## 1 Introduction  
Smart infrastructure has become an attractive research field in recent years. It integrates technologies of sensing, control, and Internet of Things (IoT) to provide a more secure, efficient, and convenient living space for the residents. While energy management [1] and the IoT network [2] are some of the research aspects, there is another growing trend of deploying domestic robots in smart infrastructure to enhance the quality of living and working. Personal robots that do laundry or floor cleaning would reduce the housework burden, and assistive mobile robots can help the elderly and disabled to live with more ease and security [3]. To interact with these robots, a natural and intuitive interface is needed for interpreting human's intention. Hand gesture recognition is just the appropriate tool, as we naturally use our hands to convey information while communicating with others [4]. In previous research, hand gesture recognition has been applied to many smart-home applications, including the control of appliances like lamp [5] and TV [6], interaction with computer games [7], and robot control [8]. Hand gestures can be categorized into two types: static and dynamic gestures. The former is generally defined by a static posture of the hand and fingers, and the latter is formed by the dynamic movement of the hand and arm. Though non-wearable camera system can recognize both types of gestures [9], it suffers from a restricted working region that the user has to stand in front of the camera. In contrast, the problem can be solved by the thriving technology of wearable IoT [10]. Using a wearable camera, the user can walk from room to room or even going outdoors, while keeping the capability of sending gesture commands. Using wearable camera for hand gesture recognition has many advantages over other types of sensors such as surface electromyography (sEMG) and inertial sensors. Though sEMG sensors can detect useful signals from muscle activities [11], there remain some open questions about its sensitivity to different subjects and different mounting positions. Inertial sensors mounted around user's wrist [12] can obtain the posture and acceleration of the hand to recognize dynamic gestures. However, when it comes to static gestures, a set of bulky sensors need to be placed at each finger [13] to know the fingers' bending condition. Whereas for the wearable camera, since both the hand and background images can be captured by the camera, it is promising to achieve recognition of both static and dynamic gestures. Moreover, through the analysis of background image, it also provides other smart-home usages such as text tracking for the blind [14], diet monitoring of the elderly [15], and recognizing daily activities [16] to ensure the security of the elderly who stay alone at home. Several vision-based wearable systems have been developed in past years for hand gesture recognition. [PERSON] et al. [17] analyzed the goals and feasibility of using wrist-worn RGB camera. [PERSON] et al. [18] used infrared sensors beneath the wrist to detect whether a finger is bending. Later, [PERSON] et al. [19] utilized a combination of infrared LED and infrared camera beneath the wrist to calculate the bending angle of each finger. However, placing a large device beneath the wrist can be intrusive when people use a mouse on the table or stand straight where the underside of the wrist naturally contacts with the table surface or leg. [PERSON] et al. [20] directly placed a smartphone on the back of the wrist, whose hardware is too cumbersome and the software only detects gestures formed by the index finger and middle finger. [PERSON] et al. [21] used Google Glass on the head to recognize in-air hand gestures, but the user has to look at their hands all the time, which is inconvenient. A more thorough investigation on wearable cameras is needed to make it more user-friendly and convenient. Many algorithms have been proposed to recognize hand gestures from the image.