## 4 Results  
### Experimental Environment  
In this section, the environment used for the proposed method is presented. In this virtual environment, C# and Unity 3D Rendering Engine were used for language and software. In the XR rehabilitation simulation, a multi-environment using two or more HoloLens2 devices was configured. In the extended reality environment, 6 DoF coordinate axes were used. Communication between the Python server and the Unity Client uses the TCP protocol. Data measurement was performed using Azure Kinect and Intan RHD 2216. Both devices were used to capture and visualize lower extremity motion figures and EMG signals used in rehabilitation software. Table 2 below shows the experimental environment.  
The Signal Processing section measures the EMG signal from the amputated sited and emitted by the amputee patient via the connected an EMG sensor and 3 electrodes. The measured EMG signal is transmitted to the Raspberry Pi to determine whether it is a motion signal or not, and the signal is classified as 0 or 1. The communication between RHD 2216 Arduino Shield and Raspberry pi 4 is serial communication using USB b to a. If there is a movement of the leg muscles, the signal is processed. Signals with motion among the classified signals are transmitted to the Unity 3D Rendering Engine via TCP communication. The configuration of the device to acquire the EMG signal is as follows. It consists of Intan's RHD2216 Arduino Shield, Arduino Uno, and Raspberry PI4. The EMG signal was acquired at the amputated site of the amputee patient and recorded with 1 channel of the surface electromyography signal. The inputs to the on-chip ADC is 0.10 V\(-\)2.45 V Range. In the acquisition system, the number of samples per time is 1 kHz and the resolution is \(\pm\)4.4 peak to the peak. A 60 Hz notch filter was used to remove power noise during signal acquisition. In the low pass filter, the butterworth filter is used and the cut off at 5 Hz. The high pass filter is cut off at 75 Hz. Signals with a weaker intensity than the threshold are classified as'static'. Equation (6) is an equation of a threshold value set for each patient with amputation of the lower extremity.  
\[Threshold=\sqrt{\frac{\sum(x_{i}-\overline{x})^{2}}{n}}\ \times r,\ r=20 \tag{6}\]  
The threshold value is set by multiplying the standard deviation value of the raw EMG signal average by 20. The number of parameter inputs per layer in the Bidirectional LSTM model is 108,422. Real-time prediction of raw signals up to 65,000 Hz per second is possible. The hyperparameters of the deep learning model used in the experiment are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **eXtended Reality** \\ \hline \hline \multirow{2}{*}{Device} & HoloLens 2 \\  & Raspberry Pi 4 Model B \\  & Azure Kinect \\ \multirow{2}{*}{Measuring Device} & Intan RHD 2216 \\ Language & C\# \\ Software & Unity (2020.3.8) \\ Number of Axes & 6 Degrees of Freedom \\ \multirow{2}{*}{OS} & Windows Holographic \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental Environment.  
For the training parameters, the optimizer was set to Adam, epoch to 100, and batch size to 200. The length of the data to be trained and inferred was set to 64. The initial learning rate was 0.005, and the scheduler used Polynomial Decay. The dropout is 0.5. The hardware specifications and deep learning frameworks used for learning the deep learning model are shown in Table 4.  
In this experiment, the dataset used for learning, verification, and evaluation was conducted 100 times with 5 normal groups and 5 cut groups who received test consent from the mixed reality rehabilitation simulation system. Data acquisition was done for a total of 4 s with 2 s of'static state' and 2 s of 'dynamic state'. The number of data used for learning and evaluation of the deep learning model is shown in Table 4.