Hand gesture recognition in real world scenarios using approximate string matching

Alonso, Diego Gabriel; Teyseyre, Alfredo Raúl; Soria, Álvaro; Berdún, Luis

doi:10.1007/s11042-020-08913-7

Cited by 13 publications

(4 citation statements)

References 25 publications

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“…Ref. [ 13 ] created a string matching technique for understanding hand motions in real-time situations. The k-means technique was used to create an approximation string matching to capture the features of hand joints.…”

Section: Significant Research Work On Hand Gesture Recognitionmentioning

confidence: 99%

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

et al. 2022

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Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

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Section: Significant Research Work On Hand Gesture Recognitionmentioning

confidence: 99%

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

et al. 2022

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“…Alonso et al [29] presented a string matching to analyze the hand gesture in real-time scenarios. An approximate string matching (ASM) was implemented to encode the characteristics of hand joints by introducing the k-mean algorithm.…”

Section: Related Workmentioning

confidence: 99%

Smart healthcare solutions using the internet of medical things for hand gesture recognition system

Mahmoud

Fouad

Soliman

2020

Complex Intell. Syst.

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Patient gesture recognition is a promising method to gain knowledge and assist patients. Healthcare monitoring systems integrated with the Internet of Things (IoT) paradigm to perform the remote solutions for the acquiring inputs. In recent years, wearable sensors, and information and communication technologies are assisting for remote monitoring and recommendations in smart healthcare. In this paper, the dependable gesture recognition (DGR) using a series learning method for identifying the action of patient monitoring through remote access is presented. The gesture recognition systems connect to the end-user (remote) and the patient for instantaneous gesture identification. The gesture is recognized by the analysis of the intermediate and structuring features using series learning. The proposed gesture recognition system is capable of monitoring patient activities and differentiating the gestures from the regular actions to improve the convergence. Gesture recognition through remote monitoring is indistinguishable due to the preliminary errors. Further, it is convertible using series learning. Therefore, the misdetections and classifications are promptly identified using the DGR and verified by comparative analysis and experimental study. From the analysis, the proposed DGR approach attains 94.92% high precision for the varying gestures and 89.85% high accuracy for varying mess factor. The proposed DGR reduces recognition time to 4.97 s and 4.93 s for the varying gestures and mess factor, respectively.

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“…Research work related to gestures includes: intuitiveness of gesture use [14], the quantity of gestures for efficient application [15], or the ergonomics of in-vehicles gestures [16,17]. In [18] authors proposed a classifier based on Approximate String Matching (ASM). Their approach achieves a high performance rates in gesture recognition.…”

Section: Related Workmentioning

confidence: 99%

Gesture-Based User Interface for Vehicle On-Board System: A Questionnaire and Research Approach

2020

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Touchless interaction with electronic devices using gestures is gaining popularity and along with speech-based communication offers their users natural and intuitive control methods. Now, these interaction modes go beyond the entertainment industry and are successfully applied in real-life scenarios such as a car interior. In the paper, we analyse the potential of hand gesture interaction in the vehicle environment by physically challenged drivers. A survey conducted with potential users shows that the knowledge of gesture-based interaction and its practical use by people with disabilities is low. Based on these results we proposed a gesture-based system for vehicle on-board system. It has been developed on the available state-of-the-art solutions and investigated in terms of usability on a group of people with different physical limitations who drive a car on daily basis mostly using steering aid tools. The obtained results are compared with the performance of users without any disabilities.

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Hand gesture recognition in real world scenarios using approximate string matching

Cited by 13 publications

References 25 publications

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

A Structured and Methodological Review on Vision-Based Hand Gesture Recognition System

Smart healthcare solutions using the internet of medical things for hand gesture recognition system

Gesture-Based User Interface for Vehicle On-Board System: A Questionnaire and Research Approach

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