Trajectory reconstruction algorithm based on sensor fusion between IMU and strain gauge for stand-alone digital pen

Toyozumi, Naoya; Takahashi, Junji; Lopez, Guillaume

doi:10.1109/robio.2016.7866607

Cited by 4 publications

(1 citation statement)

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“…Some investigations have supplemented inertial data with additional low-level sensor data. A study by [ 14 ], added strain gauges to inertial sensor data. The authors claimed that the strain gauge data helped in compensating for the intrinsic inertial sensor drifting error.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Character Recognition from Handwriting Motion Data Captured Using IMU and Force Sensors

Alemayoh

Shintani

Lee

et al. 2022

Sensors

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Digitizing handwriting is mostly performed using either image-based methods, such as optical character recognition, or utilizing two or more devices, such as a special stylus and a smart pad. The high-cost nature of this approach necessitates a cheaper and standalone smart pen. Therefore, in this paper, a deep-learning-based compact smart digital pen that recognizes 36 alphanumeric characters was developed. Unlike common methods, which employ only inertial data, handwriting recognition is achieved from hand motion data captured using an inertial force sensor. The developed prototype smart pen comprises an ordinary ballpoint ink chamber, three force sensors, a six-channel inertial sensor, a microcomputer, and a plastic barrel structure. Handwritten data of the characters were recorded from six volunteers. After the data was properly trimmed and restructured, it was used to train four neural networks using deep-learning methods. These included Vision transformer (ViT), DNN (deep neural network), CNN (convolutional neural network), and LSTM (long short-term memory). The ViT network outperformed the others to achieve a validation accuracy of 99.05%. The trained model was further validated in real-time where it showed promising performance. These results will be used as a foundation to extend this investigation to include more characters and subjects.

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Section: Introductionmentioning

confidence: 99%