SmartHandwriting: Handwritten Chinese Character Recognition With Smartwatch

Jian, Zhang; Bi, Hongliang; Chen, Yanjiao; Wang, Mingyu; Han, Liming; Cai, Ligan

doi:10.1109/jiot.2019.2947448

Cited by 21 publications

(2 citation statements)

References 29 publications

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“…Since the neural network needs a large amount of data, we use the data augmentation scheme to increase the diversity of data and the robustness of the system. Specifically, we refer to the data augmentation method in [ 37 ] and scale the original data along the timestamp by different multiples

. In addition, Gaussian noise is added to the original data and the scaled data to make the data have some errors, so as to better simulate the real situation.…”

Section: Methodsmentioning

confidence: 99%

Capturing Features and Performing Human Detection from Human Gaits Using RFID

Zhang

Liu

Yang

et al. 2022

Sensors

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Recently, radio frequency identification (RFID) sensing has attracted much attention due to its contact-free nature, low cost, light weight and other advantages. RFID-based person detection has also become a hot research topic, but there are still some problems in the existing research. First, most of the current studies cannot identify numerous people at a time well. Second, in order to detect more accurately, it is necessary to evaluate the whole-body activity of a person, which will consume a lot of time to process the data and cannot be applied in time. To solve these problems, in this paper we propose RF-Detection, a person detection system using RFID. First of all, RF-Detection takes step length as the standard for person detection, divides step length into specific sections according to the relationship between step length and height, and achieves high accuracy for new user detection through a large amount of training for a specific step length. Secondly, RF-Detection can better identify the number of people in the same space by segmenting continuous people. Finally, the data collection was reduced by expanding the data set, and the deep learning method was used to further improve the accuracy. The results show that the overall recognition accuracy of RF-Detection is 98.93%.

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. In addition, Gaussian noise is added to the original data and the scaled data to make the data have some errors, so as to better simulate the real situation.…”

Section: Methodsmentioning

confidence: 99%

Capturing Features and Performing Human Detection from Human Gaits Using RFID

Zhang

Liu

Yang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Digital devices, especially mobile or wearable devices, are increasingly capable of capturing various sources of real-time behavioral, physiological, and psychosocial data in a precise and confidential manner [13]- [15]. Examples of these technologies include smartphones [16]- [18] and smartwatches [19], [20]. Interestingly, how we use these technologies to improve mental well-being or mitigate mental illness in terms of emerging uncertainties is opened to discussion.…”

Section: Introductionmentioning

confidence: 99%