# Study on Elevator Recognition Techniques for Upper-Limb-Disabled Wheelchair Users
## 1 Introduction  
According to the World Health Organization (WHO), there were about 1.3 billion disabled people in the world as of 2022, accounting for about 16% of the total population, including people who have difficulty walking and require the use of a wheelchair [1, 2]. There are two main types of wheelchairs: manual wheelchairs that move by pushing the pushrim directly by hand, and electric wheelchairs that enable movement through use of a controlling joystick [3, 4]. Users of manual wheelchairs may experience lack of endurance in arm muscle strength or difficulty driving on ramps during long-distance driving. On the other hand, electric wheelchairs are suitable for users who have difficulty driving due to lack of arm strength and endurance because they enable movement by control of a controller joystick using a small force [4, 5]. However, according to an in-depth survey of patients with emergency room injuries caused by electric wheelchair accidents, 93.6% of 407 people surveyed experienced collisions [6]. The main cause of collision accidents with obstacles was the user's inexperience in manipulation and cognitive decline. The biggest reason for accidents caused by wheelchair manipulation errors or inadequate operations is the failure to respond appropriately in unexpected situations. Therefore, among wheelchair users, those who suffer from severe disabilities are exposed to the risk of accidents resulting from upper limb physical function decline. The reason is that the most severely disabled people with impaired upper limb physical function have symptoms such as shaking, stiffness, and weakness of the arms or hands, and these symptoms can cause problems in wheelchair manipulation [7]. In the past, research has focused on improving the operation of electric wheelchairs for people who have difficulty with operating joysticks, but with the development ofautonomous driving technology, researchers are trying to apply this technology to electric wheelchairs. However, although autonomous driving technology is effective where there is a structured map, there is still a problem of safety in areas where the nature of the space changes, such as moving between floors and riding an elevator. Therefore, in this paper, we propose a technique for elevator door recognition and doorway width estimation for wheelchair entry in an autonomous wheelchair system to ensure that a wheelchair can safely enter the elevator. Recent studies on elevator recognition include those examining depth-camera-based elevator recognition and strong elevator door recognition using an LRF (laser range finder) and cameras [8; 9; 10; 11; 12; 13; 14; 15]. A study on depth-camera-based elevator recognition suggested a method of finding the exact location of the door using forward-oriented, three-dimensional distance data and particle filters for incorrect initial location of the elevator door. The experiment findings suggested that the average position error and angle error between the actual door position and the recognized position were 8 cm and 2\({}^{\circ}\) or less, respectively [8]. In the case of elevator recognition studies using an LRF and cameras, in one study, a camera-image-based door verification process was added to the existing method, which used an LRF only, producing a high elevator door recognition rate of 96.7% when looking at the elevator from the front [11]. However, existing elevator recognition studies only consider a position where the elevator can be viewed directly from the front due to the angle of view range of the sensor. This requires driving and aligning to a position where the user directly faces the front to perform elevator recognition. However, it is not appropriate for an inexperienced wheelchair user to drive from the location of the call switch to the location and correct angle for boarding the elevator. Electric wheelchairs are divided into rear-wheel-, front-wheel-, and mid-wheel-drive types, with the center of rotation located at the rear, front, and middle, respectively.