Assistive technology (AT) design is critical in enabling functionality for people with disabilities, blending essential elements of both practical utility and user experience. Traditionally, AT has successfully addressed core functional needs, such as enabling cursor movement and clicking actions with devices like computer mice. However, a comprehensive approach to AT design also necessitates a thorough consideration of sensory feedback, including tactile sensations, ergonomics, and auditory cues like button click sounds. These aspects are not merely supplementary but are integral to the device’s functionality, enhancing user interaction and long-term comfort, especially for individuals with motor impairments. In this work, we present MouseClicker, a mechatronic AT to surrogate physical agency over a computer mouse and to foster the haptic sensory experience of clicking on it tailored specifically for an individual with Spinal Muscular Atrophy (SMA) who faces challenges in using a standard mouse due to severe hand motor impairments. Our design aims at replicating the holistic experience of clicking a mouse, from its functional mechanical actions to its nuanced tactile and auditory feedback. This work details the MouseClicker’s design and reports on an exploratory user study aimed at identifying optimal vibrotactile feedback parameters – such as location, and intensity – that represent mouse button clicks. MouseClicker presents a step forward in AT design by integrating the functionality, sensory feedback, and the overall experience of taking control over non-AT devices.

As robotic systems become increasingly integrated into daily life, the need for user experience (UX) assessment methods that are both privacy-conscious and suitable for embedded hardware platforms has grown. Traditional UX evaluations relying on vision, audio, or lengthy questionnaires are often intrusive, computationally demanding, or impractical for low-power devices. In this study, we introduce a novel sensor-based method for assessing UX through direct physical interaction. We designed a robot lamp with a force-sensing button interface and conducted a user study involving controlled robot errors. Participants interacted with the lamp during a reading task and rated their UX on a 7-point Likert scale. Using force and time data from button presses, we correlated force and time data to user experience and demographic information. Our results demonstrate the potential of bodily interaction metrics as a viable alternative for UX assessment in human-robot interaction, enabling real-time, embedded, and privacy-aware evaluation of user satisfaction in robotic systems.

My research is on Human-Robot Interaction (HRI) from a User Centered Design (UCD) perspective and values the user’s qualitative assessment of their interaction under the concept of User Experience (UX). Traditionally, UX is retrieved in a written questionnaire as a satisfaction survey, nevertheless, with the hike in electronic devices equipped with a diversity of sensors, actuators and processing capabilities (turning everyday appliances into robots) comes an intention to migrate from qualitative to quantitative assessment of the UX; for example, visual recognition of emotional facial expressions. In this regard, what I present as a novelty in the research area is the study of interaction data generated through the robot’s interface during its intended use (everyday use), while providing an anonymous experience (no cameras, no mics) to the user. For the experimental setup, I designed and built a robotic Desk-Lamp with 5 degrees of freedom (height, brightness, projection angle, sensitivity of the interface and ambient lighting) as test bench, equipped with: an interface instrumented to sense the force delivered by the user while “pushing buttons”; 18 variable data-logging system at 20 SPS (Sample Per Second); high precision and high repeatability electronics and mechanisms; real-time sensing of the user and low-latency operation due to parallel processing. The test bench is designed to be used in multiple experiments with users, varying the robot behavior and interaction procedures to compare the interaction data generated in the interface with a written UX questionnaire.