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Dynamic Gesture Recognition with Data Reduction and Machine Learning Algorithms Using Soli Dataset
(Institute of Electrical and Electronics Engineers Inc., 2025-06-27) Sevinc, Harun; Seyfi, Levent
As technology continues to advance, the expectations of technology users are also evolving. Smart electronic devices have significantly altered many daily habits of individuals, making them increasingly dependent on sensors. Among these sensors, radars have gained widespread use in daily life due to their advantages such as operating in a noncontact manner, being unaffected by lighting and weather conditions, and not infringing on privacy. In 2016, Google introduced the Soli project. Soli project has presented a dynamic hand gesture recognition system with a radar of very small dimensions. In this study, the Soli dataset-comprising 11 distinct hand gestures and 5,225 samples-has been utilized. Classical techniques such as truncation and downsampling have been applied to reduce data dimensions, and the classification has been performed using various machine learning algorithms, including Random Forest, k-Nearest Neighbors, Radial Basis Function Support Vector Machine, Decision Tree, and Gradient Boosting. Although deep learning algorithms often yield high performance with large datasets, in this study, the Random Forest algorithm achieved the highest classification accuracy with 8 3. 2 7%. © 2025 Elsevier B.V., All rights reserved.
Hand Gesture Recognition With FMCW Radar Using Data Reshaping and Machine Learning
(Institute of Electrical and Electronics Engineers Inc., 2025-09-10) Sevinc, H.; Seyfi, L.
Hand gesture recognition, one of the research areas of human computer interaction (HCI) based technology, has become a major focus of attention in the last two decades. Hand gesture recognition, which can be performed with multiple sensors, has found a new sensor especially with the development of radar technology. Hand gesture recognition, which has found its own application areas such as entertainment, security, gesture and posture analysis, is still a very suitable subject to be researched with artificial intelligence. In this study, we used the Dop-net dataset, which contains four different gestures: wave, pinch, swipe, swipe and click from six different individuals. The dataset was generated with frequency modulated continuous wave (FMCW) radar, which has the characteristics of high resolution and easy processing of the output signal. The data is preprocessed with short time Fourier transform (STFT), which is a highly preferred method because it produces two-dimensional output in time and frequency. In this study, it is proposed to reshape the Dop-Net data by downsampling and truncation and to classify them with machine learning algorithms such as support vector machine (SVM) and k-nearest neighbors (k-NN). As a result, the amount of data has been reduced and 93.71% classification accuracy has been obtained with the k-NN algorithm. © 2025 IEEE.

Journal	Count
-- 2025 Innovations in Intelligent Systems and Applications Conference, ASYU 2025 -- 2025-09-10 through 2025-09-12 -- Bursa -- 214381	1
-- 9th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2025 -- Gaziantep -- 211342	1

Sevinç, Harun

Sevinç, Harun

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