Cross-People Mobile-Phone Based Airwriting Character Recognition

Li, Yunzhe; Zheng, Hui; Zhu, He Guo; Ai, Haojun; Dong, Xiaowei

doi:10.1109/icpr48806.2021.9411989

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…Over the last few years, the challenge of recognizing writing from motion sensors has received significant attention, and a variety of techniques have been developed for the task [3], [4], [5], [6]. The studies employing motion sensors can be separated into two categories: dedicated devices, such as a wearable glove [7], Wii remote [8], [9], smartphone [10], and visionbased studies [11]. However, these solutions require the user to carry an additional physical device, which may be inconvenient for the users.…”

Section: A Backgroundmentioning

confidence: 99%

ImAiR: Airwriting Recognition Framework Using Image Representation of IMU Signals

et al. 2022

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The problem of airwriting recognition is focused on identifying letters written by movement of finger in free space. It is a type of gesture recognition where the dictionary corresponds to letters in a specific language. In particular, airwriting recognition using sensor data from wrist-worn devices can be used as a medium of user input for applications in humancomputer interaction (HCI). Recognition of in-air trajectories using such wrist-worn devices is limited in literature and forms the basis of the current work. In this letter, we propose an airwriting recognition framework by first encoding the time-series data obtained from a wearable inertial measurement unit (IMU) on the wrist as images and then utilizing deep learningbased models for identifying the written alphabets. The signals recorded from 3-axis accelerometer and gyroscope in IMU are encoded as images using different techniques such as self-similarity matrix (SSM), Gramian angular field (GAF), and Markov transition field (MTF) to form two sets of 3-channel images. These are then fed to two separate classification models, and letter prediction is made based on an average of the class conditional probabilities obtained from the two models. Several standard model architectures for image classification such as variants of ResNet, DenseNet, VGGNet, AlexNet, and GoogleNet have been utilized. Experiments performed on two publicly available datasets demonstrate the efficacy of the proposed strategy. The code for our implementation will be made available at https://github.com/ayushayt/ ImAiR.

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Section: A Backgroundmentioning

confidence: 99%

ImAiR: Airwriting Recognition Framework Using Image Representation of IMU Signals

et al. 2022

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show abstract

“…Over the last few years, the challenge of recognizing writing from motion sensors has received significant attention, and a variety of techniques have been developed for the task [3]- [6]. The studies employing motion sensors can be separated into two categories: dedicated devices, such as a wearable glove [7], Wii remote [8], [9], smartphone [10], and vision-based studies [11]. However, these solutions require the user to carry an additional physical device, which may be inconvenient for the users.…”

Section: A Backgroundmentioning

confidence: 99%

ImAiR: Airwriting Recognition framework using Image Representation of IMU Signals

Tripathi¹,

Mondal²,

Kumar³

et al. 2022

Preprint

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The problem of Airwriting Recognition is focused on identifying letters written by movement of finger in free space. It is a type of gesture recognition where the dictionary corresponds to letters in a specific language. In particular, airwriting recognition using sensor data from wrist-worn devices can be used as a medium of user input for applications in Human-Computer Interaction (HCI). Recognition of in-air trajectories using such wrist-worn devices is limited in literature and forms the basis of the current work. In this paper, we propose an airwriting recognition framework by first encoding the time-series data obtained from a wearable Inertial Measurement Unit (IMU) on the wrist as images and then utilizing deep learning-based models for identifying the written alphabets. The signals recorded from 3-axis accelerometer and gyroscope in IMU are encoded as images using different techniques such as Self Similarity Matrix (SSM), Gramian Angular Field (GAF) and Markov Transition Field (MTF) to form two sets of 3-channel images. These are then fed to two separate classification models and letter prediction is made based on an average of the class conditional probabilities obtained from the two models. Several standard model architectures for image classification such as variants of ResNet, DenseNet, VGGNet, AlexNet and GoogleNet have been utilized. Experiments performed on two publicly available datasets demonstrate the efficacy of the proposed strategy. The code for our implementation will be made available at https://github.com/ayushayt/ImAiR.

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“…Sanodiya et al [24] proposed training different transformation matrices for the source and target domains to achieve the goal of transfer learning. Based on the MTL method, Li et al [25] proposed the TransEMDT method, which uses a decision tree to build a robust behaviour recognition model based on the labelled data. The RTL method is poorly researched and discussed in only a few articles [26].…”

Section: Introductionmentioning

confidence: 99%

A Deep Transfer Learning-Based Network for Diagnosing Minor Faults in the Production of Wireless Chargers

Wang,

Li,

Zhu

2023

Applied Sciences

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Wireless charger production is critical to energy storage, and effective fault diagnosis of bearings and gears is essential to ensure wireless charging performance with high efficiency, high tolerance to misalignment, and thermal safety. As minor faults are usually difficult to detect, timely diagnosis and detection of minor faults can prevent the fault from worsening and ensure the safety of wireless charging systems. Diagnosing minor faults in bearings and gears with data is a useful but difficult task. To achieve a satisfactory diagnosis of minor faults in the production of wireless charging systems related to the mechanical system that produces wireless charging devices, such as robot arms, this paper proposes a deep learning network based on CNN and LSTM (DTLCL). The method uses deep learning network, model-based transfer learning and range adaptation technology. First, a deep neural network is built to extract significant fault features. Second, the deep transfer network is initialised using model-based transfer learning with a good starting point. Finally, range adaptation using the maximum mean discrepancy between the features learned from the source and target ranges is realised by a multi-layer adaptive technology. The effectiveness of the method was verified using actual measurement data. The training time is 19 s, and the accuracy exceeds 94.5%. The explanation results show that the proposed DTLCL method provides higher accuracy and robust identification of smaller errors compared to the current combination of integrated and single non-transmission models. Due to its data-driven nature, the DTLCL method could be used for fault diagnosis of bearings and gears, which would further promote the application process of wireless charging.

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Cross-People Mobile-Phone Based Airwriting Character Recognition

Cited by 6 publications

References 18 publications

ImAiR: Airwriting Recognition Framework Using Image Representation of IMU Signals

ImAiR: Airwriting Recognition Framework Using Image Representation of IMU Signals

ImAiR: Airwriting Recognition framework using Image Representation of IMU Signals

A Deep Transfer Learning-Based Network for Diagnosing Minor Faults in the Production of Wireless Chargers

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