arXiv subjects

Beatrice Luciani

Publications and source records attributed to Beatrice Luciani.

3 recordsLinked to original sources

arXiv2026

Therapist-Robot-Patient Physical Interaction is Worth a Thousand Words: Enabling Intuitive Therapist Guidance via Remote Haptic Control

Robotic systems can enhance the amount and repeatability of physically guided motor training. Yet their real-world adoption is limited, partly due to non-intuitive trainer/therapist-trainee/patient interactions. To address this gap, we present a haptic teleoperation system for trainers to remotely guide and monitor the movements of a trainee wearing an arm exoskeleton. The trainer can physically interact with the exoskeleton through a commercial handheld haptic device via virtual contact points at the exoskeleton's elbow and wrist, allowing intuitive guidance. Thirty-two participants tested the system in a trainer-trainee paradigm, comparing our haptic demonstration system with conventional visual demonstration in guiding trainees in executing arm poses. Quantitative analyses showed that haptic demonstration significantly reduced movement completion time and improved smoothness, while speech analysis using large language models for automated transcription and categorization of verbal commands revealed fewer verbal instructions. The haptic demonstration did not result in higher reported mental and physical effort by trainers compared to the visual demonstration, while trainers reported greater competence and trainees lower physical demand. These findings support the feasibility of our proposed interface for effective remote human-robot physical interaction. Future work should assess its usability and efficacy for clinical populations in restoring clinicians' sense of agency during robot-assisted therapy.

cs.RO↗

arXiv2025

The Impact of Gait Pattern Personalization on the Perception of Rigid Robotic Guidance: A Pilot User Experience Evaluation

Exoskeletons modulate human movement across diverse applications, from performance augmentation to daily-life assistance. These systems often enforce specific kinematic patterns to mitigate injury risks and motivate users to keep moving despite diminished capacity. However, little is known about users' perception of such robot-imposed guidance, especially when personalized to the uniqueness of individual human walk. Given the usually substantial computational cost for personalization, understanding its subjective impact is essential to justify its implementation over standard patterns. Ten unimpaired participants completed a within-subject experiment in a multi-planar treadmill-based exoskeleton that enforced three different gait patterns: personalized, standard, and a randomly selected pattern from a publicly available database. Personalization was achieved using a data-driven framework that predicts hip, knee, and pelvis trajectories from walking speed, anthropometric, and demographic data. The standard pattern was obtained by averaging gait patterns from the aforementioned database. After each condition, participants rated enjoyment, comfort, and perceived naturalness. Knee joint interaction forces were also recorded. Subjective ratings revealed no significant differences among patterns, despite all trajectories being executed with high accuracy. However, gait patterns experienced last were rated as significantly more comfortable and natural, indicating adaptation to the system. Higher interaction forces were observed only for the random vs. standard pattern. Personalizing gait kinematics had minimal short-term influence on user experience relative to the dominant effect of adaptation to the exoskeleton. These findings highlight the importance of integrating subjective feedback and accounting for user adaptation when designing personalized robot controllers.

cs.RO↗

arXiv2024

Augmented reality for upper limb rehabilitation: real-time kinematic feedback with HoloLens 2

Exoskeletons for rehabilitation can help enhance motor recovery in individuals suffering from neurological disorders. Precision in movement execution, especially in arm rehabilitation, is crucial to prevent maladaptive plasticity. However, current exoskeletons, while providing arm support, often lack the necessary 3D feedback capabilities to show how well rehabilitation exercises are being performed. This reduces therapist acceptance and patients' performance. Augmented Reality technologies offer promising solutions for feedback and gaming systems in rehabilitation. In this work, we leverage HoloLens 2 with its advanced hand-tracking system to develop an application for personalized rehabilitation. Our application generates custom holographic trajectories based on existing databases or therapists' demonstrations, represented as 3D tunnels. Such trajectories can be superimposed on the real training environment. They serve as a guide to the users and, thanks to colour-coded real-time feedback, indicate their performance. To assess the efficacy of the application in improving kinematic precision, we tested it with 15 healthy subjects. Comparing user tracking capabilities with and without the use of our feedback system in executing 4 different exercises, we observed significant differences, demonstrating that our application leads to improved kinematic performance. 12 clinicians tested our system and positively evaluated its usability (System Usability Scale score of 67.7) and acceptability (4.4 out of 5 in the 'Willingness to Use' category in the relative Technology Acceptance Model). The results from the tests on healthy participants and the feedback from clinicians encourage further exploration of our framework, to verify its potential in supporting arm rehabilitation for individuals with neurological disorders.

cs.HC↗