Neurodegenerative diseases often result in pathological gait patterns, reducing mobility, stability, and overall functional capabilities. Given their impact on older adults' quality of life, early and accurate diagnosis is crucial for timely intervention. Traditional gait assessment technologies present some limitations related to low portability levels and user comfort. In this context, Socially Assistive Robots (SARs) offer an alternative by enabling non-intrusive gait monitoring while also supporting professional caregivers with objective measurements of users' motor performance. This study investigates the feasibility of using a mobile robotic platform to extract and analyze digital biomarkers related to gait activity. A novel pipeline was developed to automatically detect gait parameters from laser sensor data, segment the gait cycle, and compare these measurements against inertial measurement unit (IMU) data, which is the widely used approach. Results demonstrate a strong correlation (CI > 0.7) between laser-derived and IMU-based temporal gait parameters. However, discrepancies in step length measurements suggest that laser-based tracking provides more precise spatial information than IMU estimations. Additionally, this study explores the influence of the robotic platform on gait performance. Findings indicate that users walk faster when the robot is absent, despite its position behind them and out of sight. This suggests an unconscious adaptation to the robot’s presence, aligning with previous studies on human-robot interaction.

Socially Assistive Robots (SARs) represent a valid support to professional caregivers in providing care to person with need. To improve the quality of the interaction, SARs should be able to automatically assess user engagement. In this work, we addressed this problem by investigating user engagement dynamics during a robot-to-human handover task, considering 3 main components of engagement: affective, cognitive, and behavioral. For this study, we automatically extracted 10 visual features from the camera recordings of 31 participants. Each individual engaged in eight consecutive sessions with a robot manipulator designed with social cues. Our statistical analysis indicates that prolonged interaction with the robot could influence user engagement. Namely, we observed a decrease in positive emotions (affective), a more regulated quantity of motion (behavioral), and a reduced attention on the robot tasks (cognitive). Overall, the results of this study suggests that engagement dynamics can be described by the selected behavioral features, and that the a more predictable robot’s behavior could negatively influence user engagement.

Adaptability and behavioral diversity are core components of social interactions between humans. Naturally, these are traits research should strive to achieve in social robotics so agents may be better accepted and engage with their user peers. In this paper, we propose a novel activity modulation to increase behavioral diversity, based on a surprise-exploration correlation model, in a social robot undergoing behavioral optimization to user state and preference. This framework was tested with 21 participants to assess preferences as well as the impact that action variability and persistence would have on user perception of the robot. Results indicate a positive effect of persistence and variability over robot likability as well as user engagement, contributing insight for future research in social robotics.

This work aims to investigate if a "robot's personality" can affect the social perception of the robot in the navigation task. To this end, we implemented a dedicated human-aware navigation system that adapts the configuration of the navigation parameters (i.e. proxemics and velocity) based on two different human-like personalities, extrovert (EXT) and introvert (INT), and we compared them with a no social behavior (NS). We evaluated the system in a dynamic scenario in which each participant needed to pass by a robot moving in the opposite direction, showing a different personality each time. The Eysenck Personality Inventory and a modified version of the Godspeed questionnaire were administered to assess the user’s and the perceived robot’s personalities, respectively. The results show that 19 out of 20 subjects involved in the study perceived a difference among the personalities exhibited by the robot, both in terms of proxemics and velocity. Furthermore, the results highlight a general preference of a complementary robot’s personality, helping to suggest some guidelines for future works in the human-aware navigation field.

The European population is getting older; many elderlies would like to live independently in their homes as long as possible. In this context, a robotic telepresence service could support the frail persons in their homes, empowering their social relationships. In this work, 10 frail elderly were asked to live with a telepresence robot (i.e. Double Robot), through which the formal caregiver could remotely visit and chat with them. A total of 169 days of field-test trial was evaluated before (TO) and after (TF) the tests with a multidimensional framework, including acceptance, usability, and expectation domains. The system was used for 2871 mins, and the results underline good usability- and acceptance- related domains (average score equals to 70.83 at TF) and expectation (average score equal to 67.01 at TF). Results remark that expectation could influence the potential and the real use of the robot. Additionally, a positive trend in the answers was identified between T0 and TF. Indeed, the evaluation of a system should envisage a complex, multidisciplinary and holistic approach, that may influence the success or failure of the robot's purpose, if not properly analysed during the evaluation and design phase.

The aim of this work is to provide an automatic analysis to assess the user attitude when interacts with a companion robot. In detail, our work focuses on defining which combination of social cues the robot should recognize so that to stimulate the ongoing conversation and how. The analysis is performed on video recordings of 9 elderly users. From each video, low-level descriptors of the behavior of the user are extracted by using open-source automatic tools to extract information on the voice, the body posture, and the face landmarks. The assessment of 3 types of attitude (neutral, positive and negative) is performed through 3 machine learning classification algorithms: k-nearest neighbors, random decision forest and support vector regression. Since intra- and intersubject variability could affect the results of the assessment, this work shows the robustness of the classification models in both scenarios. Further analysis is performed on the type of representation used to describe the attitude. A raw and an auto-encoded representation is applied to the descriptors. The results of the attitude assessment show high values of accuracy (>0.85) both for unimodal and multimodal data. The outcome of this work can be integrated into a robotic platform to automatically assess the quality of interaction and to modify its behavior accordingly.

This article presents the School Attachment Monitor, a novel interactive system that can reliably administer the Manchester Child Attachment Story Task (a standard psychiatric test for the assessment of attachment in children) without the supervision of trained professionals. Attachment problems in children cause significant mental health issues and costs to society which technology has the potential to reduce. SAM collects, through instrumented doll-play games, enough information to allow a human assessor to manually identify the attachment status of children. Experiments show that the system successfully does this in 87.5% of cases. In addition, the experiments show that an automatic approach based on deep neural networks can map the information collected into the attachment condition of the children. The outcome SAM matches the judgment of expert human assessors in 82.8% of cases. This is the first time an automated tool has been successful in measuring attachment. This work has significant implications for psychiatry as it allows professionals to assess many more children cost effectively and to direct healthcare resources more accurately and efficiently to improve mental health.

Aging society is characterized by a high prevalence of sarcopenia, which is considered one of the most common health problems of the elderly population. Sarcopenia is due to the age-related loss of muscle mass and muscle strength. Recent literature findings highlight that the Tinetti Balance Assessment (TBA) scale is used to assess the sarcopenia in elderly people. In this context, this article proposes a model for sarcopenia assessment that is able to provide a quantitative assessment of TBA-gait motor parameters by means of a cloud robotics approach. The proposed system is composed of cloud resources, an assistive robot namely ASTRO and two inertial wearable sensors. Particularly, data from two inertial sensors (i.e., accelerometers and gyroscopes), placed on the patient's feet, and data from ASTRO laser sensor (position in the environment) were analyzed and combined to propose a set of motor features correspondent to the TBA gait domains. The system was preliminarily tested at the hospital of “Fondazione Casa Sollievo della Sofferenza” in Italy. The preliminary results suggest that the extracted set of features is able to describe the motor performance. In the future, these parameters could be used to support the clinicians in the assessment of sarcopenia, to monitoring the motor parameters over time and to propose personalized care-plan.