian and European companies (Germany, Belgium, Spain, Italy and Turkey) from various industrial sectors. Data analysis was applied on the survey responses, based on Machine Learning techniques, specifically through an adaptive Gradient Boosting algorithm, for results comparison between Brazil and Europe. Three statistical models are proposed to analyze acceptance inside and outside the workspace and acceptance outside workspace when there is a benefit for the worker respondent. The acceptance levels of wearable technologies in the workspace resulted in 82.22% in Brazil, and 81.74% in Europe. On the other side, acceptance of the technology outside the workspace reached 79.68% in Brazil and 66.21% in Europe. The last part of the study presents an acceptance outside the workspace increasing to 91.22% in Brazil and 84.02% in Europe when there is some type of compensation for the user. • In general. to accept a given technology, it is important to the user to be informed about the wearable device proposal. • Wearable technology users typically worry about the privacy of collected data. • Technology acceptance for both Brazilian and European users are not statistically different when survey data are considered together. • According to the geographic area, technology acceptance outside workspace is higher in Brazil than in Europe. • For both Brazilian and European survey respondents, technology acceptance reach higher levels when there is compensation for the user. [ABSTRACT FROM AUTHOR]

The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively. [ABSTRACT FROM AUTHOR]