A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments

Jiang, Hairong; Duerstock, Bradley S.; Wachs, Juan P.

doi:10.1109/tsmc.2013.2270226

Cited by 35 publications

(11 citation statements)

References 30 publications

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“…For this reason, it can only be applied to known objects. For recognition of unknown objects, one shot learning techniques can be used (Fei-Fei et al, 2006;Jiang et al, 2013a). To speed up the algorithm for real-time object recognition, GPU-based SURF can be applied, and this will be the subject of future work.…”

Section: Discussionmentioning

confidence: 99%

Integrated vision-based system for efficient, semi-automated control of a robotic manipulator

Jiang

Wachs

Duerstock

2014

International Journal of Intelligent Computing and Cybernetics

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Purpose-Develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries (SCIs) including object tracking and face recognition to function as an efficient, hands-free WMRM controller. Design/Methodology/Approach-Two Kinect cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the operator's face for object positioning, and then send those as commands to control the WMRM. The other sensor was used to automatically recognize different daily living objects selected by the subjects. An object recognition module employing the Speeded Up Robust Features (SURF) algorithm was implemented and recognition results were sent as a commands for "coarse positioning" of the robotic arm near the selected object. Automatic face detection was provided as a shortcut enabling the positing the objects close by the subject's face. Findings-The gesture recognition interface incorporated hand detection, tracking and recognition algorithms, and yielded a recognition accuracy of 97.5% for an eight-gesture lexicon. Tasks' completion time 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 reduced the completion times for retrieving a variety of daily living objects. Originality/value-Integration of three computer vision modules were used to construct an effective and handfree interface for individuals with upper-limb mobility impairments to control a WMRM.

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Section: Discussionmentioning

confidence: 99%

Integrated vision-based system for efficient, semi-automated control of a robotic manipulator

Jiang

Wachs

Duerstock

2014

International Journal of Intelligent Computing and Cybernetics

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“…This generates the motion control commands for the robochair according to the head gesture reorganization and thus, triggering the chair to move in the direction as per users intension. Zyad Shaaban [8] proposed various face detection methods. As face detection plays an vital role in the face recognition system.…”

Section: Methodsmentioning

confidence: 99%

Robo chair assistance with obstacle detection using head gesture

Manikpure

Bankar

Salankar

2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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The need of many users or individuals with disabilities can be satisfied with traditional or manual or powered wheelchairs. The disabled users or individuals find it difficult or impossible to use the wheelchairs. The goal of our proposed intelligent system is to design and develop a system that allows the user to interact with the wheelchair at the different levels of the control, sensing and obstacle detection. Gesture Recognition means to recognize meaningful expressions of motion by a human, involving hands, face, arm, head, and or body. In this paper, we provide a review on gesture recognition with particular emphasis on head gestures and facial expressions. The system applications involve face detection, face tracking, gesture recognition, and obstacle detection. The obstacle detecting ultrasonic sensor attached to the robo chair in front which provides the safe navigation. The LCD also attached on the top which gives the user / individual friendly interface.

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“…Machine vision is a comprehensive technology [7] that can perceive the position and shape of objects in the surrounding environment [8]. Its main working principle [9] is to obtain the relevant information of images by using cameras, process the information of these images by using processors and obtain the relevant recognition results.…”

Section: Machine Visionmentioning

confidence: 99%

Industrial Automatic Assembly Technology Based on Machine Vision Recognition

Xiang¹

2021

Manufacturing Technology

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With the rapid development of science and technology, the means of industrial production have become more diversified and intelligent, and the development of new means of industrial production has become an increasingly important research topic. Therefore, the automatic assembly technology was studied, taking machine vision system as the main research subject in this study. An automated assembly model of industrial technology based on machine vision recognition was established, parameters, such as the part positioning parameter, assembly time, the number of parts wrongly assembled, the number of parts missing, and the assembly qualification rate were obtained, and the corresponding experimental conclusions were obtained. Moreover, it was compared with the traditional manual assembly technology, and it was found that the automatic assembly technology based on machine vision recognition had better performance and more remarkable experimental results compared with the traditional manual assembly technology, and the traditional manual assembly technology needed continuous modification and optimization. This work provides a new route for automatic assembly technology in industrial technology.

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A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments

Cited by 35 publications

References 30 publications

Integrated vision-based system for efficient, semi-automated control of a robotic manipulator

Integrated vision-based system for efficient, semi-automated control of a robotic manipulator

Robo chair assistance with obstacle detection using head gesture

Industrial Automatic Assembly Technology Based on Machine Vision Recognition

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