Gesture Control Robotic Arm Using Flex Sensor

Afzal, Waseem

doi:10.11648/j.acm.20170604.12

Cited by 15 publications

(2 citation statements)

References 9 publications

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“…It provides finger-joint flexion angle measurement domain that range from 0° to 100° [14]. It utilizes a carbon on a piece of plastic to act as a variable resistor with its ranges of value from 25k ohm to 125k ohm and its sizes range from 1 inch to 3 inches [15]. The flexure sensor was combined with a static resistor to build a voltage divider, that delivered a variable voltage that could be measured by analog to digital converter of the microcontroller.…”

Section: System Componentmentioning

confidence: 99%

Hand Gesture Design Using Bionic Prosthetic Hand

Leong

Swee²,

Thye³

et al. 2023

HumEnTech

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Brain computer interface (BCI) system empowers command over external device by retrieving brain waves and interpreting them into machine instructions. The system utilizes electroencephalogram (EEG) for receiving, processing and classifying signals to control by means of brain generated signals. The paper focuses on mental task design for BCI by acquiring the signals generated by mental activity through EEG comb electrodes, placed over three-dimensional (3D) printed headset. The experiment involved the blinking of left and right eye for the forward and backward movements of the prototype wheelchair. The experimental measurement was performed using a Cyton board where the information was transmitted through Bluetooth which were later processed and translated to the wheelchair to perform activities. The system has successfully achieved the real time control of an assistive device by using signals from the brain.

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Section: System Componentmentioning

confidence: 99%

Hand Gesture Design Using Bionic Prosthetic Hand

Leong

Swee²,

Thye³

et al. 2023

HumEnTech

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“…To demonstrate the effectiveness of the proposed approach, a two-joint robot manipulator was used [43]. The dynamic equations for this planar manipulator of 2DoF as shown in Figure 4 are described by the joint positions, velocities, and accelerations as functions of time [44]:…”

Section: Simulationmentioning

confidence: 99%

Robust Adaptive Tracking Control of Manipulator Arms with Fuzzy Neural Networks

Fouzia

Khenfer

Boukezzoula

2020

Eng. Technol. Appl. Sci. Res.

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The learning space for executing general motions of a flexible joint manipulator is quite large and the dynamics are, in general, nonlinear, time-varying, and complex. The objective of this paper is to design a nonlinear system based on the fuzzy neural network control using supervised training, into executing reference trajectories by a flexible joint manipulator. The structure identifications of controller networks are performed by using the Adaptive Neural Fuzzy Inference System (ANFIS), with new parameters and weight coefficients automatically adapted and adjusted, in order to decrease position tracking errors. In order to adapt and reduce the number of undefined parameters in the network, a new technique is used. Reported research works use the Euler method for the resolution of the arm's dynamic function, in this paper, a more exact method was used, represented by the Fourth-Order Runge-Kutta (RK4) method. A comparative study has been carried out between these two methods in order to prove the effectiveness of the later. Finally, in order to test the robustness of the proposed approach, it was also investigated considering parameter variations. The tracking speed of the model on the system control accuracy was also analyzed. The simulation results show that the proposed approach has a good tracking effect.

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