# Machine Learning-Based Smartphone Grip Posture Image Recognition and Classification
## 2 Related Work  
### Preprocessing Techniques for Hand Recognition  
In the preprocessing stage, hand data captured using a regular camera--without the use of gloves or markers--was primarily segmented from the background through skin color filtering, contour detection techniques, or a combination of both methods. [PERSON] et al. [17] employed a combination of skin color segmentation and depth-based segmentation to separate the hand from other skin-colored objects for hand gesture tracking. In their study, the YCbCr color space--characterized by the separation of luminance and chrominance and the compactness of the skin cluster--was used for skin color segmentation, achieving a gesture recognition accuracy of 90.7% [18]. [PERSON] et al. [19] proposed a method combining two color spaces, HS and CbCr (HS-CbCr), to detect skin regions, and employed a background averaging technique for hand gesture recognition. They reported an accuracy of 96.87%, while noting that recognition performance was affected by lighting conditions, with better results under stable illumination. [PERSON] et al. (2006) recognized hands using two techniques: the Hausdorff distance of the hand contours and the independent component features of the hand silhouette images [20]. [PERSON] and [PERSON] (2014) developed a hand contour model to simplify the gesture matching process, which can reduce the computational complexity of gesture matching [21]. [PERSON] and [PERSON] (2011) detected hands by identifying local peaks in depth images and developed a method to distinguish the hand from the forearm based on the radius of the palm [22].  
In addition, in the preprocessing stage, multiple techniques are often used in combination, and algorithms such as YOLOv7 are also being applied. [PERSON] and [PERSON] (2022) proposed a pre-processing approach that integrates YCbCr and HSV color-space-based skin segmentation, motion-based three-frame differencing, and a double-tracking system combining particle filtering and CAMshift for robust hand tracking and gesture trajectory extraction [23]. [PERSON] et al. [24] proposed a Gaussian Mixture Model (GMM) based on CbCr-I color components to improve hand segmentation under varying illumination. By applying this model with an adaptive threshold, skin regions were effectively detected and the hand region was extracted as the region of interest (ROI). Furthermore, a novel hand shape distribution feature in polar coordinates was introduced to reduce misclassification among postures with the same number of extended fingers and to enhance the accuracy of hand posture recognition. [PERSON] et al. [25] emphasized the importance of pre-processing in hand gesture recognition systems, particularly to address challenges such as illumination variation, noise, and complex backgrounds in real-world settings. Their proposed method involved reducing noise and adjusting brightness in the input images, followed by detecting the region of interest (ROI) using directional images. After extracting the hand region, geometric features were obtained via convex hull-based landmark extraction. These features were then used for gesture classification through a convolutional neural network (CNN). The system achieved high recognition accuracy, with results of 93.2% and 90.2% on the MNIST dataset, and 91.6% and 88.14% on the ASL dataset, using one-third and two-thirds training-validation splits, respectively. [PERSON] et al. [26] adopted the YOLOv7 object detection model--which integrates enhanced layer aggregation (E-ELAN) and reparameterized convolution (RepConvN) to improve accuracy and scalability--for the task of hand detection. Leveraging its efficient architecture, they trained the YOLOv7x variant for 200 epochs on the Oxford Hand dataset and reported a high mean average precision (mAP) of 86.3%, confirming the model's effectiveness in detecting hand shapes under various conditions.