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The present study aimed to assess the validity of using a robotic platform (RODDI) to increase and enhance communicative exchanges between educators and children with Autism Spectrum Disorder (ASD) and intellectual disability. Nine children with ASD and intellectual disability and their educators took part in the study. Their communicative exchanges were assessed in two different settings: a normal play session and a RODDI play session. RODDI platform did not appear to enhance communicative exchanges in children with ASD. They produced a significantly lower number of vocal productions to the educators in the RODDI play sessions than in the normal ones. Even the educators’ vocal production was significantly lower in quantitative and qualitative terms in the RODDI play sessions. However, analysing children’s gaze direction, RODDI appeared to enhance children’s attention on the task. The benefits of using robotic platforms with children with ASD should be further investigated.

Objectives: To confirm the data reported in our previous studies on the analysis of the variability of the electroglottographic signal in the pathological voice; to evaluate possible differences in variability between organic and functional pathologies; to identify any distinctive/typical EGG patterns for these pathologies. Methods: One hundred twenty-five subjects were enrolled (36 euphonic and 89 pathological: 24 functional dysphonia, 21 bilateral vocal nodules, 23 unilateral polyps and 21 unilateral cysts). All subjects were studied with videolaryngostroboscopy, spectrographic analysis of voice and electroglottography (EGG). The EGG signal variability was then investigated using amplitude-speed combined analysis, by means of a proprietary software algorithm. Amplitude and Speed variation were expressed as a new parameter, the Variability Index (VI), calculated both for the whole EGG signal recorded (VI-tot) and in each phase of the glottic cycle (VI-Q, absolute value; VI-Q%, percentage value). Results: In the comparison of VI values between pathological and normal groups, VI-tot and VI-Q2% (which corresponds to the final phase of vocal fold contact) were significantly greater in pathological subjects (P= 0.002). The comparison of VI values among subgroups of the various pathologies showed a difference for VI-tot (P< 0.0001) and VI-Q2% (P= 0.001); this difference was more marked in the cysts than in the functional dysphonia. The cut-off values of VI-tot and VI-Q2% were 0.191 and 18.17%, respectively (sensitivity and specificity 65.2% and 66.7% for VI-tot and 84.3% and 77.8% for VI-Q2%). Conclusions: The variability of the EGG signal investigated through the combined analysis of the amplitude and the speed of vibration using a proprietary algorithm software has proved useful not only to distinguish the normal voice from the pathological voice, but also to characterize which phases are more altered in the various voice pathologies studied, both functional and org

In a recent study we introduced a new approach for analysis of the electroglottographic (ECG) signal. This method is based on the evaluation of variation of the EGG signal and its first derivative, through new software developed by the Pisan phoniatric school. This software is designed to extract quantitative indices related to the contacting and decontacting phases of the vocal folds during phonation. The software allows us to study the combined variability of vibration amplitude and velocity (i.e. the first derivative of the EGG signal). Pathological voices show a much more variable EGG signal compared to normal voices, since cordal vibration is made irregular due to the presence of glottis plane pathologies. With the aim of demonstrating the differences between normal and pathological voices relevant to combined vibration amplitude and velocity variability, we have introduced a new quantitative parameter named "variability index, VI". We studied 95 subjects (35 normal and 60 with pathological voice); among pathologic subjects, 15 showed functional dysphonia and 45 showed organic dysphonia. Subjects affected by organic dysphonia presented: 15 bilateral vocal nodules, 15 unilateral polyps and 15 unilateral cysts. All subjects were studied with videolaryngostroboscopy; electro-acoustic parameters of the voice were analysed with the KayPENTAX CSL (Model 4500) system. The EGG signal was recorded using KAY Model 6103 connected to the CSL system. The new software for the analysis of the EGG signal allows us to obtain not only a VI total value relevant to variability during all the recording, but also partial VI values relevant to the different glottis cycle phases. In fact, plotting the amplitude variation and its first derivative on a Lissajous graph, it is possible to divide the whole glottis cycle into four phases (each represented by four quadrants on the graph): the initial vocal folds contacting activity (VI-Q1), the last phase of vocal folds contacting (VI-Q2), t

Minimally invasive arthroscopic surgery is a very challenging procedure that requires the manipulation of instruments in limited intraarticular space using distorted and sometimes uninformative images. Localizing the arthroscope reliably and at all times w.r.t. surrounding tissue is of fundamental importance to prevent unintended injury to patients. However, even highly-trained surgeons can struggle to localize the arthroscope using poor image feedback. In this paper, we propose and demonstrate for the first time a visual Simultaneous Localization and Mapping (SLAM) system, termed ArthroSLAM, capable of robustly and reliably localizing an arthroscope inside a human knee joint. The proposed system fuses the information obtained from the arthroscope, an external camera mounted on an arthroscope holder, and the odometry of a robotic arm manipulating the scope, in an Extended Kalman Filter framework. Also for the first time, we implement five alternative strategies for localization and compare them to our method in a realistic setup with a human cadaver knee joint. ArthroSLAM is shown to outperform the alternative strategies under various challenging conditions, localizing reliably and at all times with a mean Relative Pose Error of up to 1.4mm and 0.7°. Additional experiments conducted with degraded odometry data also validate the robustness of the method. An initial evaluation of the sparse map of a knee section computed by our method exhibits good morphological agreement. All results suggest that ArthroSLAM is a viable component for the robotic orthopedic surgical assistant of the future.

This paper presents the design and implementation of an assisted control technology for a small multirotor platform for aerial inspection of fixed energy infrastructure. Sensor placement is supported by a theoretical analysis of expected sensor performance and constrained platform behaviour to speed up implementation. The optical sensors provide relative position information between the platform and the asset, which enables human operator inputs to be autonomously adjusted to ensure safe separation. The assisted control approach is designed to reduced operator workload during close proximity inspection tasks, with collision avoidance and safe separation managed autonomously. The energy infrastructure includes single vertical wooden poles and crossarm with attached overhead wires. Simulated and real experimental results are provided.

In this paper, we propose a novel Image-Based Visual Servoing (IBVS) controller for multirotor aerial robots based on a recent deep reinforcement learning algorithm named Deep Deterministic Policy Gradients (DDPG). The proposed RL-IBVS controller is successfully trained in a Gazebo-based simulated environment in order to learn the appropriate IBVS policy for directly mapping a state, based on errors in the image, to the linear velocity commands of the aerial robot. A thorough validation of the proposed controller has been conducted in simulated and real flight scenarios, demonstrating outstanding capabilities in object following applications. Moreover, we conduct a detailed comparison of the RL-IBVS controller with respect to classic and partitioned IBVS approaches.

This paper represents a step towards vision-based manipulation of plastic materials. Manipulating deformable objects is made challenging by: 1) the absence of a model for the object deformation, 2) the inherent difficulty of visual tracking of deformable objects, 3) the difficulty in defining a visual error and 4) the difficulty in generating control inputs to minimise the visual error. We propose a novel representation of the task of manipulating deformable objects. In this preliminary case study, the shaping of kinetic sand, we assume a finite set of actions: pushing, tapping and incising. We consider that these action types affect only a subset of the state, i.e., their effect does not affect the entire state of the system (specialized actions). We report the results of a user study to validate these hypotheses and release the recorded dataset. The actions (pushing, tapping and incising) are clearly adopted during the task, although it is clear that 1) participants use also mixed actions and 2) actions' effects can marginally affect the entire state, requesting a relaxation of our specialized actions hypothesis. Moreover, we compute task errors and corresponding control inputs (in the image space) using image processing. Finally, we show how machine learning can be applied to infer the mapping from error to action on the data extracted from the user study.

This work presents Semi-Supervised SLAM - a method for developing a map suitable for coarse localization within an underground environment with minimal human intervention, with system characteristics driven by real-world requirements of major mining companies. This work leverages existing information common within a mining environment - namely a surveyed mine map - which is used to sparsely ground map locations within the mine environment, increasing map accuracy and allowing localization within a global frame. Map creation utilizes a low cost camera sensor and minimal user information to produce a map which can be used for single camera localization within a mining environment. We evaluate the localization capabilities of the proposed approach in depth by performing data collection on operational underground mining vehicles within an active underground mine and by simulating occlusions common to the environment such as dust and water. The proposed system is capable of producing maps which have an average localization error 2.5 times smaller than the next best performing method ORB-SLAM2, comparable localization performance to a state-of-the-art deep learning approach (which is not a feasible solution due to both compute and training requirements) and is robust to simulated environmental obscurants.