A computerized multidimensional measurement of mental workload via handwriting analysis

Changing handedness after nerve reconstruction in brachial plexus birth palsy

Purpose

Right obstetric brachial plexus injuries (OBPI) often lead to left-handedness before limb function is restored post-surgery. A pertinent question arises about promoting a transition from left to right-handedness. We hypothesized that, with the decrease in neuroplasticity, handedness switching is not only difficult, but also reduces handedness-speech lateralization, impaired motor adaptability, and compromised language proficiency.

Methods

We retrospectively analyzed clinical data from January 1996 to January 2012 at our hospital. Participants were divided into intervention or control groups based on handedness switching. We compared handedness and computed lateral quotient (LQ) and lateralization index (LI) for handedness-speech center. Additionally, we assessed dominant hand's writing speed, language function, and IQ. Associations between absolute LI and LQ values, writing speed, language scores, and IQ were examined.

Results

Nineteen extended Erb's palsy participants were enrolled, eight in the intervention group, and 11 in the control. No right-handed individuals were found in either cohort. The intervention group had significantly lower LQ and LI values, and fewer achieved normal writing speed. Yet, no notable disparities in language scores or IQ emerged. Notably, we established correlations between motor finesse, handedness degree, and handedness-speech lateralization.

Conclusion

For right extended Erb's palsy, shifting handedness is nearly unfeasible, and such an endeavor could trigger a reduction in handedness-speech lateralization magnitude and diminished motor finesse.

The uulmMAC Database-A Multimodal Affective Corpus for Affective Computing in Human-Computer Interaction

In this paper, we present a multimodal dataset for affective computing research acquired in a human-computer interaction (HCI) setting. An experimental mobile and interactive scenario was designed and implemented based on a gamified generic paradigm for the induction of dialog-based HCI relevant emotional and cognitive load states. It consists of six experimental sequences, inducing Interest, Overload, Normal, Easy, Underload, and Frustration. Each sequence is followed by subjective feedbacks to validate the induction, a respiration baseline to level off the physiological reactions, and a summary of results. Further, prior to the experiment, three questionnaires related to emotion regulation (ERQ), emotional control (TEIQue-SF), and personality traits (TIPI) were collected from each subject to evaluate the stability of the induction paradigm. Based on this HCI scenario, the University of Ulm Multimodal Affective Corpus (uulmMAC), consisting of two homogenous samples of 60 participants and 100 recording sessions was generated. We recorded 16 sensor modalities including 4 × video, 3 × audio, and 7 × biophysiological, depth, and pose streams. Further, additional labels and annotations were also collected. After recording, all data were post-processed and checked for technical and signal quality, resulting in the final uulmMAC dataset of 57 subjects and 95 recording sessions. The evaluation of the reported subjective feedbacks shows significant differences between the sequences, well consistent with the induced states, and the analysis of the questionnaires shows stable results. In summary, our uulmMAC database is a valuable contribution for the field of affective computing and multimodal data analysis: Acquired in a mobile interactive scenario close to real HCI, it consists of a large number of subjects and allows transtemporal investigations. Validated via subjective feedbacks and checked for quality issues, it can be used for affective computing and machine learning applications.