1,721,004 research outputs found
VPM: Analyzing human daily habits through process discovery
Full text linkModels usually employed for Ambient Intelligence (AmI) in smart homes are usually obtained directly from sensor logs composed by timestamped sequences of sensor measurements. Such approaches, still effective at different tasks, have the drawback of producing representations difficult to read and validate. In this paper we propose a tool, called Visual Process Maps (VPM), intended to allow the analysis of human routines at the human action level thanks to log preprocessing and the application of process discovery
A Preliminary Study on Virtual Reality Tools in Human-Robot Interaction
Full text linkChoosing the best interaction modalities and protocols in Human-Robot Interaction (HRI) is far from being straightforward, as it strictly depends on the application domain, the tasks to be executed, the types of robots and sensors involved. In the last years, a growing number of HRI researchers exploited Virtual Reality (VR) as a mean to evaluate proposed solutions, focusing in particular on safety and correctness of collaborative tasks. This allows to prove the effectiveness and robustness of a certain approach in a simulated environment, thus permitting to converge more easily to the best solution, also avoiding to experiment potentially harmful actions in a real scenario. In this paper, we aim at reviewing existing VR based approaches targeting or embodying HRI
Towards an Information Systems-driven Maturity Model for Industry 4.0
No full textThe term Industry 4.0 is used to denote the last evolution of manufacturing, concerning the large employment of information technologies, Internet-of-Things (IoT) and Artificial Intelligence (AI) to reduce the costs and produce high quality products. Even though many manufacturers declare themselves Industry 4.0-compliant, in order to attract public investments or to simply emerge among competitors, often only very limited aspects of the production comply with the definition. In this paper, we introduce the technologies involved in Industry 4.0, and, according to those ones, we propose the idea of a framework to assess the maturity of a company as an Industry 4.0 player
Gesture Recognition for Human-Robot Interaction Through Virtual Characters
No full textHuman-Robot Interaction (HRI) has grown increasingly important with the integration of various types of robots into industrial and daily life aspects. Non-verbal communication plays a vital role in HRI and heavily relies on gesture recognition. The ability of robot’s interpretation of human gestures is essential for enhancing the overall user experience. To achieve precision and effectiveness of gesture recognition in HRI, the incorporation of Virtual Reality (VR) technology offers promising avenues. In this article, we describe the development of a gesture recognition pipeline tailored to HRI in Precision Agriculture scenarios. Its key feature is the use of Virtual characters and Machine learning techniques to allow agricultural robots to recognize gestures performed by both digital agents and humans. We address challenges by presenting a set of gesture definitions, data generation, and system evaluation using varies combinations of real and virtual simulated data. Our results demonstrate how the adoption of virtual simulations can significantly increase the system’s accuracy and efficiency
Predicting activities of daily living via temporal point processes: Approaches and experimental results
No full textActivity Prediction is foreseeing the following activity people are going to execute. This is a crucial task in smart home environments, i.e., in order to facilitate the daily routines of elderly people with or without special needs. In this paper, we focused on Activity Daily Living prediction and we proposed a novel activity prediction technique based on the combination of Marked Temporal Point Processes and Neural Networks. Experiments on real and synthetic smart space datasets have shown that our approach is able to conveniently represent and predict daily living activities in an unsupervised way. We evaluated its performance and compared its results with state-of-the-art methods providing freely available implementations. Noticeably, the proposed approach outperforms the best concurrent algorithm by obtaining an improvement of F1-score of 60% (on average of the considered datasets)
Exploiting Marked Temporal Point Processes for Predicting Activities of Daily Living
No full textThe increasingly large availability of sensors in modern houses, due to the establishment of home assistants, allow to think in terms of smart houses where behaviours can be automatized based on user habits. Common tasks required to this aim include activity prediction, i.e., the task of forecasting what is the next activity a human is going to perform in the smart space based on past sensor logs. In this paper, we propose a novel activity prediction method for smart houses based on the seminal probabilistic method named Marked Temporal Point Process Prediction
From Component-Based Architectures to Microservices: A 25-years-long Journey in Designing and Realizing Service-Based Systems
Full text linkDistributed information systems and applications are generally described in terms of components and interfaces among them. How these component-based architectures have been designed and implemented evolved over the years, giving rise to the so-called paradigm of Service-Oriented Computing (SOC). In this chapter, we will follow a 25-years-long journey on how design methodologies and supporting technologies influenced one each other, and we discuss how already back in the late 90s the ancestors of the SOC paradigm were there, already paving the way for the technological evolution recently leading to microservice architectures and serverless computing
Supporting Zero Defect Manufacturing Through Cloud Computing and Data Analytics: the Case Study of Electrospindle 4.0
No full textIndustry 4.0 represents the last evolution of manufacturing. With respect to Industry 3.0, which introduced the digital interconnection of machinery with monitoring and control systems, the fourth industrial revolution extends this concept to sensors, products and any kind of object or actor (thing) involved in the process. The tremendous amount of data produced is intended to be analyzed by applying methods from artificial intelligence, machine learning and data mining. One of the objective of such an analysis is Zero Defect Manufacturing, i.e., a manufacturing process where data acquired during the entire life cycle of products is used to continuously improve the product design in order to provide customers with unprecedented quality guarantees. In this paper, we discuss the design choices behind a Zero Defect Manufacturing system architecture in the specific use case of spindle manufacturing
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