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.