## 1 Introduction  
For some people with limb problems, it is impossible to have accurate control over hand movement. Common assistive devices for them include a head-controlled control stick-shaped device, mouth-held stick device, and mouth-controlled blow device. However, these assistive tools are not sanitary, comfortable, or convenient because users have to wear or touch some mechanical sensing devices [1; 2; 3]. Given the inconvenience of the existing assistive systems for the disabled, a system, featuring a combination of computer-based vision technology and movement detection, was developed [4; 5; 6]. Based on information about the head, it is a household control system for the disabled, and includes color segmentation and head recognition [7].  
When the head is held straight without any deflection, the eyes are almost on the same horizontal line; when the head tilts right or left, the angle between the line linking the canthi of the eyes and the horizontal line changes. Therefore, a tilt of the head can be judged according to the angle. There are many algorithms and configurations for head-control devices and sensing human activities [8]. In an image-type head gesture control device, a CCD (charge-coupled device) camera is used to detect the position of the head without influence from the background. Head movements can also be linked to the position of the cursor on a screen, and the user can control the cursor with his head [9]. [PERSON] developed a system that deals with dynamic gestures, such as waving or nodding, that were recognized using a dynamic time-warping approach based on gesture-specific features computed from depth maps [10]. [PERSON] developed an interface used by severely disabled people who can only move their heads and have difficulties using a joystick, chin stick, and voice- or breath-sensor assistive devices [11]. [PERSON] focused on detecting head-nodding as a relatively simple, non-verbal communication modality because of its significance as a gesture displayed during social interactions [12]. [PERSON] outlined an approach to automating G-unit and gesture phase segmentation. Their approach treated the segmentation question as a classification problem and used a vector machine with different strategies to solve it [13]. [PERSON] proposed an intelligent wheelchair system based on sEMG (surface electromyography), head gestures, and a feature extraction algorithm based on an improved wavelet packet and sample entropy [14]. [PERSON] pointed out the need to consider many measures together with the recognition accuracy of the algorithm to predict its success in real-world applications [15]. [PERSON] used a single-web camera as the input device to recognize hand gestures. Some of these gestures included controlling the mouse cursor, clicking actions, and a few shortcuts for opening specific applications [16]. [PERSON] proposed a method to detect gestures stored in a dictionary with an accuracy of 91% and could define and add custom-made gestures [17].  
This study develops a machine vision system. Equipped with a laptop and connected with numeric head-controlled software, it is transformed into a sound-generating system suitable for the disabled. With the system, users can change their head pose, and, using the program code, the change will be converted into acoustic signals to generate sound. This study thus establishes a numeric human-machine interface to provide a more flexible and diverse interface for users.  
The rest of this paper is organized as follows: in Section 2, the proposed conceptual design is described in detail. User testing is described in Section 3. Section 4 presents the experimental results and the discussion. Finally, the conclusions are given in Section 5.