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Ying Tan

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7 papers

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JBHI Journal 2025 Journal Article

An Information-Rich and Highly Wearable Soft Sensor System Based on Displacement Myography for Practical Hand Gesture Interfaces

Alireza Mohammadi
Chu Wang
Tianshi Yu
Ying Tan
Peter Choong
Denny Oetomo

Wearable sensors for hand gesture recognition have demonstrated significant potential for creating non-invasive human-machine interfaces. Nonetheless, the trade-off between wearability, practicality and performance constrains their applicability in real-world scenarios. This paper introduces MyoLog, a wearable soft sensor system that utilises forearm muscle deformations for accurate hand gesture recognition. Muscle displacements are captured using an array of magnets and tri-axis magnetometers (displacement myography), integrated into soft and flexible structures that conform to and deform with the shape of forearm muscles. The high signal-to-noise ratio and sensitivity of the sensor modules in MyoLog produce information-rich signals, enabling the detection and differentiation of a wide spectrum of hand gestures. The study used the results of 9 participants performing 44 diverse gestures with MyoLog to investigate its performance in terms of number of gestures and achieved classification accuracy. The average performance achieved by participants was 97. 7%, 91. 5%, and 89. 1% accuracy in executing 13, 22, and 28 gestures, respectively. To demonstrate the capabilities of MyoLog in practical settings, we explored two potential applications in virtual reality training for laparoscopic surgery and prosthetic hand control. The high wearability of MyoLog without compromising the performance paves the way for more practical human-machine interactions in diverse applications.

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JBHI Journal 2025 Journal Article

FastCRL: A Fast Network With Adaptive Fourier Transform and Offset Prediction for Fetal Crown-Rump Length Measurement and Position Estimation in Ultrasound Images

Jiatao Liu
Ying Tan
Chunlian Wang
Kenli Li
Guanghua Tan
Chubo Liu

Fetal crown-rump length (CRL) is one of the most accurate method for estimating gestational age in early pregnancy. Typically, the process of manual CRL measurement by physicians is cumbersome, prone to errors due to fetal position, and susceptible to inter-observer variability. To provide an accurate, real-time, and reliable fetal CRL measurement solution, we propose FastCRL that utilizes key landmarks detection for efficient CRL measurements and fetal position estimation. Specifically, fast and lightweight network blocks are employed for both the encoder and decoder. By outputting low-resolution heatmaps and axial offset maps of key landmarks, we achieve a balance between high accuracy and fast inference speed. A novel Lightweight Adaptive Fourier Transform (LAFT) module is introduced to globally filter noise in ultrasound images and enhance the features required for landmark prediction. Additionally, the challenge of evaluating fetal position flexion and extension is effectively addressed by analyzing the angles between key landmarks on the fetal head, buttocks, and neck. The experimental results on our dataset indicate that our method for determining fetal position is both objective and efficient. FastCRL achieves a performance level consistent with the average human expert. In terms of measuring CRL, FastCRL achieved an error rate of less than 3% in 99. 1% of measurements with 32 ms latency, significantly outperforming other baselines and demonstrating substantial potential for clinical application.

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JBHI Journal 2025 Journal Article

TKR-FSOD: Fetal Anatomical Structure Few-Shot Detection Utilizing Topological Knowledge Reasoning

Xi Li
Ying Tan
Bocheng Liang
Bin Pu
Jiewen Yang
Lei Zhao
Yanqing Kong
Lixian Yang

Fetal multi-anatomical structure detection in ultrasound (US) images can clearly present the relationship and influence between anatomical structures, providing more comprehensive information about fetal organ structures and assisting sonographers in making more accurate diagnoses, widely used in structure evaluation. Recently, deep learning methods have shown superior performance in detecting various anatomical structures in ultrasound images, but still have the potential for performance improvement in categories where it is difficult to obtain samples, such as rare diseases. Few-shot learning has attracted a lot of attention in medical image analysis due to its ability to solve the problem of data scarcity. However, existing few-shot learning research in medical image analysis focuses on classification and segmentation, and the research on object detection has been neglected. In this paper, we propose a novel fetal anatomical structure few-shot detection method in ultrasound images, TKR-FSOD, which learns topological knowledge through a Topological Knowledge Reasoning Module to help the model reason about and detect anatomical structures. Furthermore, we propose a Discriminate Ability Enhanced Feature Learning Module that extracts abundant discriminative features to enhance the model's discriminative ability. Experimental results demonstrate that our method outperforms the state-of-the-art baseline methods, exceeding the second-best method with a maximum margin of 4. 8% on 5-shot of split 1 under four-chamber cardiac view.

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AAAI Conference 2024 Conference Paper

PMAC: Personalized Multi-Agent Communication

Xiangrui Meng
Ying Tan

Communication plays a crucial role in information sharing within the field of multi-agent reinforcement learning (MARL). However, how to transmit information that meets individual needs remains a long-standing challenge. Some existing work focus on using a common channel for information transfer, which limits the capability for local communication. Meanwhile, other work attempt to establish peer-to-peer communication topologies but suffer from quadratic complexity. In this paper, we propose Personalized Multi-Agent Communication (PMAC), which enables the formation of peer-to-peer communication topologies, personalized message sending, and personalized message receiving. All these modules in PMAC are performed using only multilayer perceptrons (MLPs) with linear computational complexity. Empirically, we show the strength of personalized communication in a variety of cooperative scenarios. Our approach exhibits competitive performance compared to existing methods while maintaining notable computational efficiency.

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AAMAS Conference 2023 Conference Paper

Learning Group-Level Information Integration in Multi-Agent Communication

Xiangrui Meng
Ying Tan

In multi-agent systems, it’s hard to make proper decisions for agents due to the partial observability of the environment. Among categories of multi-agent reinforcement learning (MARL) algorithms, communication learning is a common approach to solving this problem. However, existing work focus on individual-level communication which usually leads to significant communication costs. Meanwhile, the group feature couldn’t be well captured at the individual level. To tackle these problems, this paper proposes a group-level information integration model called Double Channel Communication Network (DC2Net). In DC2Net, individual and group features are learned in two independent channels. Agents no longer interact with each other at the individual level and all information interaction is carried out in the group channel. This model ensures effective learning of group features while reducing individual-level communication costs. Empirically, we conducted experiments on several environments and tasks. The experimental results show that the DC2Net not only has a better performance compared to other state-of-the-art MARL communication models but also reduces the costs of communication. Furthermore, it’s a natural communication topology with the ability in balancing individual and communication learning.

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AAAI Conference 2017 Conference Paper

Variational Autoencoder for Semi-Supervised Text Classification

Weidi Xu
Haoze Sun
Chao Deng
Ying Tan

Although semi-supervised variational autoencoder (SemiVAE) works in image classiﬁcation task, it fails in text classiﬁcation task if using vanilla LSTM as its decoder. From a perspective of reinforcement learning, it is veriﬁed that the decoder’s capability to distinguish between different categorical labels is essential. Therefore, Semi-supervised Sequential Variational Autoencoder (SSVAE) is proposed, which increases the capability by feeding label into its decoder RNN at each time-step. Two speciﬁc decoder structures are investigated and both of them are veriﬁed to be effective. Besides, in order to reduce the computational complexity in training, a novel optimization method is proposed, which estimates the gradient of the unlabeled objective function by sampling, along with two variance reduction techniques. Experimental results on Large Movie Review Dataset (IMDB) and AG’s News corpus show that the proposed approach signiﬁcantly improves the classiﬁcation accuracy compared with pure-supervised classiﬁers, and achieves competitive performance against previous advanced methods. State-of-the-art results can be obtained by integrating other pretraining-based methods.

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EAAI Journal 2002 Journal Article

Enhancing trajectory tracking for a class of process control problems using iterative learning

Jian-xin Xu
Tong-Heng Lee
Ying Tan

A method of enhancing tracking in repetitive processes, which can be approximated by a first-order plus dead-time model is presented. Enhancement is achieved through filter-based iterative learning control (ILC). The design of the ILC parameters is conducted in frequency domain, which guarantees the convergence property in iteration domain. The filter-based ILC can be easily added to existing control systems. To clearly demonstrate the features of the proposed ILC, a water heating process under a PI controller is used as a testbed. The empirical results show improved tracking performance with iterative learning.

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