Archivio della ricerca della Scuola Superiore Sant'Anna
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Organizing and Prioritizing User Needs for Upper Limb Prostheses
No full textUpper limb amputation leads to severe restrictions in daily activities and psychosocial difficulties, which can dramatically reduce the quality of life of affected people. Despite the significant scientific and engineering effort in building advanced robotic prostheses, their abandonment rates suggest a discrepancy between user needs and device performance. To address this gap, we present an initial step in identifying, harmonizing, and prioritizing the needs of people with limb loss. Using the PRISMA methodology, we analysed 73 papers on the topic. Using a bottom-up approach, we clustered the needs identified in different surveys into categories and macro-categories based on a taxonomy derived from the terms identified in the literature. We then prioritized the needs after a normalization of the major surveys. We believe that this work will provide both a high-level and low-level understanding of the needs of people with limb loss, thereby helping to guide the design of more user-friendly prostheses. In the future, combining these results with the definition of technical specifications will enable the identification of needs that can be satisfied through personalized design, particularly considering recent advances in manufacturing processes
Intelligent Processing System (IPS)
No full textThe Intelligent Processing System (IPS) integrates deep learning models (YOLOv11x for object detection and MobileNetV3 for affordance segmentation) for robotic teleoperation tasks. The system is implemented on a UR5 with a RealSense D455 RGB-D camera
RoboSense25-DashGrasp Dataset
No full textThis dataset comprises data collected by 30 participants during a robotic experiment with and without using the virtual dashboard. Thus, it integrates into the RoboSense25 Dataset and contains task execution times, operator level of fatigue declarations, and task success rates for statistical analysis
What “V” of the big data support firms’ radical and incremental innovation?
Full text linkDespite the considerable attention from both academics and practitioners to the effects of big data on firms’
innovation performance, a noticeable research gap remains in understanding how big data influences different
types of innovation—namely, radical and incremental innovation. Many studies recognize that big data can be a
valuable source of innovation, as it enables firms to gather and incorporate insights from customers, partners,
suppliers, and other stakeholders. However, prior research has rarely investigated this relationship through a
granular lens, failing to distinguish the specific effects of big data on radical and incremental innovation.
Focusing on firms’ intent of introducing radical and incremental innovation using big data, we employ the
Knowledge Based View and the four well-known dimensions of big data (i.e., volume, velocity, variety, and
veracity) to explore if and when big data is a source of knowledge for radical and incremental innovation.
Performing an OLS regression analysis on a sample of 155 Italian firms, we find that both big data variety and
veracity positively affect firms’ radical and incremental innovation. These findings provide insights about the
conditions under which big data can improve firms’ innovation processes, contributing to a more comprehensive
theoretical understanding of the opportunities big data bring in the context of firms’ product, service and process
innovation. Moreover, our findings offer valuable guidance to managers navigating the complexities of
leveraging big data for new product development
PHYSICS GUIDED GRAPH NEURAL NETWORKS (PG-GNNS): NUMERICAL INVESTIGATION OF HIGH-PRESSURE TURBINE FLOW
No full textA Metal Tailor-Made IoT Platform to Enable Power Management Integration and Support Procurement Access to the Power Market
No full textPower management is crucial in energy-intensive metals manufacturing. An IIoT platform tailored for the steel industries was applied to reduce energy costs by integrating energy data-driven models considering volatile markets and contracts. This innovative tool enhances energy monitoring, allowing factories to efficiently interact with the power grid. Its interoperability and scalability enable optimized scheduling of tasks like Electric Arc Furnace (EAF) route, improving energy efficiency and reducing operational costs by responding effectively to power market fluctuations