Integrated vision-based robotic arm interface for operators with upper limb mobility impairments.
Integrated vision-based robotic arm interface for operators with upper limb mobility impairments.
复制标题
DOI：
10.1109/icorr.2013.6650447
复制
发表时间：
2013-06-01
期刊：
影响因子：
--
通讯作者：
Duerstock, Bradley S
中科院分区：
文献类型：
--
作者：
Jiang, Hairong;Wachs, Juan P;Duerstock, Bradley S
开发了一个集成的，基于计算机视觉的系统来操作商业轮椅安装的机器人机械手（WMRM）。在本文中，一个手势识别接口系统开发的上层脊髓损伤（SCI）的个人与对象跟踪和人脸识别系统相结合，是一个有效的，免提WMRM控制器。在这个测试系统中，两个Kinect摄像头被协同使用来执行各种简单的物体检索任务。一个摄像头被用来解释手势，以发送命令来控制WMRM并定位操作员的面部以进行对象定位。另一个传感器用于自动识别不同的日常生活对象，供测试对象选择。手势识别界面结合了手检测，跟踪和识别算法，以获得97.5%的高识别准确率为八手势词典。执行采用加速鲁棒特征（SURF）算法的对象识别模块，并且将识别结果作为用于在所选日常生活对象附近的机器人臂的“粗略定位”的命令发送。自动人脸检测也提供了一个快捷方式，为主题的位置对象的脸，通过使用WMRM。完成时间任务进行比较手动（手势）和半手动（手势，自动人脸检测和对象识别）WMRM控制模式。自动人脸和物体检测的使用显著增加了检索各种日常生活对象的完成时间。
An integrated, computer vision-based system was developed to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In this paper, a gesture recognition interface system developed specifically for individuals with upper-level spinal cord injuries (SCIs) was combined with object tracking and face recognition systems to be an efficient, hands-free WMRM controller. In this test system, two Kinect cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures to send as commands to control the WMRM and locate the operator's face for object positioning. The other sensor was used to automatically recognize different daily living objects for test subjects to select. The gesture recognition interface incorporated hand detection, tracking and recognition algorithms to obtain a high recognition accuracy of 97.5% for an eight-gesture lexicon. An object recognition module employing Speeded Up Robust Features (SURF) algorithm was performed and recognition results were sent as a command for "coarse positioning" of the robotic arm near the selected daily living object. Automatic face detection was also provided as a shortcut for the subjects to position the objects to the face by using a WMRM. Completion time tasks were conducted to compare manual (gestures only) and semi-manual (gestures, automatic face detection and object recognition) WMRM control modes. The use of automatic face and object detection significantly increased the completion times for retrieving a variety of daily living objects.