## 4 Experiment  
The purpose was to secure a selection factor for the state data set for training to improve the communication function of the communication-weak by combining the previously developed technology with the training contents under development, and to verify the validity of the non-contact biometric data collection and analysis technology. The test group is the target of 8 people with weak communication and 14 people in the control group as shown in Table 1. The criterion for selecting a group of people with communication weakness is adolescents and adults aged 13 to 40 years old. The comparative group is a person who voluntarily agreed to participate in the study after reading the study guide and consent to participate in the study for adolescents and adults aged 13 to 40 years old. Contents consist of Music based Attention Test (MAT) and Comprehensive Attention Test (CAT). Observation items are contact and non-contact optical blood flow signals/respiration signals, facial features, and facial expression recognition. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Category** & **Comparative Group** & **Control Group** & **Total** \\ \hline \multirow{2}{*}{Gender} & Male & 3 & 8 & 11 \\  & Female & 11 & & 11 \\ \hline \multirow{4}{*}{Age} & 12–15 & 1 & 2 & 3 \\  & 16–19 & 5 & 4 & 9 \\  & 20–24 & 6 & 1 & 7 \\  & 25–29 & 2 & 1 & 3 \\ \hline \multirow{4}{*}{Education} & Junior Highschool & 2 & 2 & 4 \\  & Attending Highschool & 4 & 2 & 6 \\ \cline{1-1}  & Graduated Highschool & 1 & 2 & 3 \\ \cline{1-1}  & Attending University & 5 & 2 & 7 \\ \cline{1-1}  & Graduated University & 2 & & 2 \\ \hline \multirow{2}{*}{Disability} & ASD & 2 & 2 \\ \cline{1-1}  & ID & 6 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Basic information of research participants. Figure 26: Implementing hand movement tracking interaction. Status data was collected and analyzed based on the face images of individuals with. ASD through a webcam or front camera in a PC or tablet environment in which the content is driven. The participants of the experiment wore ECG and EMG sensors, and were conducted in an environment of 200 lux or more of illumination. Tables 2 and 3 compare ECG and EMG sensor data with heart rate and respiration data acquired through non-contact biosignal measurement technology. With the subject sitting in a chair, the distance between the subject and the camera was about 60 cm, and the heart rate measurement data was acquired from the subject's face image, and the accuracy was calculated by sampling at 6 s intervals. In simple numerical terms, the difference is 1.27 in heart rate and 0.29 in respiration on average, and the RMSE (Root Mean Square Deviation) is less than 2 in heart rate and less than 1 in breathing. Compared to the conventional contact collection method, it was verified in Tables 2 and 3 that our non-contact technology shows competitive results. In Table 4, the expression recognition rate of the subjects was calculated through (3) for 6 types of expressions (joy, surprise, disgust, sadness, fear, neutral) by comparing the DISFA dataset [36] with the subject's face image. \[\text{ACC}=\text{ R}\times 100,\text{ R}=\frac{T}{T+F},\text{ }(0\leq\text{ R}\leq 1),\] \[\text{T}=\text{Number of successful recognition}, \tag{3}\] \[\text{F}=\text{Number of failed recognition}\]  
For each of the six expressions, the test was performed 100 times, and the accuracy of 95.7% was verified in the expression recognition rate with 574 times of recognition and 26 times of false recognition. Table 5 is the determination of the measurement accuracy for the gaze (face direction) and hand interaction. Three interaction areas were selected in consideration of the camera angle of the environment using the tablet and to characterize that precise interactions of the people with ASD. The screen size was based on 640 \(\times\) 480, and the accuracy was determined for the following three areas.