The outbreak of the Severe Acute Respiratory Syndrome coronavirus (SARS-CoV2) has resulted in a significant disruption of almost all aspects of everyday life. Several governments around the world have adopted emergency actions to reduce spreading of the virus, which included suspension of non-essential activities and the implementation of social distancing practices. In our case, governmental measures have resulted in the suspension of our experimental protocol for testing the effectiveness of robot-based treatment of children diagnosed with Autism Spectrum Disorder (ASD) compared to conventional human (therapist)-based treatment. These circumstances led to an investigation of the potential of tele-consulting. This paper describes alternatives to implement synchronous and asynchronous therapeutic sessions for children already participating in the protocol, in order to reduce the negative effects of the strict cessation of the in-person sessions. The usefulness of our approach was assessed by recording the children’s and the parent’s satisfaction via questionnaires. In addition, we compare satisfaction between the synchronous and asynchronous sessions. The results show that the approach has been very satisfactory and useful for both children and parents, and that this was especially the case for the robot-based material.

Ripeness estimation of fruits and vegetables is a key factor for the optimization of field management and the harvesting of the desired product quality. Typical ripeness estimation involves multiple manual samplings before harvest followed by chemical analyses. Machine vision has paved the way for agricultural automation by introducing quicker, cost-effective, and non-destructive methods. This work comprehensively surveys the most recent applications of machine vision techniques for ripeness estimation. Due to the broad area of machine vision applications in agriculture, this review is limited only to the most recent techniques related to grapes. The aim of this work is to provide an overview of the state-of-the-art algorithms by covering a wide range of applications. The potential of current machine vision techniques for specific viticulture applications is also analyzed. Problems, limitations of each technique, and future trends are discussed. Moreover, the integration of machine vision algorithms in grape harvesting robots for real-time in-field maturity assessment is additionally examined.

Our interest is in time series classification regarding cyber–physical systems (CPSs) with emphasis in human-robot interaction. We propose an extension of the k nearest neighbor (kNN) classifier to time-series classification using intervals’ numbers (INs). More specifically, we partition a time-series into windows of equal length and from each window data we induce a distribution which is represented by an IN. This preserves the time dimension in the representation. All-order data statistics, represented by an IN, are employed implicitly as features; moreover, parametric non-linearities are introduced in order to tune the geometrical relationship (i.e., the distance) between signals and consequently tune classification performance. In conclusion, we introduce the windowed IN kNN (WINkNN) classifier whose application is demonstrated comparatively in two benchmark datasets regarding, first, electroencephalography (EEG) signals and, second, audio signals. The results by WINkNN are superior in both problems; in addition, no ad-hoc data preprocessing is required. Potential future work is discussed.

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