An agile approach for human gesture detection using synthetic radar data

Gigie, Andrew; Rani, Smriti; Chowdhury, Arijit; Chakravarty, Tapas; Pal, Arpan

doi:10.1145/3341162.3349332

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…Previous research has presented and evaluated multiple techniques to recognize radar gestures. For example, Gigie et al [14] showcase a case study of data explosion for radar-based human gesture detection. They introduced a simulation framework based on a physical model to generate radar signals corresponding to various human gestures.…”

Section: B Radar-based Gesture Sensing and Recognitionmentioning

confidence: 99%

“…Radar-detected gestures can range from basic presence and proximity detection to multitouch gestures or foot-based gestures [12], [13], [6]. Studies on radar device interactions suggest various sets of gestures, some focusing on hand gestures [14], [15] while others advocate interactions that involve the whole body [16]. These examples highlight that radar device interactions typically involve the upper body, as detailed by S , iean et al [1].…”

Section: Introductionmentioning

confidence: 99%

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SkySculptor: Intuitive Drone Control Through Ground-Integrated Radar and Foot Gestures in Smart Indoor Environments

Siean

2024

IJACSA

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SkySculptor is a software application designed to optimize drone control in smart indoor environments. The primary focus is on using gesture input for drone control, particularly investigating mid-air free-foot interactions detected by radar sensing. This software application simplifies the process of controlling drones in smart indoor environments. Additionally, outcomes of utilizing a 15-antenna ultra-wideband 3D radar are presented, establishing a dictionary of six directional swipe gestures for controlling drone functions. Based on the findings of this research article, guidelines for the future development of software applications for drone control in intelligent indoor environments are proposed.

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Section: B Radar-based Gesture Sensing and Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SkySculptor: Intuitive Drone Control Through Ground-Integrated Radar and Foot Gestures in Smart Indoor Environments

Siean

2024

IJACSA

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“…Then, they extracted the position of the body joints, used them in a custom Frequencymodulated continuous wave (FMCW) Radar simulator and generated a dataset with 2000 samples per gesture. Finally yet importantly, in [17] another method was presented for generating synthetic Radar data for gesture recognition. The authors converted a Kinect dataset into Radar signatures using a simulation framework, then they extracted several features out of the spectrogram and used them to train a machine learning (ML) model with four gestures.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Radar Dataset Generator for Macro-Gesture Recognition

et al. 2021

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Recent developments in mmWave technology allow the detection and classification of dynamic arm gestures. However, achieving a high accuracy and generalization requires a lot of samples for the training of a machine learning model. Furthermore, in order to capture variability in the gesture class, the participation of many subjects and the conduct of many gestures with different arm speed are required. In case of macro-gestures, the position of the subject must also vary inside the field of view of the device. This would require a significant amount of time and effort, which needs to be repeated in case that the sensor hardware or the modulation parameters are modified. In order to reduce the required manual effort, here we developed a synthetic data generator that is capable of simulating seven arm gestures by utilizing Blender, an open-source 3D creation suite. We used it to generate 600 artificial samples with varying speed of execution and relative position of the simulated subject, and used them to train a machine learning model. We tested the model using a real dataset recorded from ten subjects, using an experimental sensor. The test set yielded 84.2% accuracy, indicating that synthetic data generation can significantly contribute in the pre-training of a model.

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