A Low Latency On-Body Typing System through Single Vibration Sensor

Chen, Wenqiang; Guan, Maoning; Huang, Yandao; Wang, Lu; Ruby, Rukhsana; Hu, Wen; Wu, Kaishun

doi:10.1109/tmc.2019.2928549

Cited by 14 publications

(3 citation statements)

References 34 publications

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“…The distance values more than 18 cm were removed in order to guarantee that the model was trained on reliable data. After trimming the raw data, each dataset has a shape of 20 × 3, as in (2), where the row represents data gathered by three ToF sensors for once and the column is the width of the shifting window. These datasets can be directly fed into LSTM and BiLSTM, while being reshaped as 60 × 1 as the input of CNN.…”

Section: Data Acquisition Modulementioning

confidence: 99%

“…dataset has a shape of 20 × 3, as in (2), where the row represents data gathered by three ToF sensors for once and the column is the width of the shifting window. These datasets can be directly fed into LSTM and BiLSTM, while being reshaped as 60 × 1 as the input of CNN.…”

Section: Data Acquisition System Experimentsmentioning

confidence: 99%

“…Typing, the most used method for the majority of individuals in human-computer interaction, requires dedicated writing instruments, such as keyboards or touch screens, which are not always available in everyday life [1]. W. Chen et al [2] presented an on-body typing system that allowed users to type on the backs of their hands on a T9-shaped keyboard. It is an advance in typing systems, but typing with one hand on a T9 keyboard is still awkward and slow.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Finger-Writing Character Recognition via ToF Sensors on Edge Deep Learning

et al. 2023

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Human–computer interaction is demanded for natural and convenient approaches, in which finger-writing recognition has aroused more and more attention. In this paper, a device-free finger-writing character recognition system based on an array of time-of-flight (ToF) distance sensors is presented. The ToF sensors acquire distance values between sensors to a writing finger within a 9.5 × 15 cm square on a surface at specific time intervals and send distance data to a low-power microcontroller STM32F401, equipped with deep learning algorithms for real-time inference and recognition tasks. The proposed method enables one to distinguish 26 English lower-case letters by users writing with their fingers and does not require one to wear additional devices. All data used in this work were collected from 21 subjects (12 males and 9 females) to evaluate the proposed system in a real scenario. In this work, the performance of different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNNs) and bidirectional LSTM (BiLSTM), was evaluated. Thus, these algorithms provide high accuracy, where the best result is extracted from the LSTM, with 98.31% accuracy and 50 ms of maximum latency.

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Section: Data Acquisition Modulementioning

confidence: 99%

Section: Data Acquisition System Experimentsmentioning

confidence: 99%