The cooperation between humans and robots is becoming increasingly important in our society. Consequently, there is a growing interest in the development of models that can enhance and enrich the interaction between humans and robots. A key challenge in the Human-Robot Interaction (HRI) field is to provide robots with cognitive and affective capabilities, by developing architectures that let them establish empathetic relationships with users. Over the last several years, multiple models were proposed to face this open-challenge. This work provides a survey of the most relevant attempts/works. In details, it offers an overview of the architectures present in literature focusing on three specific aspects of HRI: the development of adaptive behavioral models, the design of cognitive architectures, and the ability to establish empathy with the user. The research was conducted within two databases: Scopus and Web of Science. Accurate exclusion criteria were applied to screen the 4916 articles found. At the end, 56 articles were selected. For each work, an evaluation of the model is made. Pros and cons of each work are detailed by analyzing the aspects that can be improved to establish an enjoyable interaction between robots and users.
Motor and Cognitive Dual-Task (MCDT) represents an innovative chance to assess Mild Cognitive Impairment (MCI). We compare two novel MCDTs, fore-finger tapping (FTAP), toe-tapping (TTHP), to gold standards for cognitive screening (Mini-Mental State Examination—MMSE), and to a well-established MCDT (GAIT). We administered the aforementioned MCDTs to 44 subjects (MCIs and controls). Motor parameters were extracted, and correlations with MMSE investigated. Logistic regression models were built, and AUC areas computed. Spearman’s correlation demonstrated that FTAP and TTHP significantly correlate with MMSE, at each cognitive load. AUC areas computed report similar (FTAP, 0.87), and even higher (TTHP, 0.97) capability to identify MCIs, if compared to GAIT (0.92). We investigated the use of novel MCDT approaches to assess MCI, aiming to enrich the clinical repertoire with objective and non-invasive tools. Our protocol shows good correlations with MMSE, and reaches high performances in identifying MCI, adopting simpler exercises.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.