A Continuous Hand Gestures Recognition Technique for Human-Machine Interaction Using Accelerometer and Gyroscope Sensors

Gupta, Hari Prabhat; Chudgar, Haresh S.; Mukherjee, Siddhartha; Dutta, Tanima; Sharma, Kulwant

doi:10.1109/jsen.2016.2581023

Cited by 156 publications

(71 citation statements)

References 22 publications

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“…In Yuhui, Shuo & Peter (2018), Yuta et al (2017, Donq-Liang & Wei-Shiuan (2018), Ananta & Piyanuch (2016) and Nabeel, Rosa & Chan (2017) a sensor-based wearable wristband was presented for static hand gestures. The authors in Hari et al (2016) and Erhan, Hakan & Baran (2017) presented making use of the sensors in smartphones. The authors of Yifan et al (2018) used wearable devices such as VR/AR helmet and glasses in a gesture recognition system.…”

Section: Gesture Acquisition Methodsmentioning

confidence: 99%

“…Regional Contrast (RC) based salient object extraction algorithm, and a method using the color statistics of image were used in Qingrui et al (2017). To detect the start and end points of gestures a gesture spotting algorithm was applied in Hari et al (2016). Experiments of Chenyang, Yingli & Matt (2016) followed five different feature extraction strategies; depth image sequence, body joints & facial landmarks, hand shapes & facial expressions/attributes.…”

Section: Feature Extractionmentioning

confidence: 99%

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

Yasen

Jusoh

2019

PeerJ Computer Science

123

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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 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: Gesture Acquisition Methodsmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

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

Yasen

Jusoh

2019

PeerJ Computer Science

123

View full text Add to dashboard Cite

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“…Whilst some trackers are vision-based [39], such as Microsoft Kinect, Leap Motion controller and Nimble UX, others are more amenable to being incorporated into wearables, e.g. [40][41][42][43], and thus can be more suitable for a cockpit setting.…”

Section: Pointing Gesture Trackermentioning

confidence: 99%

“…in 3-D) must be tracked. Nonetheless, plethora of third party solutions (gesture tracking technologies) are now available for use in confined spaces including automotive and aerospace, see [39][40][41][42][43]. This gives rise to a technology that is potentially not restricted to screens of any sort and may also assist or allow free-air gestures in a perturbed vibrating environment.…”

Section: Design Recommendationsmentioning

confidence: 99%

Stabilising Touch Interactions in Cockpits, Aerospace, and Vibrating Environments

Ahmad

Langdon

Godsill

2018

Universal Access in Human-Computer Interaction. Methods, Technologies, and Users

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Incorporating touch screen interaction into cockpit flight systems is increasingly gaining traction given its several potential advantages to design as well as usability to pilots. However, perturbations to the user input are prevalent in such environments due to vibrations, turbulence and high accelerations. This poses particular challenges for interacting with displays in the cockpit, for example, accidental activation during turbulence or high levels of distraction from the primary task of airplane control to accomplish selection tasks. On the other hand, predictive displays have emerged as a solution to minimize the effort as well as cognitive, visual and physical workload associated with using in-vehicle displays under perturbations, induced by road and driving conditions. This technology employs gesture tracking in 3D and potentially eye-gaze as well as other sensory data to substantially facilitate the acquisition (pointing and selection) of an interface component by predicting the item the user intents to select on the display, early in the movements towards the screen. A key aspect is utilising principled Bayesian modelling to incorporate and treat the present perturbation, thus, it is a software-based solution that showed promising results when applied to automotive applications. This paper explores the potential of applying this technology to applications in aerospace and vibrating environments in general and presents design recommendations for such an approach to enhance interactions accuracy as well as safety.

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“…Using hand gesture for human-machine interaction will facilitate the interaction process [2]. Hand gesture recognition can find applications in domains including smart device control [3] and robot-assisted living [4]. One of the areas that can be benefitted by the application of hand gesture recognition is education for children with learning difficulties.…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Recognition with Inertial Sensors

Teachasrisaksakul

Yang

et al. 2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Dyscalculia is a learning difficulty hindering fundamental arithmetical competence. Children with dyscalculia often have difficulties in engaging in lessons taught with traditional teaching methods. In contrast, an educational game is an attractive alternative. Recent educational studies have shown that gestures could have a positive impact in learning. With the recent development of low cost wearable sensors, a gesture based educational game could be used as a tool to improve the learning outcomes particularly for children with dyscalculia. In this paper, two generic gesture recognition methods are proposed for developing an interactive educational game with wearable inertial sensors. The first method is a multilayered perceptron classifier based on the accelerometer and gyroscope readings to recognize hand gestures. As gyroscope is more power demanding and not all low-cost wearable device has a gyroscope, we have simplified the method using a nearest centroid classifier for classifying hand gestures with only the accelerometer readings. The method has been integrated into open-source educational games. Experimental results based on 5 subjects have demonstrated the accuracy of inertial sensor based hand gesture recognitions. The results have shown that both methods can recognize 15 different hand gestures with the accuracy over 93%.

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A Continuous Hand Gestures Recognition Technique for Human-Machine Interaction Using Accelerometer and Gyroscope Sensors

Cited by 156 publications

References 22 publications

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

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

Stabilising Touch Interactions in Cockpits, Aerospace, and Vibrating Environments

Hand Gesture Recognition with Inertial Sensors

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