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Longitudinal interaction studies with Socially Assistive Robots are crucial to ensure that the robot is relevant for long-term use and its perceptions are not prone to the novelty effect. In this paper, we present a dynamic Bayesian network (DBN) to capture the longitudinal interactions participants had with a teleoperated robot coach (RC) delivering mindfulness sessions. The DBN model is used to study complex, temporal interactions between the participants self-reported personality traits, weekly baseline wellbeing scores, session ratings, and facial AUs elicited during the sessions in a 5-week longitudinal study. DBN modelling involves learning a graphical representation that facilitates intuitive understanding of how multiple components contribute to the longitudinal changes in session ratings corresponding to the perceptions of the RC, and participants relaxation and calm levels. The learnt model captures the following within and between sessions aspects of the longitudinal interaction study: influence of the 5 personality dimensions on the facial AU states and the session ratings, influence of facial AU states on the session ratings, and the influences within the items of the session ratings. The DBN structure is learnt using first 3 time points and the obtained model is used to predict the session ratings of the last 2 time points of the 5-week longitudinal data. The predictions are quantified using subject-wise RMSE and R2 scores. We also demonstrate two applications of the model, namely, imputation of missing values in the dataset and estimation of longitudinal session ratings of a new participant with a given personality profile. The obtained DBN model thus facilitates learning of conditional dependency structure between variables in the longitudinal data and offers inferences and conceptual understanding which are not possible through other regression methodologies., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Social robots are starting to become incorporated into daily lives by assisting in the promotion of physical and mental wellbeing. This paper investigates the use of social robots for delivering mindfulness sessions. We created a teleoperated robotic platform that enables an experienced human coach to conduct the sessions in a virtual manner by replicating upper body and head pose in real time. The coach is also able to view the world from the robot's perspective and make a conversation with participants by talking and listening through the robot. We studied how participants interacted with a teleoperated robot mindfulness coach over a period of 5 weeks and compared with the interactions another group of participants had with a human coach. The mindfulness sessions delivered by both types of coaching invoked positive responses from the participants for all the sessions. We found that the participants rated the interactions with human coach consistently high in all aspects. However, there is a longitudinal change in the ratings for the interaction with the teleoperated robot for the aspects of motion and conversation. We also found that the participants' personality traits -- conscientiousness and neuroticism influenced the perceptions of the robot coach., Comment: 11 pages. Corrected Typos

Aim: Evaluation of solid lipid nanoparticles (SLNs) for ocular delivery of isoniazid (INH). Materials & methods: INH-SLNs were characterized for morphological, thermal, crystalline and nuclear magnetic resonance properties. In vitro release and ex vivo corneal permeability of INH-SLNs was also evaluated. Proof-of-concept uptake studies were performed in corneal and conjunctival cell lines and in vivo in rat eye using fluorescein-labeled SLNs. Antimycobacterial activity of INH-SLNs was confirmed. In vivo aqueous humor pharmacokinetics, toxicity and tolerance was performed in rabbit/rat eye. Results: INH-SLNs showed extended release (48 h), enhanced corneal permeability (1.6-times), five-times lower MIC, significant in vitro and in vivo uptake of fluorescein-labeled SLNs, 4.2-times ocular bioavailability (area under the curve) and in vivo acute and repeat dose safety. Conclusion: INH-SLNs are an effective ocular delivery system., Department of Biotechnology (DBT), New Delhi, India, Unidad Docente de Bioquímica y Biología Molecular, Fac. de Óptica y Optometría, TRUE, pub

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots---inferred capability and intention---and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.

This paper presents a novel method for classifying four different levels of cognitive workload. The workload levels are generated using visual stimuli degradation. EEG signals recorded from 16 subjects were used for workload classification. The proposed solution includes preprocessing of EEG signals and feature extraction based on statistical features. This is followed by variable selection using stepwise regression and multiclass linear classification. The presented method achieved an average classification accuracy of 93.4%. The effect of EEG channel selection on the classification accuracy is also investigated. In comparison to the existing work, we show that the proposed solution is more accurate and computationally less demanding.