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.

Movement is one of the basic tools that humans use to convey emotional states. Body language and movement are also relevant in the perception that we have of other people. Many studies have been done on movement and emotion sharing, involving humans on one side and robots or automated agents on the other. From these studies, there is evidence of the importance of robots to improve their social capabilities and develop more effective social interaction. This work aims at embedding some social movements in a robot manipulator, that will elicit an emotional response from users. Laban Movement Analysis is used to do so, and social movements are developed on a robotic arm, then shown to participants in an online study and a questionnaire. The results show that it is possible to elicit emotions through movement only and that the perception is not affected by personal experiences.

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.

Hemiparesis is one of the most frequent poststroke conditions, which causes muscle weakness and/or inability to move one side of the body. Physical rehabilitation is the main treatment for hemiparesis recovery, and physiotherapists agree that using the impaired arm in the activities of daily living (ADLs)is crucial for a complete recovery. Currently, rehabilitation is assessed through diaries and self-questionnaires, which are subjective and do not tell the real condition of the patients throughout the day. Assistive devices can objectively evaluate the functional improvement of the impaired arm monitoring its activity. This work aimed to identify the purposeful arm movements during patient's ADLs. We consider arm's swing while walking as a non-purposeful movement. Firstly, the event-based approach was applied to separate movement and non-movement segments. Secondly, movement segments were used to detect the change-point (events)and their locations in time series signal. Two machine learning classifiers, Support Vector Machine (SVM)and Artificial Neural Network (ANN), were trained using 10-fold cross validation for the classification of purposeful and non-purposeful movements. Data from 10 healthy and 12 post-strokes volunteers from Institute Guttmann (Barcelona)were collected using the SensHand device. The volunteers, wearing one SensHand on each wrist, performed the following activities: resting, eating, pouring water, drinking, brushing, folding towel, grasp towel, grasp brush, grasp glass, continuous and walking. Developing a model based on the healthy subjects, the overall classification accuracy obtained from SVM classifier and ANN was 81.21 % and 97.06% respectively. Similarly, with poststroke subjects obtained accuracy with the SVM and ANN was 84.18% and 99.74% respectively. Considering the whole dataset, SVM and ANN obtained maximum accuracy equal to 86.21 % and 99.91 % respectively. In conclusion, our work showed promising results for the classification of purposeful and non-purposeful movements.

In recent years, autonomous robots have proven capable of solving tasks in complex environments. In particular, robot manipulations in activities of daily living (ADL) for service robots have been widely developed. However, manipulations of grasping an unreachable object in domestic environments still present difficulty. To perform those applications better, we developed an autonomous system with human-in-the-loop that combined the cognitive skills of a human operator with autonomous robot behaviors. In this work, we present techniques for integration the system for assistive mobile manipulation and new strategies to support users in the domestic environment. We demonstrate that the robot can grasp multiple objects with random size at known and unknown table heights. Specifically, we developed three strategies for manipulation. We also demonstrated these strategies using two intuitive interfaces, a visual interface in rviz and a voice user interface with speech recognition. Moreover, the robot can select strategies automatically in random scenarios, which make the robot intelligent and able to make decisions independently in the environment. We demonstrated that our robot shows the capabilities for employment in domestic environments to perform actual tasks.

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.

Internet of Robotic Things paradigm offers a concrete support to daily life. The pervasiveness of smart things, together with advances in cloud robotics, can help the smart systems to perceive and collect more information about the users and the environment. Often citizens have experienced “one-size-fits-all” approach, since the delivered service was not personalized, therefore resulting far from user's expectations. Hence, future smart agents, like robots, should produce personalized behaviours based on user emotions and moods in order to be more integrated into ordinary activities. In this work, we investigated the performances with unsupervised and supervised approaches to recognize three different moods elicited during a social interaction by means of a wearable system capable of measuring the Electrocardiogram, the ElectroDermal Activity and the Electroencephalographic signals. Particularly, the classification problem was analysed using three unsupervised (K-Mean, Self-Organizing Map and Hierarchical Clustering) and three supervised methods (Support Vector Machine, Decision Tree and k-nearest neighbour). The supervised algorithms reached an accuracy of 0.86 in the best case. The outcomes show that even in an unsupervised context the system is able to recognize the mood, reaching an accuracy equal to 0.76 in the best case.

Human activity recognition is an important and active field of research having a wide range of applications in numerous fields including ambient‐assisted living (AL). Although most of the researches are focused on the single user, the ability to recognise two‐person interactions is perhaps more important for its social implications. This study presents a two‐person activity recognition system that uses skeleton data extracted from a depth camera. The human actions are encoded using a set of a few basic postures obtained with an unsupervised clustering approach. Multiclass support vector machines are used to build models on the training set, whereas the X ‐means algorithm is employed to dynamically find the optimal number of clusters for each sample during the classification phase. The system is evaluated on the Institute of Systems and Robotics (ISR) ‐ University of Lincoln (UoL) and Stony Brook University (SBU) datasets, reaching overall accuracies of 0.87 and 0.88, respectively. Although the results show that the performances of the system are comparable with the state of the art, recognition improvements are obtained with the activities related to health‐care environments, showing promise for applications in the AL realm.

Daily activity recognition can help people to maintain a healthy lifestyle and robot to better interact with users. Robots could therefore use the information coming from the activities performed by users to give them some custom hints to improve lifestyle and daily routine. The pervasiveness of smart things together with advances in cloud robotics can help the robot to perceive and collect more information about the users and the environment. In particular thanks to the miniaturization and low cost of Inertial Measurement Units, in the last years, body-worn activity recognition has gained popularity. In this work, we investigated the performances with an unsupervised approach to recognize eight different gestures performed in daily living wearing a system composed of two inertial sensors placed on the hand and on the wrist. In this context our aim is to evaluate whether the system is able to recognize the gestures in more realistic applications, where is not possible to have a training set. The classification problem was analyzed using two unsupervised approaches (K-Mean and Gaussian Mixture Model), with an intra-subject and an inter-subject analysis, and two supervised approaches (Support Vector Machine and Random Forest), with a 10-fold cross validation analysis and with a Leave-One-Subject-Out analysis to compare the results. The outcomes show that even in an unsupervised context the system is able to recognize the gestures with an averaged accuracy of 0.917 in the K-Mean inter-subject approach and 0.796 in the Gaussian Mixture Model inter-subject one.

This paper presents a work on mapping the use of space by humans in long periods of time. Daily geometric maps with the same coordinate frame were generated with SLAM, and in a similar manner, daily affordance density maps (places people use) were generated with the output of a human tracker running on the robot. The contribution of the paper is two-fold: an approach to detect geometric changes to cluster them in similar geometric configurations and the building of geometric and affordance composite maps on each cluster. This approach avoids the loss of long term retrieved information. Geometric similarity was computed using a normal distance approach on the maps. The analysis was performed on data collected by a mobile robot for a period of 4 months accumulating data equivalent to 70 days. Experimental results show that the system is capable of detecting geometric changes in the environment and clustering similar geometric configurations.

In this paper we present the design and technical implementation of a web based Multi-Modal User Interface (MMUI) tailored for elderly users of the robotic services developed by the EU FP7 Large-Scale Integration Project Robot-Era. The project partners are working to significantly enhance the performance and acceptability of technological services for ageing well by delivering a fully realized system based on the cooperation of multiple heterogeneous robots and with the support of an Ambient Assisted Living environment. To this end, elderly users were involved in the definition of the services and in the design of the hardware and software of the robotic platforms from the first stages of the development process and in real experimentation in two test sites. In particular, here we detail the interface software system for multi-modal elderly-robot interaction. The MMUI is designed to run on any device including touch-screen mobiles and tablets that are preferred by the elderly. This is obtained by integrating web based solutions with the Robot-Era middlewares and planner. Finally we present some preliminary results of ongoing experiments to show the successful evaluation of usability by potential users and to discuss the future directions to improve the proposed MMUI software system.

This paper presents the full experience of designing, developing and testing ASTROMOBILE, a system composed of an enhanced robotic platform integrated in an Ambient Intelligent (AmI) infrastructure that was conceived to provide favourable independent living, improved quality of life and efficiency of care for senior citizens. The design and implementation of ASTRO robot was sustained by a multidisciplinary team in which technology developers, designers and end-user representatives collaborated using a user-centred design approach. The key point of this work is to demonstrate the general feasibility and scientific/technical effectiveness of a mobile robotic platform integrated in a smart environment and conceived to provide useful services to humans and in particular to elderly people in domestic environments. The main aspects faced in this paper are related to the design of the ASTRO's appearance and functionalities by means of a substantial analysis of users' requirements, the improvement of the ASTRO's behaviour by means of a smart sensor network able to share information with the robot (Ubiquitous Robotics) and the development of advanced human robot interfaces based on natural language.

Minimally invasive surgery (MIS) has become very common in recent years, thanks to the many advantages it provides for patients. Since it is difficult for surgeons to learn and master this technique, several training methods and metrics have been proposed, both to improve the surgeon's abilities and also to assess his/her skills. This paper presents the use of the WB-1R (Waseda bioinstrumentation system no.1 refined), which was developed at Waseda University, Tokyo, to investigate and analyze a surgeon's movements and performance. Specifically, the system can measure the movements of the head, the arms, and the hands, as well as several physiological parameters. In this paper we present our experiment to evaluate a surgeon's ability to handle surgical instruments and his/her depth perception using a laparoscopic view. Our preliminary analysis of a subset of the acquired data (i.e. comfort of the subjects; the amount of time it took o complete each exercise; and respiration) clearly shows that the expert surgeon and the group of medical students perform very differently. Therefore, WB-1R (or, better, a newer version tailored specifically for use in the operating room) could provide important additional information to help assess the experience and performance of surgeons, thus leading to the development of a global performance index for surgeons during MIS. These analyses and modeling, moreover, are an important step towards the automatization and the robotic assistance of the surgical gesture.

In this paper, we develop a system for which applications in the field of personal navigation are planned. In the current version, the system embodies a Global Positioning System (GPS) receiver and an inertial measurement unit (IMU), composed of two dual-axis accelerometers and one single-axis gyro. The IMU is positioned at a subject's foot instep, and it is intended to produce estimates of some gait parameters, including stride length, stride time, and walking speed. Data from GPS and IMU are managed by a DSP-based control box. The computations performed by the DSP processor allow to detect subsequent foot contacts by a threshold-based method applied to gyro signal, and to reconstruct the trajectory of the foot instep by numerical strapdown integration. Features of human walking dynamics are incorporated in the algorithm to enhance the estimation accuracy against errors due to sensor noise and integration drift. All computations are performed by the DSP processor in real-time conditions. The foot sensor performance is assessed during outdoor level walking trials. The traveled distance estimated by inertial dead-reckoning is compared with the estimate produced by GPS in experimental conditions where GPS can be used as a reference source for accurate absolute positioning. Results show the remarkable accuracy achieved by foot inertial sensing.