# Research on Monocular-Vision-Based Finger-Joint-Angle-Measurement System
## 1 Introduction  
The quantitative measurement of hand-joint range of motion (ROM) is important for clinicians to assess a patient's level of hand disability and the effectiveness of intervention therapy. In the clinical setting, knuckle goniometers are often used to measure ROM due to their ease of use, portability, and affordability. However, these devices are time-consuming for single-joint angle measurements and do not allow simultaneous multi-joint angle measurements. Many experts and scholars have conducted in-depth research in the field of knuckle-angle measurement, including wearable-sensor-based knuckle-angle-measurement methods and vision-based knuckle-angle-measurement methods. [PERSON] et al. developed a finger-joint-angle-measurement system based on flexible polymer sensors [1]. The system measures the flexion/extension movement of fingers by installing flexible polymer sensors on the surfaces of fingers, which can realize the detection of joint-angle changes during daily grasping movements. A three-dimensional (3-D) finger-motion-measurement system based on a soft sensor was proposed by [PERSON] et al. [2]. [PERSON] et al. designed an integrated mechanical-sensor detection system, consisting of an angle-measurement device and a measurement circuit in order to achieve finger-joint measurement [3]. The effectiveness of the system was verified by joint-angle measurement, motion-law evaluation and object-grasping experiments, and the experimental results showed that the root mean square (RMS) of the DIP, PIP, and MCP angle-measurement errors were 0.36, 0.59, and 0.32 degrees, respectively [3]. It has been found that these wearable-sensor-based finger-joint-angle measurement methods have high accuracy in measuring finger joint angles, but the difficulty in wearing them has not been effectively solved in clinical applications for patients with hand motor dysfunction [4, 5, 6, 7, 8, 9, 10].  
Vision-based knuckle-angle-measurement systems could realize the dynamic measurement of multi-joint angles without involving direct physical contact between the doctor and the patient's hand. Vision-based measurement systems work by first capturing an image of the entire hand and then using computer-vision techniques to estimate the hand posture [11; 12; 13; 14; 15]. Commercial devices (such as Leap Motion) are currently used for hand-angle measurement [16; 17] and, recently, they have been used in virtual-reality headsets (such as Facebook's OculusQuest and Microsoft's HoloLens2) equipped with hand tracking for human-computer interaction. The two main problems faced by current vision-based hand-posture estimation systems are the low accuracy of the knuckle-angle measurement and the high level of restriction on the camera view [18]. [PERSON] et al. proposed a method of measuring finger-joint angles and finger forces in the process of maximum cylindrical grip using a multi-camera photogrammetric method with markers and a pressure-sensitive film, respectively [19]. The experimental results showed that this method can be used to judge the extension/flexion direction of the knuckle.  
An industrial monocular-vision-based knuckle-angle-measurement system based on the existing computer-vision detection system is proposed in this paper [20]. This knuckle-angle-measurement system consists of a hardware system, a vision system, and a control system. The hand visual markers in the hardware system can simplify the difficulty of knuckle identification, and the use of high-resolution cameras can greatly improve the accuracy of the knuckle-angle detection. The active multi-angle light-detection system consisting of the control system, hardware system, and specified light source can adjust the light-irradiation angle and light-source-irradiation intensity to the marker by adjusting the height of the light source, thus enhancing the difference between the marker and the background, making the marker easy to the segment from the background and simplifying the marker-segmentation process.