Interactive projector screen with hand detection using gestures

Sharma, Rishabh; Shikher, Raj; Bansode, Nutan V.; Rajarapollu, Prachi

doi:10.1109/icacdot.2016.7877650

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…An alternative to talking for deaf & dumb people who have hearing or speech problems using sign language recognition and hand gestures was proposed in (Himadri et al, 2018;Oinam et al, 2017;S. & M., 2017;Vivek & Ramesh, 2017;Oyndrila et al, 2016;Rishabh et al, 2016). Whereas in (Rosalina et al, 2017) a sign language that consists of the alphabetic from "A" to "Z", the numbers from "0" to "9", and some additional punctuation marks like "Period", "Question Mark", and "Space" using static hand gesture recognition was produced.…”

Section: Sign Languagementioning

confidence: 99%

“…A real-time hand gesture recognition technique for presentation was proposed in (Rishabh et al, 2016), to control OS on the projected screen for a virtual mouse system without any hardware requirement. Hand-gesture-based commands were proposed in (Lalit & Pritee, 2017) to replace touch and electromechanical input panels using vision-based mid-air character input.…”

Section: Othersmentioning

confidence: 99%

“…The authors of (Hafız et al, 2017) presented a CMSWVHG (Control MS Windows via hand Gesture) to perform numerous windows actions using hand gestures using internal or external camera for taking input with the help of OpenCV. To control OS on the projected screen for a virtual mouse system without any hardware requirement one camera source was required(Rishabh et al, 2016). Also, data acquisition was done using Camera interfacing in.To detect static hand gesture RGB cameras and depth data were used by(Rytis & Arunas, 2017;Chenyang, Xin & Lianwen, 2017).…”

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confidence: 99%

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Peer Review #2 of "A systematic review on hand gesture recognition techniques, challenges and applications (v0.1)"

2019

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Background. With the development of today's technology, and as humans tend to naturally use hand gestures in their communication process to clarify their intentions, hand gesture recognition is considered to be an important part of Human Computer Interaction (HCI), which gives the computers the ability of capturing and interpreting hand gestures, and executing commands afterwards. The aim of this study is to perform a systematic literature review for identifying the most prominent techniques, applications and challenges in hand gesture recognition. Methodology. To conduct this systematic review, we have screened 560 papers retrieved from IEEE Explore published from the year 2016 to 2018, in the searching process keywords such as "hand gesture recognition" and "hand gesture techniques" have been used. However, to focus the scope of the study 465 papers have been excluded. Only the most relevant hand gesture recognition works to the research questions, and the well-organized papers have been studied. Results. The results of this paper can be summarized as the following; the surface electromyography (sEMG) sensors with wearable hand gesture devices were the most acquisition tool used in the work studied, also Artificial Neural Network (ANN) was the most applied classifier, the most popular application was using hand gestures for sign language, the dominant environmental surrounding factor that affected the accuracy was the background color, and finally the problem of overfitting in the datasets was highly experienced. Conclusions. The paper will discuss the gesture acquisition methods, the feature extraction process, the classification of hand gestures, the applications that were recently proposed, the challenges that face researchers in the hand gesture recognition process, and the future of hand gesture recognition. We shall also introduce the most recent research from the year 2016 to the year 2018 in the field of hand gesture recognition for the first time.

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Section: Sign Languagementioning

confidence: 99%

Section: Othersmentioning

confidence: 99%

mentioning

confidence: 99%

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Peer Review #2 of "A systematic review on hand gesture recognition techniques, challenges and applications (v0.1)"

2019

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“…The study of hand detection or hand gesture detection which has been reviewed in [1] can be implemented in some areas such as in education [2,3], robotics [4,5], healthcare application [6][7][8], interactive projector scree [9], human computer interaction [10,11] and banking [12]. In detecting hands most of researches used the image of hand with various different method.…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0: Hand Recognition on Assembly Supervision Process

Analia

Pratama

Susanto

2021

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In the assembly industry, the process of assembling components is very important in order to produce a quality product. Assembly of components should be carried out sequentially based on the standards set by the company. For companies that still operate the assembly process manually by employee, sometimes errors occur in the assembly process, which can affect the quality of production. In order to be carried out the assembly process according to the procedure, a system is needed that can detect employee hands when carrying out the assembly process automatically. This study proposes an artificial intelligence-based real-time employee hand detection system. This system will be the basis for the development of an automatic industrial product assembly process to welcome the Industry 4.0. To verify system performance, several experiments were carried out, such as; detecting the right and left hands of employees and detecting hands when using accessories or not. From the experimental results it can be concluded that the system is able to detect the right and left hands of employees well with the resulting FPS average of 15.4.

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