NEP+: A Human-Centered Framework for Inclusive Human-Machine Interaction Development.

This article presents the Network Empower and Prototyping Platform (NEP+), a flexible framework purposefully crafted to simplify the process of interactive application development, catering to both technical and non-technical users. The name "NEP+" encapsulates the platform's dual mission: to empower the network-related capabilities of ZeroMQ and to provide software tools and interfaces for prototyping and integration. NEP+ accomplishes this through a comprehensive quality model and an integrated software ecosystem encompassing middleware, user-friendly graphical interfaces, a command-line tool, and an accessible end-user programming interface. This article primarily focuses on presenting the proposed quality model and software architecture, illustrating how they can empower developers to craft cross-platform, accessible, and user-friendly interfaces for various applications, with a particular emphasis on robotics and the Internet of Things (IoT). Additionally, we provide practical insights into the applicability of NEP+ by briefly presenting real-world user cases where human-centered projects have successfully utilized NEP+ to develop robotics systems. To further emphasize the suitability of NEP+ tools and interfaces for developer use, we conduct a pilot study that delves into usability and workload assessment. The outcomes of this study highlight the user-friendly features of NEP+ tools, along with their ease of adoption and cross-platform capabilities. The novelty of NEP+ fundamentally lies in its holistic approach, acting as a bridge across diverse user groups, fostering inclusivity, and promoting collaboration.

The where of handovers by humans: Effect of partner characteristics, distance and visual feedback.

Object handovers between humans are common in our daily life but the mechanisms underlying handovers are still largely unclear. A good understanding of these mechanisms is important not only for a better understanding of human social behaviors, but also for the prospect of an automatized society in which machines will need to perform similar objects exchanges with humans. In this paper, we analyzed how humans determine the location of object transfer during handovers- to determine whether they can predict the preferred handover location of a partner, the variation of this prediction in 3D space, and to examine how much of a role vision plays in the whole process. For this we developed a paradigm that allows us to compare handovers by humans with and without on-line visual feedback. Our results show that humans have the surprising ability to modulate their handover location according to partners they have just met such that the resulting handover errors are in the order of few centimeters, even in the absence of vision. The handover errors are least along the axis joining the two partners, suggesting a limited role for visual feedback in this direction. Finally, we show that the handover locations are explained very well by a linear model considering the heights, genders and social dominances of the two partners, and the distance between them. We developed separate models for the behavior of 'givers' and 'receivers' and discuss how the behavior of the same individual changes depending on his role in the handover.