participant_14

Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR. 64 different hand shapes were recorded from 20 participants, each with 3 repetitions. The files are structured as follows: 1st line Name of the hand shape 2nd line Left or right hand 3rd line empty 4th-23rd line Features Repetition 1 24th line empty 25th - 44th line Features repetition 2 45th line blank 46th - 65th line Features repetition 3 The features are ordered as follows: Thumb spread Index Finger spread Middle Finger spread Ring Finger spread Pinky spread Thumb stretch CMC Thumb stretch MCP Thumb stretch IP Index Finger stretch MCP Index Finger stretch PIP Index Finger stretch DIP Middle Finger stretch MCP Middle Finger stretch PIP Middle Finger stretch DIP Ring Finger stretch MCP Ring Finger stretch PIP Ring Finger stretch DIP Pinky stretch MCP Pinky stretch PIP Pinky stretch DIP

依托计算机实现的人际交互，主要依赖键盘或麦克风完成。
在追求极致沉浸体验的虚拟现实（Virtual Reality, VR）领域中，使用键盘与该目标相悖，而麦克风的使用场景也存在诸多限制：例如特种部队训练期间需静默下达指令时无法使用，或是用户存在听力障碍时无法通过麦克风交互。数据手套（Data Gloves）契合人类自然交互习惯，有助于提升VR场景中的沉浸感；同时可精准捕捉手部姿态，覆盖非语言交互（如点赞、OK手势等）与手语中使用的各类手部形态。
本研究提出一套基于Manus Prime X数据手套的手部姿态识别系统，涵盖数据采集、预处理与分类模块，以实现VR场景内的非语言交互。我们探究了在数据预处理阶段引入异常检测（Outlier Detection）与特征选择（Feature Selection）方法，对识别精度与分类耗时的影响。为提升模型泛化能力，本研究还探究了人工数据增强（Data Augmentation）的影响：即通过已录制并预处理的原始数据生成新的人工样本，以扩充训练数据集。
采用本方案可区分56种手部姿态，最高识别精度达93.28%；当缩减至27种手部姿态时，最高识别精度可提升至95.55%。投票元分类器（Voting Meta-Classifier, VL2）被证实为精度最高的分类器，尽管其分类速度最慢；随机森林（Random Forest, RF）则是优质替代方案，在部分场景下可取得更高精度，且整体耗时略低。异常检测方法已被证实可有效提升性能，尤其在缩短分类耗时方面效果显著。综上，本研究证实基于数据手套的手部姿态识别系统可适配VR场景下的人际交互需求。本数据集共招募20名受试者，采集了64种手部姿态的3次重复采样数据。
数据文件的结构如下：
第1行：手部姿态名称
第2行：左手/右手标识
第3行：空行
第4~23行：第1次重复采样的特征数据
第24行：空行
第25~44行：第2次重复采样的特征数据
第45行：空行
第46~65行：第3次重复采样的特征数据
特征数据按以下顺序排列：
1. 拇指外展
2. 食指外展
3. 中指外展
4. 无名指外展
5. 小指外展
6. 拇指腕掌关节（Carpometacarpal Joint, CMC）屈伸
7. 拇指掌指关节（Metacarpophalangeal Joint, MCP）屈伸
8. 拇指指间关节（Interphalangeal Joint, IP）屈伸
9. 食指掌指关节（Metacarpophalangeal Joint, MCP）屈伸
10. 食指近侧指间关节（Proximal Interphalangeal Joint, PIP）屈伸
11. 食指远侧指间关节（Distal Interphalangeal Joint, DIP）屈伸
12. 中指掌指关节（Metacarpophalangeal Joint, MCP）屈伸
13. 中指近侧指间关节（Proximal Interphalangeal Joint, PIP）屈伸
14. 中指远侧指间关节（Distal Interphalangeal Joint, DIP）屈伸
15. 无名指掌指关节（Metacarpophalangeal Joint, MCP）屈伸
16. 无名指近侧指间关节（Proximal Interphalangeal Joint, PIP）屈伸
17. 无名指远侧指间关节（Distal Interphalangeal Joint, DIP）屈伸
18. 小指掌指关节（Metacarpophalangeal Joint, MCP）屈伸
19. 小指近侧指间关节（Proximal Interphalangeal Joint, PIP）屈伸
20. 小指远侧指间关节（Distal Interphalangeal Joint, DIP）屈伸