Design of a low-cost Hand Gesture Controlled Automated Wheelchair

Mahmood, M.; Rizwan, Md. Fahim; Sultana, Munawar; Habib, Md. Ahasonul; Imam, Hasan

doi:10.1109/tensymp50017.2020.9230849

Cited by 16 publications

(2 citation statements)

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“…The mapping method selected for this component was a one-to-one command mapping approach. This approach was selected to conform with the design decisions made in [ 1 , 2 , 3 , 29 ] and served as a good initial solution capable of demonstrating the functionality of the fully developed HCI algorithm within the context of alternative control. This stage consists of two key developmental sections.…”

Section: Methodsmentioning

confidence: 99%

“…Alternative-control algorithms consist of two main components, a non-standard human–computer interface (HCI) and a command mapping algorithm [ 1 , 2 , 3 , 4 ]. An alternative-control algorithm is considered successful in its application if the alternative HCI extends upon the functionality offered by the conventional control medium.…”

Section: Introductionmentioning

confidence: 99%

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Design and Evaluation of an Alternative Control for a Quad-Rotor Drone Using Hand-Gesture Recognition

Khaksar,

Checker,

Borazjan

et al. 2023

Sensors

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Gesture recognition is a mechanism by which a system recognizes an expressive and purposeful action made by a user’s body. Hand-gesture recognition (HGR) is a staple piece of gesture-recognition literature and has been keenly researched over the past 40 years. Over this time, HGR solutions have varied in medium, method, and application. Modern developments in the areas of machine perception have seen the rise of single-camera, skeletal model, hand-gesture identification algorithms, such as media pipe hands (MPH). This paper evaluates the applicability of these modern HGR algorithms within the context of alternative control. Specifically, this is achieved through the development of an HGR-based alternative-control system capable of controlling of a quad-rotor drone. The technical importance of this paper stems from the results produced during the novel and clinically sound evaluation of MPH, alongside the investigatory framework used to develop the final HGR algorithm. The evaluation of MPH highlighted the Z-axis instability of its modelling system which reduced the landmark accuracy of its output from 86.7% to 41.5%. The selection of an appropriate classifier complimented the computationally lightweight nature of MPH whilst compensating for its instability, achieving a classification accuracy of 96.25% for eight single-hand static gestures. The success of the developed HGR algorithm ensured that the proposed alternative-control system could facilitate intuitive, computationally inexpensive, and repeatable drone control without requiring specialised equipment.

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