Understanding how people occupy indoor spaces is important for applications such as smart building management, space utilization analysis, and adaptive environments. Traditional occupancy sensing solutions often rely on dedicated infrastructure, such as cameras, Wi-Fi access points, or specialized sensors, which may raise privacy concerns and require complex deployments. This work demonstrates an infrastructure-free approach for estimating indoor occupancy using passive Bluetooth Low Energy (BLE) scanning from commodity smartphones. A BLE scanner application for Android and iOS collects BLE advertisement packets emitted by nearby personal devices, which are treated as ambient signals that indirectly reflect human presence. To validate the system we collected a dataset within an academic building through repeated scanning sessions across two classrooms, a lab, and a university hall. The collected observations are aggregated by room and time and visualized through an interface that overlays device-density heat maps on building floor plans, enabling the exploration of spatial and temporal activity patterns. The proposed demonstration will be deployed at the AVI conference venue on the island of San Servolo, where smartphones in conference rooms will perform passive BLE scans and update the visualization interface, allowing participants to observe how device density evolves across rooms and time during the event.

This poster presents an interactive mobile interface that helps users describe their indoor surroundings, enabling automatic AI-based localization on evacuation maps. The interface prompts users to select the environmental elements they see around them from a set of icons commonly used in evacuation maps, such as elevators, fire extinguishers, alarm buttons, doors, and corridor shapes. As selections are made, the pool of possible locations narrows, and incompatible icons are removed from the set. The approach builds on recent work in which users described their surroundings through open-ended conversations, often including irrelevant or unexpected information, resulting in poor localization results. As a first step in assessing the efficacy of the proposed interface, a user study (N = 16) was conducted across multiple locations within a university building to investigate the impact of the interface design on usability and the overall localization process. This work aims to contribute to ongoing investigations of localization via human collaboration by proposing an interface that reduces the cognitive effort required to provide significant information to the localization backend.

Accurately estimating indoor occupancy is fundamental to the development of modern smart buildings, which aim to optimize critical parameters such as Heating, Ventilation, and Air Conditioning (HVAC) control, safety, and resource management in real time to reduce energy waste. Traditional sensing approaches, including cameras, Passive Infrared (PIR) sensors, and CO 2 monitors, often encounter high deployment costs, maintenance overhead, and significant privacy concerns, particularly under General Data Protection Regulation (GDPR) regulations. This paper presents the design, implementation, and evaluation of a non-invasive occupancy estimation system that exclusively relies on Bluetooth Low Energy (BLE) scans performed via a mobile device, eliminating the need for prior structural information or dedicated sensing infrastructure. The proposed method analyzes statistical differences between various university environments, such as Laboratories , Classrooms , and Corridors , while integrating variables such as the number of fixed and mobile devices, the average device density per person, and the interference caused by signal bleed-through between adjacent rooms. Based on a foundational study of device ownership behavior, we develop context-dependent calibration coefficients to address the multi-device phenomenon, in which a single occupant may carry multiple Bluetooth Low Energy emitters. Our system utilizes a three-layer architecture that includes Passive Bluetooth scanning , Signal filtering with night-baseline infrastructure detection , and Automatic room-type classification . This design allows for the dynamic selection of estimation parameters without the need for manual input. Field experiments conducted across various university spaces over a multi-week data collection period demonstrate that our context-aware model significantly reduces estimation error compared to traditional device-counting methods. This approach offers a scalable, cost-effective, and privacy-preserving engineering solution for real-time occupancy monitoring in smart campus environments.

This poster presents preliminary investigations into detecting human presence using Smartphone Bluetooth Low Energy (BLE) beacon signals. By treating smartphones as proxies for individuals, we summarize BLE signal visibility with privacy-aware features such as Stable Neighbors, Presence Counts, Turnover Rates, and Signal Strength statistics. Early findings from other research works suggest these stability-based summaries can effectively track occupancy and movement in indoor and public spaces, offering tiered density estimates rather than exact counts. We discuss several key challenges for this approach, including the presence of multiple devices per person, stationary IoT beacons, other irrelevant devices, and environmental variability. We emphasize the importance of conservative thresholds and space-specific calibration to minimize bias while preserving individual privacy. Potential applications include Crowd Awareness, Smart Buildings, Accessibility, Intelligent Transportation, and Adaptive Interfaces, among others. Future directions could involve calibrating this method, implementing lightweight multimodal fusion, and incorporating ethical safeguards for privacy-respecting implementations.

This paper introduces CWGPT, a ChatGPT-4-based tool designed for Cognitive Walkthrough (CW) inspired evaluations of web interfaces. The primary goal is to assist users, particularly students and inexperienced designers, in evaluating web interfaces. Our tool, operating as a conversational agent, provides detailed evaluations of a user-specified task by intelligently guessing the subtasks and actions required to accomplish them, answering the standard CW questions, and providing helpful feedback and practical suggestions to improve the usability of the analyzed interface. For our study, we selected a group of web applications designed by students from a Web and Software Architecture course. We compare the outcome of the CWs we executed on ten web apps against the corresponding CWGPT analyses. We then describe the study we conducted involving five author-students to assess the tool’s efficacy in helping them recognize and solve usability issues. In addition to introducing a novel adaptation of ChatGPT, the outcomes of the described experience underscore the promising potential of AI in usability evaluations.

Interacting with a smart parking system to find a parking spot might be tedious and unsafe if performed while driving. We present a system based on a Boosted Tree classifier that runs on the smartphone and automatically detects when the driver is cruising for parking. The system does not require direct intervention from the driver and is based on the analysis of context data. The classifier was trained and tested on real data (615 car trips) collected by 9 test users. With this research, we contribute (i) by providing a literature review on cruising detection, (ii) by proposing an approach to model cruising behavior, and (iii) by describing the design, training, and testing of the classifier and discussing its results. In the long term, our work aims to improve user experience and safety in car-related contexts by relying on human-centered features that implicitly understand users’ behavior and anticipate their needs.

Implicit interaction is a possible approach to improve the user experience of smartphone apps in car-related environments. Indeed, it can enhance safety and avoids unnecessary and repetitive interactions on the user’s part.

This work proposes an implicit interaction approach to ease implementing basic car-related tasks on a smartphone application. Many car drivers use apps on their smartphones to get support in typical tasks related to car usage, yet some of the available apps have a poor user experience because they require the user’s attention, causing a distraction while driving. In addition, they often rely on users inputting relevant data repetitively. Implicit interaction is a possible solution to improve the user experience of car-related interfaces. Basic user tasks for many car applications are (i) reporting parking the car in a specific position, (ii) declaring that the user will soon free a parking spot, and (iii) that a new trip with the car has begun (thus, that a parking spot became free). The proposed context-aware interaction approach to executing these tasks is described together with its implementation in an application that leverages the smartphone’s sensing capability of users’ locations and motion activities and merges them to infer parking and unparking events.

In this work, we present ISIDE, the prototype of a student dropout alert system integrated within Infostud, i.e., the online student portal of the Sapienza University of Rome. Our proposed solution is based on a student dropout prediction (SDP) module built from a large dataset of academic records using advanced machine learning techniques. Offline experiments show that the best-performing SDP model can detect students prone to leave the school with an F1score of 0.92. To further validate our prototype online, we run a pilot study on a subset of students from our School of Information Engineering, Informatics, and Statistics. This study shows that our prototype can detect students who are most likely to drop out early, as it clearly separates them from those with higher key engagement indicators.

This work proposes BowlingVR, an advanced Virtual Reality (VR) multiplayer game that tackles two main goals: the first one is to provide a realistic User eXperience (UX) to the user, by reproducing the dynamics and physical context of a real bowling challenge; the second one is to allow a remote, distributed, socially satisfying gameplay, providing the user the illusion of the real presence of the remote players. The prototype was evaluated using a modified version of SUXES, a kind of user interview schema that was originally devised for multimedia applications and that has been modified in order to better compare the responses of different users and get a more reliable estimation of user appreciation.

We present an approach to integrating extemporary annotations on topics of interest to a user with information on the context in which the annotation is being taken. Context is here defined in terms of the set of surrounding devices (Bluetooth Low Energy enabled smartphones, WiFi hotspots) and the current calendar event. Contextify is a context-aware Android application which detects the current context and organizes the notes taken within context, allowing a form of context-based retrieval. Thus, the detected context represents location, other people present (referring to their BLE equipped smartphones as user proxies), and events. Users can easily retrieve notes when they return to the context where they created them. A context similarity algorithm derived from the Jaro-Winkler string similarity algorithm is used to compare contexts. Each note is tagged by the user and the system suggests the most appropriate tag, among the already used ones, at annotation creation time: the suggestion is based on the similarity of the current context with the contexts associated with previously tagged notes.

Pressing buttons on a smartphone touch screen is difficult if you are not looking at the screen. We developed a numerical keyboard that provides a tactile feedback using phone short vibrations. The feedback is provided both when the user swipes the keyboard and when he presses keys. We describe how we implemented it on iPhone7, using the iPhone 3Dtouch capability and the UIFeedbackGenerator.

We designed and developed an app, for iOS and Android, which uses internal device accelerometer to detect earthquakes that may occur while the smartphone is stable on a at surface and to deliver a crowdsourced early warning to users in the region where the earthquake might be dangerous. We describe our interface for the Android operating system and we compare our system to the other main research work.

Many apps for mobile devices show lists of elements that the user can sort by choosing a single metric, e.g. price or date or alphabetical order. Often, none of the different sorting is ideal, in fact elements that are more interesting to the user are often spread along the list as there is no single sorting that can group them together at the top. We propose that the user can sort the list by including two or more metrics to create a personalized ranking. Sorting is thus multidimensional and the order of the chosen metrics defines a different ranking according to predefined weights. We designed and developed an iOS framework that implements a multidimensional sortable list, with a drag-and-drop interface to let the user choose the personal order of metrics. We performed qualitative user tests with 8 users.

This paper describes the design and development of a location-based Augmented Reality (AR) application for mobile devices. The application provides real time data about transportation in a urban area. It can be set along the line of the continuous creation of richer and more complex interaction modalities between users and data. A relevant element in this strategy is the visual enrichment of the real scene perceived though the mobile camera, by superimposing to it a set of user-relevant information. In the presented work, this information is related to nearby bus stops and to the arrival of next buses. More details, such as routes, distances etc. can be displayed on demand in order to gain awareness of the surrounding infomobility data. The presented application is included in a multiservice framework named RMob, developed for the city of Rome.

This paper describes the design and development of RMob, a mobile app for real time information in urban transportation. RMob provides fast, reliable and clear information about transportation in Rome, e.g. bus arrival times, urban travel times, bus stop maps, etc. People moving in large urban areas need to predict travel times and choose the fastest routes; they generally take real time decisions without planning ahead and are usually in hurry so they have limited time for interaction with the device and the application. This work aims to providing travellers in the urban Rome area with timely and accurate information, minimizing the interaction and the text input, so encouraging the best travel choice and the use of public transportation. Based on the different travel phases described in the literature, we propose a distinction on the nearby resources (bus stops, bus arrival times, parkings, car/bike sharing stations, etc.) and trip-destination resources (routes, travel times, parkings and stations at destination, etc.). The application was designed using this model that seems appropriate and usable according to our observations. RMob is published online for iOS and Android devices.

The described project is an iPhone application to control a robot using the accelerometer and the touch screen of the mobile device. The iPhone and the robot are connected over WiFi. The application interface displays full screen real time images coming from the robot vision system, allowing remote driving of the robot. We implemented 2 different driving modes and interactions: the first one fully exploits the accelerometer to move the robot forward and backward and to change direction, the second one uses the interface buttons for forward and backward movements, while the accelerometer is used only to change direction. In both driving modes there is the possibility to control the robot's mechanical arm to take and release an object through two touchscreen buttons. We carried on several user tests in order to validate and enhance our design. In fact, based on the test results, we could improve the interface as well as the driving experience (e.g. we could tune the correct power to drive the gearmotors of the robot as a function of the device inclination and the pressing duration of the interface controls). Users that tested the final version could smoothly drive the robot along a route with obstacles.

MADCOW 2.0 is a system for annotation of Web content, supporting the production and exploration of personal and public annotations on text, images and videos in a Web page. Its design starts from the main requirement that the annotation activity does not have to disrupt the normal browsing of Web pages by a user. MADCOW 2.0 allows interaction with the annotated portions of the page to provide access to the annotation content. Conversely, the representation of the existing notes supports different forms of exploration of the Web page, and can become the starting point for further navigation over the Web. A uniform style of interaction has been adopted for creating and accessing annotations on text, images and videos, and some novel solutions have been introduced to cope with overlaps between the annotated portions. The annotation user experience is facilitated by enabling forms of in-place annotation and manipulation of both the annotated portion and the annotation content.

This work describes the interface design and interaction of a generic annotation service for Digital Library Management Systems (DLMSs), called Digital Library Annotation Service (DiLAS), that has been designed and is currently undergoing development and user test in the framework of the DELOS European Network of Excellence. The objective of DiLAS is to design and develop an architecture and a framework able to support and evaluate a generic annotation service, i.e. a service that can be easily used into different DLMSs enhancing their User Interfaces (UIs) in order to offer to Digital Library (DL) users a set of uniform, user-tested (under certain required conditions), and recognizable functionalities. Copyright 2006 ACM.

The use of the Web and the diffusion of knowledge management systems makes it possible to base discussions upon a vast set of documents, many of which also include links to multimedia material, such as images or videos. This perspective could be exploited by allowing a team to collaborate by exchanging and retrieving annotated multimedia documents (text, images, audio and video). We designed and developed a digital annotation system, MADCOW, to assist users in constructing, disseminating, and retrieving multimedia annotations of documents, supporting collaborative activities to build a web of decision-related documents. We made a strong effort in designing the user interface and we tested it with 24 users. We describe a scenario in which annotation plays a crucial role, where the object of the collaboration is a politically and artistically important palace of Rome, for which the availability of images and historical documentation is fundamental in order to take informed decisions. We demonstrate the MADCOW interface and its use in the restoration team. The annotations can be used to support teamwork as well as to offer the public some reasoned integration and guide to the available material.

This paper identifies a number of factors involved in current practices of usability testing and presents profiles for three prototype methods: think-aloud, subjective ratings, and history files. We then identify ideal levels to generate the profile for new methods. These methods involve either a human observer or a self-administration of the test by the user. We propose methods of automating the evaluation form by dynamically adding items and modifying the form and the tasks in the process of the usability test. For self-administration of testing, we propose similar ideas of dynamically automating the forms and the tasks. Furthermore, we propose methods of eliciting the user's goals and focus of attention. Finally, we propose that user testing methods and interfaces should be subjected to usability testing. (c) 2005 Elsevier B.V. All rights reserved.

A decentralized communication and control system is presented for driving assistance and automation along designated highway segments. This paper is based on a hybrid dynamical model describing vehicular motion, and the structure of a safe controller is presented. A ranging and communication system provides information for a vehicle and its nearest neighbors. The system is capable of handling sudden changes of regime, overtakes, reentries, as well as a number of maneuvers dictated by safety requirements under partially unpredicted events. A user-friendly interface, based on a palmtop computer, is developed to assist drivers visually and acoustically.

Digital annotation of multimedia documents adds information to a document (e.g. a web page) or parts of it (a multimedia object such as an image or a video stream contained in the document). Digital annotations can be kept private or shared among different users over the internet, allowing discussions and cooperative work. We study the possibility of annotating multimedia documents with objects which are in turn of multimedial nature. Annotations can refer to whole documents or single portions thereof, as usual, but also to multi-objects, i.e. groups of objects contained in a single document. We designed and developed a new digital annotation system organized in a client-server architecture, where the client is a plug-in for a standard web browser and the servers are repositories of annotations to which different clients can login. Annotations can be retrieved and filtered, and one can choose different annotation servers for a document. We present a platform-independent design for such a system, and illustrate a specific implementation for Microsoft Internet Explorer on the client side and on JSP/MySQL for the server side.