# Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror
## 1 Introduction  
Thanks to the development of AI technologies in recent years, simple tasks can be done by computers. As a result, tasks that are too simple and time-consuming for humans are being done by computers. One such task is motion monitoring/surveillance, as it is inappropriate for a human to manage and monitor multiple CCTVs because it requires a long period of concentration. With the advent of automated surveillance systems, several algorithms have appeared in automated detection.  
Examples of automated surveillance systems include systems that detect falls [1] or the gait of passers-by [2]. Each of these systems is based on its own movement detection algorithm, some of which are based on pose estimation models. The pose estimation model has weaknesses such as natural error and detection error, which can make the false movement of the target user's joint point regardless of the target user's actual movement. This weakness does not usually make a problem because the amount of such error is small enough to deal with large movement from some distance. However, it can cause a problem in dealing with a slow-moving target at a near distance.  
In this study, to enhance the effectiveness of smart mirrors for the well-being of the elderly, we attached a camera to a smart mirror system that detects the movement of the elderly at a near distance. In this case, the elderly can often make slow motions and motion detection is made at a near distance. Therefore, the weakness of the motion detection system made by natural error becomes critical.  
As the human lifespan and the population of the elderly are continuously increasing [3], the detection of slow-motion should not be overlooked. The population of the elderly age group is growing at a faster rate than other age groups [4]. Our study aims to implement a health management system for the elderly with consideration for their lack of mobility.  
In [5; 6; 7], the authors suggest that regular exercise, such as a national exercise program, could have a positive impact on their health, especially for the elderly. However, as their physical ability declines, it is recommended that they should avoid strenuous exercise [8]. Moreover, as humans age, their mobility and speed tend to decrease. In [9], the mean gait speed of men aged 40 to 49 years was 143.4 cm/s, whereas, for women aged 80 to 99 years, the mean was 94.3 cm/s. Thus, targeting movements in the proposed system mainly includes walking and a national exercise program and excludes single joint movements such as lifting dumbbells.  
By increasing the motion detection threshold, natural errors could be easily resolved, but another problem occurs when detecting slow movements by traditional methods, which use a threshold value to detect movement. If a target user's speed is too slow, the motion may not be detected. In this study, dealing with such a problem is our goal. The Smart mirror environment will be explained in Section 3 and the proposed algorithm will be introduced in Section 4. Experiments on the weaknesses of the previous pose estimation model and the effectiveness of the proposed method will be presented in Section 5.