## 5 Experimental Results  
### LMA and Statistical Machine Learning  
Laban Movement Analysis (LMA) is particularly useful for analyzing dance performance. By describing a dancer's movements in detail and translating them into specific parameters, it is possible to quantitatively assess performance characteristics; LMA parameters quantify the quality and characteristics of the movements, which can then be used as a basis for analysis in machine learning models. The LMA has four attributes as its main components: the effort attribute for movement quality, the shape attribute for movement shape, the space attribute for movement direction, and the body attribute for body parts. Effort represents movement dynamics and emotional expression. The space attribute represents the space between the body part and the dynamics of the movement. For example, performing the same movement forcefully, quickly, or fluidly changes the emotion or intent of the movement and creates a different emotional impression on the observer. This includes weight, space, time, and flow, which are elements used to capture the intensity, rhythm, direction, and continuity of movement. Shape captures changes in body posture and shape. Space represents how movement is oriented in space. This includes the direction, path, and relationship of movement. Body indicates which body part a particular movement is performed by. A method for extracting dance features based on LMA was proposed in a previous study, Analysis and Evaluation of Dancing Movement Based on LMA [2]. In the present study, we implemented a method to compute each of the following four effort attributes based on that previous study. Weight is calculated using velocity information from the center of the body (hips) and extremities (ankles and wrists). Time is calculated from the acceleration information of the \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Weight** & **1** & **2** & **3** & **4** & **5** \\ \hline
1 & 1.0000 & 0.9375 & 0.7500 & 0.4375 & 0.0000 \\ \hline
2 & 0.9375 & 1.0000 & 0.9375 & 0.7500 & 0.4375 \\ \hline
3 & 0.7500 & 0.9375 & 1.0000 & 0.9375 & 0.7500 \\ \hline
4 & 0.4375 & 0.7500 & 0.9375 & 1.0000 & 0.9375 \\ \hline
5 & 0.0000 & 0.4375 & 0.7500 & 0.9375 & 1.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Example of quadratic weighted kappa weights. \begin{table}
\begin{tabular}{l l} \hline \hline  & **Kappa Score** & **Interpretation** \\ \hline \(-1.0\) & Perfect mismatch \\ \hline
0.0 & Match by chance \\ \hline
0.01–0.20 & Slight match \\ \hline
0.21–0.40 & Generally match with \\ \hline
0.41–0.60 & Moderate match \\ \hline
0.61–0.80 & Fairly close match \\ \hline
0.81–0.99 & Almost match \\ \hline
1.0 & Perfect match \\ \hline \hline \end{tabular}
\end{table} 
Table 5: General criteria for kappa score. dancer's movements. Space is calculated from the dancer's gaze direction (face direction) and body direction (hip direction), and its relative angle is used as the feature value. This allows the spatial development and directionality of the dance to be analyzed. Flow assumes the entire body as a rectangular body and uses the amount of change as a feature quantity, indicating how much it varies along the left-right, up-down, and front-back directions. This allows the continuity and fluidity of movement to be evaluated. The above LMA-based features are expressed as time-series data, which makes it possible to capture temporal changes in movement. As statistics, the mean, variance, maximum, and minimum values are calculated, and they are used as the final feature values. This statistical approach enables a more comprehensive evaluation of dance by capturing its consistency and variability. Statistical machine learning is a method of machine learning that exploits the statistical properties of data to learn patterns to make predictions and decisions. Random forest, one of the statistical machine learning models, is a widely used ensemble learning method that combines many decision trees.