Whole hand modeling using 8 wearable sensors: biomechanics for hand pose prediction

Whole hand modeling using 8 wearable sensors: biomechanics for hand pose prediction
复制标题

使用 8 个可穿戴传感器进行全手建模:用于手部姿势预测的生物力学

DOI:
10.1145/2459236.2459241
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发表时间:
2013
期刊:
Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology
影响因子:
--
通讯作者:
Mathias Wilhelm
Mathias Wilhelm
中科院分区:
--
文献类型:
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作者:
Christopher;Katrin Wolf;Mathias Wilhelm

文献摘要

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虽然数据手套允许对人手进行建模,但它们可能会导致可用性降低,因为它们覆盖了整个手,限制了触觉,并降低了手的可行性。由于对整只手进行建模具有许多优点(例如,对于复杂的手势检测),我们的目标是对整只手进行建模,同时保持手的自然自由度(DOF)和触觉灵敏度尽可能高,同时允许手动任务,如抓取工具和设备。因此,我们将运动传感器板(加速度计,磁力计和陀螺仪)连接到人手上。我们进行了一项用户研究,并发现指尖闭合关节(DIP)和手掌闭合关节(PIP)之间的关节角度的生物力学依赖关系的DIP = 0.88 PIP的所有四个手指(SD=0.10,R2=0.77)。这使得数据手套可以减少8个传感器板,每个手指一个,拇指三个,手背上一个作为整个手建模的方向基线。尽管我们发现拇指也存在关节弯曲关系,但我们决定在此保留3个传感器单元,因为该关系变化更大(R2=0.59)。我们的手模型可以潜在地为丰富的基于手模型的手势交互服务,因为它覆盖了人手的所有26个自由度。
Although Data Gloves allow for the modeling of the human hand, they can lead to a reduction in usability as they cover the entire hand and limit the sense of touch as well as reducing hand feasibility. As modeling the whole hand has many advantages (e.g. for complex gesture detection) we aim for modeling the whole hand while at the same time keeping the hand's natural degrees of freedom (DOF) and the tactile sensibility as high as possible while allowing for manual tasks like grasping tools and devices. Therefore, we attach motion sensor boards (accelerometer, magnetometer and gyroscope) to the human hand. We conducted a user study and found the biomechanical dependence of the joint angles between the fingertip close joint (DIP) and the palm close joint (PIP) in a relation of DIP = 0.88 PIP for all four fingers (SD=0.10, R2=0.77). This allows the data glove to be reduced by 8 sensors boards, one per finger, three for the thumb, and one on the back of the hand as an orientation baseline for modeling the whole hand through. Even though we found a joint flexing relationship also for the thumb, we decided to retain 3 sensor units here, as the relationship varied more (R2=0.59). Our hand model could potentially serve for rich handmodel-based gestural interaction as it covers all 26 DOF in the human hand.