# An Intelligent Error Correction Algorithm for Elderly Care Robots
## 4 Experimental Results and Analysis
### Experimental Methods  
To verify whether the error-correction algorithm can improve the gesture recognition rate, this paper designed a tea-drinking escort prototype system combined with the error-correction algorithm. It is hoped that the elderly can send instructions to the robot through gestures, and the robot will perform corresponding operations after recognition. Finally, the robot will help the elderly finish the activity of drinking tea. A daily family living environment was simulated in the laboratory, which included a round tea table, chair with back, teacup, tea, kettle, and other tea service necessities. The detailed scene diagram is shown in Figure 6.  
In the experimental operation method, this study also added other channel information, such as speech recognition and target detection. The purpose was to improve the real experience of the elderly experiment and improve the authenticity and reliability of the experimental data. The main operation was still that the elderly gave hand instructions to the robot through hand gestures. For example, when the elderly made gesture 01, they could command the robot to turn 90\({}^{\circ}\) to the left. In addition, when the elderly made gesture 07, they could command the robot to take the cup. In this process, we added the function of target detection. Only when the robot detected the cup would it execute the command to take the cup. The experiment was composed of 20 old people. They needed to make seven gestures to experience the tea service. Each gesture was tested 200 times. Some gesture instructions are shown in Table 4.  
In this experiment, the whole tea-drinking service scene is divided into several steps. The start switch of each step is the corresponding gesture. That is to say, only when the \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Some Important Gesture Instructions in Tea Service Experiment**} \\ \hline Experimenter: & Make gesture 01 (left turn command). \\ [PERSON]: & The robot turns left 90\({}^{\circ}\) without obstacles on the left. \\ Experimenter: & Make gesture 03 (right turn command) \\ [PERSON]: & The robot turns 90\({}^{\circ}\) to the right without obstacles on the right side. \\ Experimenter: & Make gesture 05 (forward command). \\ [PERSON]: & If there is an obstacle ahead, the robot will stop automatically to ensure absolute safety. \\ Experimenter: & Make gesture 06 (take the cup command). \\ [PERSON]: & The robot determines that the cup is in front of the robot through target detection, and then performs the \\  & grab operation. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Instructions of experimental operation. Figure 6: Schematic diagram of the experiment scene. robot's gesture recognition is correct can the corresponding service be started. On the contrary, if a certain step is wrong, the whole service will not be completed:  
* The experimenters should interact with natural gestures as in daily life, and the speed should not be too fast;
* The experimenters only make gestures related to tea drinking service to avoid affecting the experiment time;
* After each gesture instruction, the experimenter should make the second gesture instruction after the robot finishes;
* The experimenters conducted ten tea service experiments based on a behavior-mechanism-error-correction algorithm and tea service experiments based on a robot cognitive-error-correction algorithm. The purpose of the experiment is to verify whether the algorithm achieves the expected goal. Therefore, it is necessary to show the authenticity of the experimental process through on-site photos, and to illustrate the effect of the algorithm through the number of errors found and the number of errors corrected. In this study, two counters were added to the experimental code to record the number of errors found and the number of errors corrected, respectively. Readers may misunderstand \"discovery error,\" and think that as long as the recognition result number is in the error gesture library number, the error is found, but it is not.