지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2019.1
- 수록면
- 344 - 349 (6page)
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초록· 키워드
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Objectives: Human motion analysis can be applied to the diagnosis of musculoskeletal diseases, rehabilitation therapies, fall detection, and estimation of energy expenditure. To analyze human motion with micro-Doppler signatures measured by radar, a deep learning algorithm is one of the most effective approaches. Because deep learning requires a large data set, the high cost involved in measuring large amounts of human data is an intrinsic problem. The objective of this study is to augment human motion micro-Doppler data employing generative adversarial networks (GANs) to improve the accuracy of human motion classification. Methods: To test data augmentation provided by GANs, authentic data for 7 human activities were collected using micro-Doppler radar. Each motion yielded 144 data samples. Software including GPU driver, CUDA library, cuDNN library, and Anaconda were installed to train the GANs. Keras-GPU, SciPy, Pillow, OpenCV, Matplotlib, and Git were used to create an Anaconda environment. The data produced by GANs were saved every 300 epochs, and the training was stopped at 3,000 epochs. The images generated from each epoch were evaluated, and the best images were selected. Results: Each data set of the micro-Doppler signatures, consisting of 144 data samples, was augmented to produce 1,472 synthesized spectrograms of 64 × 64. Using the augmented spectrograms, the deep neural network was trained, increasing the accuracy of human motion classification. Conclusions: Data augmentation to increase the amount of training data was successfully conducted through the use of GANs. Thus, augmented micro-Doppler data can contribute to improving the accuracy of human motion recognition.
목차
등록된 정보가 없습니다.
참고문헌 (13)
참고문헌 신청
Taylor JL / 2016 / Cumulative life events, traumatic experiences, and psychiatric symptomatology in transition-aged youth with autism spectrum disorder / J Neurodev Disord 8:28
Dongwoo K / 2007 / Activity energy expenditure assessment system based on activity classification using multi-site triaxial accelerometers / Conf Proc IEEE Eng Med Biol Soc 2007:2285~2287
Ibrahim Alnujaim / 2019 / Generative Adversarial Networks for Classification of Micro-Doppler Signatures of Human Activity / IEEE Geoscience and Remote Sensing Letters 17(3):396~400
Goodfellow I / 2014 / Generative adversarial nets / Adv Neural Inf Process Syst 27:2672~2680
NVIDIA CUDA Toolkit
Dongwoo K / 2007 / Activity energy expenditure assessment system based on activity classification using multi-site triaxial accelerometers / Conf Proc IEEE Eng Med Biol Soc 2007:2285~2287
Ibrahim Alnujaim / 2019 / Generative Adversarial Networks for Classification of Micro-Doppler Signatures of Human Activity / IEEE Geoscience and Remote Sensing Letters 17(3):396~400
Goodfellow I / 2014 / Generative adversarial nets / Adv Neural Inf Process Syst 27:2672~2680
NVIDIA CUDA Toolkit
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