Pupillometric parameters of alertness during unpredictable but not predictable smooth pursuit neck torsion test are altered in patients with neck pain disorders: a cross-sectional study.

Despite commonly investigated predictable smooth-pursuit neck-torsion tasks (SPNT) in neck pain patients, unpredictable conditions have been seldom investigated but are indicative of preserved oculomotor functions during neck torsion. Although not previously studied, some speculations about compensatory cognitive mechanisms such as increased phasic alertness during unpredictable tasks were suggested. The aim of this study was to investigate eye movement accuracy and pupillometric responses during predictable and unpredictable SPNT test in neck pain patients and asymptomatic controls. Eye movements (gain and SPNT-difference) and pupillometry indicative of tonic (average and relative pupil diameter) and phasic (index of cognitive activity-ICA) alertness were measured in 28 idiopathic neck pain patients and 30 asymptomatic individuals using infrared video-oculography during predictable and unpredictable SPNT test. Gain in unpredictable SPNT test was lower as compared to predictable tasks and presented with similar levels in neutral and neck torsion positions, but not in the predictable SPNT test. ICA was lower during neutral position in all tasks in patients as compared to control group but increased during neck torsion positions in unpredictable tasks. Relative pupil diameters presented with no differences between the groups or neck positions, but the opposite was observed for average pupil diameter. Higher ICA indicates an increase in phasic alertness in neck pain patients despite no alterations in oculomotor control during SPNT test. This is the first study to indicate cognitive deficits in oculomotor task in neck pain patients. The latter could negatively affect other tasks where additional cognitive resources must be involved.

Capturing Conversational Gestures for Embodied Conversational Agents Using an Optimized Kaneda-Lucas-Tomasi Tracker and Denavit-Hartenberg-Based Kinematic Model.

In order to recreate viable and human-like conversational responses, the artificial entity, i.e., an embodied conversational agent, must express correlated speech (verbal) and gestures (non-verbal) responses in spoken social interaction. Most of the existing frameworks focus on intent planning and behavior planning. The realization, however, is left to a limited set of static 3D representations of conversational expressions. In addition to functional and semantic synchrony between verbal and non-verbal signals, the final believability of the displayed expression is sculpted by the physical realization of non-verbal expressions. A major challenge of most conversational systems capable of reproducing gestures is the diversity in expressiveness. In this paper, we propose a method for capturing gestures automatically from videos and transforming them into 3D representations stored as part of the conversational agent's repository of motor skills. The main advantage of the proposed method is ensuring the naturalness of the embodied conversational agent's gestures, which results in a higher quality of human-computer interaction. The method is based on a Kanade-Lucas-Tomasi tracker, a Savitzky-Golay filter, a Denavit-Hartenberg-based kinematic model and the EVA framework. Furthermore, we designed an objective method based on cosine similarity instead of a subjective evaluation of synthesized movement. The proposed method resulted in a 96% similarity.