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Introduction: Musicians often perform in forced postures over a long period of time, which in the worst case may lead to playing-related musculoskeletal disorders. In this context, the ergonomics of the musician's chair (construction and surface quality) can be an influencing factor, with impact on the seating position of the upper body and the pressure distribution of the bottom. Therefore, the relationship between different musician chairs and musicians of different playing levels (professional, amateur or student) was analyzed in order to gain useful insights whether playing experience, playing level, playing style (symmetrical or asymmetrical) or gender have an impact.
Method: The total dataset of 47 musicians (3 playing levels: professional, amateur, student) were analysed on six musician chairs with different ergonomic layout. Sitting on each chair without instrument (condition 1) and with instrument (condition 2), the upper body posture (videorasterstereography) and the seat pressure (load distribution) were recorded.as Also, a subjective assessment concerning constitutional data, sitting behaviour, prevailing pain in the musculoskeletal system, sport activity and chair comfort rating, was completed using a questionnaire.
Results: There were significant differences shown in 6 of 17 variables, where all between and within factors were accounted for with a MANOVA. Two measurements of the upper body posture (scapular distance and scapular height) differentiated between playing level. Four of the pressure measurements (pressure under the sit bone and the thigh for the left and the right side) differentiated between chairs and the two conditions (with and without instrument). Chairs with soft cushioning had a mean pressure reduction of about 30%. The pressure was increased by about 10% while playing an instrument. Subjective rating was correlated to age for some of the chairs.
Discussion: Differences between chairs are mainly associated with the pressure distribution under the sitting surface. Playing with an instrument puts an additional force onto the surface of the chair that is more than the weight of the instrument. No relationship between pressure data and upper body posture data could be found. Therefore, it can be speculated that the intersubject variability is larger than systematic differences introduced by the chair or instrument.
In psychotherapy, movement synchrony seems to be associated with higher patient satisfaction and treatment outcome. However, it remains unclear whether movement synchrony rated by humans and movement synchrony identified by automated methods reflect the same construct. To address this issue, video sequences showing movement synchrony of patients and therapists (N = 10) or not (N = 10), were analyzed using motion energy analysis. Three different synchrony conditions with varying levels of complexity (naturally embedded, naturally isolated, and artificial) were generated for time series analysis with windowed cross-lagged correlation/ -regression (WCLC, WCLR). The concordance of ratings (human rating vs. automatic assessment) was computed for 600 different parameter configurations of the WCLC/WCLR to identify the parameter settings that measure movement synchrony best. A parameter configuration was rated as having a good identification rate if it yields high concordance with human-rated intervals (Cohen’s kappa) and a low amount of over-identified data points. Results indicate that 76 configurations had a good identification rate (IR) in the least complex condition (artificial). Two had an acceptable IR with regard to the naturally isolated condition. Concordance was low with regard to the most complex (naturally embedded) condition. A valid identification of movement synchrony strongly depends on parameter configuration and goes beyond the identification of synchrony by human raters. Differences between human-rated synchrony and nonverbal synchrony measured by algorithms are discussed.
Motivation: Calculating the magnitude of treatment effects or of differences between two groups is a common task in quantitative science. Standard effect size measures based on differences, such as the commonly used Cohen's, fail to capture the treatment-related effects on the data if the effects were not reflected by the central tendency. The present work aims at (i) developing a non-parametric alternative to Cohen’s d, which (ii) circumvents some of its numerical limitations and (iii) involves obvious changes in the data that do not affect the group means and are therefore not captured by Cohen’s d.
Results: We propose "Impact” as a novel non-parametric measure of effect size obtained as the sum of two separate components and includes (i) a difference-based effect size measure implemented as the change in the central tendency of the group-specific data normalized to pooled variability and (ii) a data distribution shape-based effect size measure implemented as the difference in probability density of the group-specific data. Results obtained on artificial and empirical data showed that “Impact”is superior to Cohen's d by its additional second component in detecting clearly visible effects not reflected in central tendencies. The proposed effect size measure is invariant to the scaling of the data, reflects changes in the central tendency in cases where differences in the shape of probability distributions between subgroups are negligible, but captures changes in probability distributions as effects and is numerically stable even if the variances of the data set or its subgroups disappear.
Conclusions: The proposed effect size measure shares the ability to observe such an effect with machine learning algorithms. Therefore, the proposed effect size measure is particularly well suited for data science and artificial intelligence-based knowledge discovery from big and heterogeneous data.