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. 2019 Sep 16:5:e218.
doi: 10.7717/peerj-cs.218. eCollection 2019.

A systematic review on hand gesture recognition techniques, challenges and applications

Mais Yasen  1 , Shaidah Jusoh  1
Affiliations

A systematic review on hand gesture recognition techniques, challenges and applications

Mais Yasen et al. PeerJ Comput Sci. .

Abstract

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 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.

Keywords: Hand gesture; Hand gesture applications; Hand gesture recognition challenges; Recognition; Recognition techniques.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. PRISMA flow diagram of the study selection process.
Figure 2
Figure 2. Examples of hand gestures in sign language (Rosalina et al., 2017).
Figure 3
Figure 3. The basic steps of hand gesture recognition.
Figure 4
Figure 4. Results.
Each research question is illustrated in this figure, along with the suggested subcategories and the most common technique or problem resulting in them.
Figure 5
Figure 5. Example of hand gesture recognition using Kinect gestures (Microsoft, 2019).
Figure 6
Figure 6. Example of the use of hand gestures in robotics (Elecbits, 2019).
See this image and copyright information in PMC

References

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    1. Aditya T, Bertram T, Frederic G, Didier S. A probabilistic combination of CNN and RNN estimates for hand gesture based interaction in car. International symposium on mixed and augmented reality ISMAR-adjunct; Piscatway. 2017. pp. 1–6.
    1. Alvi M, Fatema TJ, Mohammad AY. An efficient approach of training artificial neural network to recognize Bengali Hand Sign. International conference on advanced computing IACC; Piscatway. 2016. pp. 152–157.

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