## 2 System Architecture  
The architecture of the proposed hand gesture recognition system is shown in Figure 1. The wearable wrist-worn RGB camera device, WwwCam, is worn on the backside of the user's right wrist to shoot images of the hand, which are then sent to the computer through Wi-Fi. The computer recognizes the hand gesture and applies it to smart-home applications such as lighting a lamp, switching TV channels, and controlling domestic robots. Gestures used in this system are static gestures, which are defined by the status of each finger and can be inferred from a single image, such as gesture \"Five\" with five spread fingers, and gesture \"Nine\" with three fingers, as shown in the two small pictures in Figure 1.  
The configuration of WwwCam is displayed in Figure 2. An RGB monocular camera is fixed on a holder and worn on the wrist through a flexible wristband. Due to the close distance between the camera and the hand, the camera with a wide camera angle of 150 deg was selected so that the hand could fall into the camera view. The camera is then connected to a Raspberry Pi Zero W through USB cable. This Raspberry Pi is a powerful microcomputer with a BCM2835 chip of 1 GHz single-core CPU and a BCM4348 chip for 2.4 GHz 802.11n wireless communication. There might be other options for the microprocessing unit, such as Arduino. However, the clock frequency of the Arduino is usually less than 0.1 GHz with limited capability for the processing of complex tasks such as image processing. Moreover, the Raspberry Pi Zero W with a Linux operating system has drawn considerable interest  
Figure 1: The pipeline of the proposed hand gesture recognition system using wearable wrist-worn camera (WwwCam) for smart-home applications.  
from researchers, due to its well-developed drives for the peripheral modules like cameras, a Wi-Fi module for various communications, as well as a relatively small size of 65 \(\times\) 30 \(\times\) 5.1 mm\({}^{3}\). Therefore, the Raspberry Pi was chosen as the processor of the device.  
We installed a Linux system called Raspbian on the Raspberry Pi and programmed on it using Python. The program reads out the video streaming from the camera and transmits it to the laptop through Wi-Fi by TCP protocol using the \"socket\" library. This process is a simulation of the transmission of video from the wearable device to the computer in a smart-home using domestic Wi-Fi environment. Then, on the laptop, a mathematical software, MATLAB, receives the video and runs the hand gesture recognition algorithm.  
The wearable device is supplied by a 5 V power bank with a capacity of 2200 mAh. The Raspberry Pi consumes an average current of 0.30 A when reading and transmitting the video, and consumes 0.11 A at the idle state. Thus, when the user continuously uses it to send gesture commands, the device could be running for more than five hours. Several approaches can be applied to further extend the battery life, such as using the cell with a larger energy density, reducing the frequency of image acquisition, and optimizing the software to keep the device in sleep mode when the user is not using it.  
The wristband and the support of the camera are adjustable, so the camera can be set to the proper position and orientation to have a good view of the hand-- this is: when five fingers are stretched out: (1) the middle finger is at about the middle of the figure; (2) the highest fingertip is above the 1/2 image height; and (3) the wrist is at the bottom of the image. A feasible position can be seen from Figures 1 and 2.