# Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror
## 4 Movement Detection Algorithm for the Elderly in Smart Mirror
### Pose Estimation Model and Its Limitation  
As shown in Table 1, There are two general limitations of a skeleton-based pose estimation model and another limitation in movement detection that uses the pose estimation model.  
1. Natural error is a small error that occurs in a stopped situation. When a target is static, all of the target's joint points are moving a little. This is a basic limitation of the pose estimation model; in this study, non-intentional movement will be included as a natural error. Non-intentional movement is negligible physiological movement, such as breathing action.
2. Detection errors are divided into two types, (2-1) whole point error and (2-2) partial point error. A whole point error is an error in which all the point's coordinates are wrong, whereas a partial point error is an error in which the coordinates of a single or a couple of points are wrong.
3. Slow movement can be detected as a movement if the threshold is set very low, but then the system may miss ignoring the natural error and detect movement in a stopped situation. We manage these errors in this session.  
The problem with the current model is that the points move even when a person is in a stationary state and there are several types of detection errors. There are several reasons why a point moves even if the subject stands still. First, it is difficult to specify a single joint coordinate in that joint area. For example, it is not possible to accurately specify the wrist coordinate in the wrist area. Because the image changes slightly with every frame, the coordinates of the representative joints are not fixed, but they are definitely present in the joint area. Second, it is a dilemma in a human motion detection system. Consider a robotic police officer saying \"freeze\" to a suspect who is raising both hands in surrender. Should the system detect the unintended micro-movement of an arm tremor as a movement even if the target keeps freezing? Humans may try to stay motionless but cannot stay strictly motionless as they breathe.  
Pose estimation has another problem in detecting targets. It is an accuracy performance issue for the pose estimation model. Detection error can be divided into two types, as shown in Figure 3: whole point error and partial point error. A whole point error is an error in which all the point's coordinates go wrong. A partial point error is an error in which the coordinates of a single point, or a couple of points are wrong, while the other points are in  
\begin{table}
\begin{tabular}{l l} \hline
**Error Type** & **Description** \\ \hline Natural error & Small error that occurs in a stopped situation. \\  & Target is static, but all of the target’s joint points are moving slightly. \\  & Non-intentional movement, negligible physiological movement. \\ Whole point error & All points detected at the wrong position. \\ Partial point error & Single/couple of points continuously detected at the wrong position. \\ Slow movements & Target has moved, and its displacement is close to a threshold value. \\ \hline \end{tabular}
\end{table}
Table 1: Three limitations. Two limitations of the skeleton-based pose estimation model, another limitation in the movement detection system that uses the pose estimation model. Natural error and slow movement are closely related.  
a tolerable place. Whole point error does not appear sequentially or frequently, while point error appears in almost every frame of a video with a high error rate.  
In the current system, Movenet [22] is used as a pose estimation model. Its detection error rate was below 15% per frame for the whole point error and less than four joint points per frame for partial point error. Movement detection in the proposed system includes walking and basic freehand exercise for the elderly. As we excluded such actions, which were only single-joint moves, the following method will not encounter such a problem. In addition, using the majority result of recent frames may be a solution for managing whole point error.