Navigation control of Drone using Hand Gesture based on Complementary Filter Algorithm

Budiyanto, Almira; Ramadhan, Muhamad Iqbal; Burhanudin, I; Triharminto, Hendri Himawan; Santoso, Bagus Jati

doi:10.1088/1742-6596/1912/1/012034

Cited by 5 publications

(2 citation statements)

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“…Budiyanto et al made a wearable device that gives instructions to drone and detects hand gestures. To reduce the noise generated due to angular transformation, a complementary filter method was used and it has been proven that hand gestures can be used for navigation control (9).…”

Section: Related Studiesmentioning

confidence: 99%

Sensor Based Wearable Devices for Road Navigation

Tyagi¹,

Singh

2022

ECS Trans.

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Internet of Things (IoT)-based applications play a significant role and are highly influenced by the global positioning system (GPS). GPS has a wide range of applications and benefits in tracking and route finding. A GPS tracker can help to explore numerous possibilities in various domains such as the health field, safety (to protect various assets), pattern recognition, logistics, in business models by using navigation systems embedded with sensors, and many more. Smart IoT wearable devices are the next step of evolution in the field of navigation using visual, auditory, ultrasonic, and other technologies. This paper introduces the various IoT sensors used in navigation systems and some prominent existing applications. This paper also provides a survey of existing wearable devices and concludes that the correct choice of sensors can easily overcome the challenges of navigation.

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Section: Related Studiesmentioning

confidence: 99%

Sensor Based Wearable Devices for Road Navigation

Tyagi¹,

Singh

2022

ECS Trans.

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“…EMG sensors can extract gesture features from electromyographic signals, but signal separation is relatively complex. Compared to these, piezoresistive sensors, due to their flexibility, thinness, and sensitivity to both dynamic and static forces, have been widely used in fields like sign language recognition [21,22], medical surgery remote operations [23], unmanned aerial vehicle (UAV) control [24][25][26], and remote robot operations [27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Real-time continuous gesture recognition system based on PSO-PNN

Ren,

Gao,

et al. 2024

Meas. Sci. Technol.

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In the field of Natural Human-Machine Interaction (NHMI), wearable gesture interaction technologies have received considerable attention, particularly Continuous Gesture Recognition (CG). However, CG faces several challenges, including the impact of motion characteristics on gesture recognition and performance that is not sufficiently robust. Traditional algorithms are highly dependent on samples, thus meeting the requirements of low sample volume and high accuracy simultaneously is challenging. To address these challenges, we propose a real-time continuous gesture recognition system based on Particle Swarm Optimization (PSO) and Probabilistic Neural Network (PNN). This system employs Principal Component Analysis (PCA) for signal dimensionality reduction to alleviate computational burden and uses K-means clustering and Pearson Correlation Coefficient (PCC) to extract optimal features for gesture classification. In offline gesture recognition experiments involving six continuous gestures, the algorithm achieved an accuracy rate of 97% with a training set of 300 samples and a runtime of just 31.25ms. Compared to other five algorithms, the proposed algorithm improved accuracy by at least 9% and reduced the runtime by 40.475ms. Moreover, gesture recognition experiments were conducted using different datasets, with the PSO-PNN algorithm achieving an average recognition rate of 90.17%, at least 9.84% higher than other algorithms. Finally, in experiments on online continuous gesture control for robots in complex environments, the PSO-PNN demonstrated real-time performance of 28.56ms and a task completion rate of 90.67%, validating the feasibility of PSO-PNN. This research provides a substantial theoretical and technical foundation for the ongoing enhancement and application of continuous gesture recognition technology.

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