# An Intelligent Error Correction Algorithm for Elderly Care Robots
## 3 Intelligent Error Correction Algorithm
### Reasons for Low Recognition Rate  
The main goal of this section is to explore the reasons for the low recognition rate of gestures at the behavior level. The main method is to select the gesture with a low recognition rate through the probability statistics method and record the corresponding gesture error recognition and the probability of error recognition. Before that, to better understand the behavioral characteristics of gestures of the elderly better, our research group paid a special visit to the local nursing home, studied the characteristics of gestures of the elderly, and listed several significant characteristics different from young people, as shown in Table 1.  
Based on the characteristics of the palms of the elderly summarized in Table 1, this study conducted experiments on several commonly used gestures in our life and created recognition probability statistics. Seven gestures of 30 elderly people were photographed and recorded, and 3000 photos were recorded for each gesture. Finally, we reached the probability of correct gesture recognition in practical use through probability statistics, as shown in Table 2.  
It can be seen from Table 2 that some features of the palms of the elderly reduce the recognition rate of the gesture recognition algorithm, which makes the robot think that the elderly have made wrong gestures. According to Table 2, we need to select the gestures with a recognition rate of lower than 85%, select the gestures that recognize errors in the statistical process, establish the misread-gestures database, and then analyze the misread gestures. Because the network training is completed under supervised learning, even if there are recognition errors, the recognition result is in the range of 00 to 06. Therefore, we found that one gesture is often incorrectly recognized as another gesture, as shown in Table 3.  
Line 00 and column 01 represent the probability that the recognition gesture category number is 00, but the actual input gesture category number is 01. Due to the non-standard \begin{table}
\begin{tabular}{c c} \hline \hline
**Gestural Characteristics of the Elderly** & **Gesture Characteristics of Young People** \\ \hline
1: Fingers bend naturally & 1: Fingers are naturally straight \\
2: Constant shaking of the palm & 2: The palm of a hand is steady \\
3: Fingers have no power & 3: Fingers have high power \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of gesture characteristics between the elderly and the young. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Signal Types** & **Fist (00)** & **Thumps Out (01)** & **Index Finger Out (02)** & **Little Finger Out (03)** & **Fingers Merger (04)** & **Five** (05)** & **Five** (06)** \\ \hline Elderly (\%) & 98.1 & 76.4 & 82.4 & 75.4 & 97.2 & 72.7 & 98.1 \\ Young (\%) & 98.4 & 90.3 & 91.2 & 96.7 & 97.3 & 96.7 & 98.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Gesture recognition rate. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Actual Number**} \\ \hline
**Predicted Number** & **00** & **01** & **02** & **03** & **04** & **05** \\ \hline
**00** & 0.805 & 0.103 & 0.008 & 0.084 & 0 & 0 \\
**01** & 0.016 & 0.765 & 0.105 & 0.114 & 0 & 0 \\
**02** & 0.001 & 0.108 & 0.888 & 0.003 & 0 & 0 \\
**03** & 0.002 & 0.012 & 0.074 & 0.872 & 0 & 0 \\
**04** & 0 & 0 & 0 & 0 & 0.781 & 0.219 \\
**05** & 0 & 0 & 0 & 0 & 0.037 & 0.962 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Gesture misreading rate. gestures of the elderly, the robot mistakenly recognizes gesture 01 as 00. From Table 2, we can see that the recognition rate of 00 gestures is 98.1%. However, it still needs to be listed in Table 3, because 01, 02, and 03 gestures may be misrecognized as 00 gestures. This indicates that when the recognition result is 00, the recognition may be wrong because the actual input gesture number may be 01 or 02 or 03. The 04 gestures are listed in Table 3 for the same reason.