Equine Kinematic Gait Analysis Using Stereo Videography and Deep Learning: Stride Length and Stance Duration Estimation

Niknejad, Nariman; Jessica, Lauder; Bidese-Puhl, Rafael; Bao, Yuhai; Staiger, Elizabeth A.

doi:10.13031/ja.15386

Cited by 2 publications

References 43 publications

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The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

Amiri,

Heidari,

Navimipour

et al. 2024

Neural Comput & Applic

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Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability.

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The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

Amiri,

Heidari,

Navimipour

et al. 2024

Neural Comput & Applic

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Anti-drift pose tracker (ADPT): A transformer-based network for robust animal pose estimation cross-species

Tang,

Han,

Sun

et al. 2024

Preprint

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Deep learning-based methods for animal pose estimation have recently made substantial progress in improving the accuracy and efficiency of quantitative descriptions of animal behavior. However, these methods commonly suffer from tracking drifts, i.e., sudden jumps in the estimated position of a body point due to noise, thus reducing the reliability of behavioral study results. Here, we present a transformer-based animal pose estimation tool, called Anti-Drift Pose Tracker (ADPT), for eliminating tracking drifts in behavior analysis. To verify the anti-drift performance of ADPT, we conduct extensive experiments in multiple cross-species datasets, including long-term recorded mouse and monkey behavioral datasets collected by ourselves, as well as two public Drosophilas and macaques datasets. Our results show that ADPT greatly reduces the rate of tracking drifts, and significantly outperforms the existing deep-learning methods, such as DeepLabCut, SLEAP, and DeepPoseKit. Moreover, ADPT is compatible with multi-animal pose estimation, enabling animal identity recognition and social behavioral study. Specifically, ADPT provided an identification accuracy of 93.16% for 10 unmarked mice, and of 90.36% for free-social unmarked mice which can be further refined to 99.72%. Compared to other multi-stage network-based tools like multi-animal DeepLabCut, SIPEC and Social Behavior Atlas, the end-to-end structure of ADPT supports its lower computational costs and meets the needs of real-time analysis. Together, ADPT is a versatile anti-drift animal behavior analysis tool, which can greatly promote the accuracy, robustness, and reproducibility of animal behavioral studies. The code of ADPT is available at https://github.com/tangguoling/ADPT.

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Equine Kinematic Gait Analysis Using Stereo Videography and Deep Learning: Stride Length and Stance Duration Estimation

Cited by 2 publications

References 43 publications

The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

The deep learning applications in IoT-based bio- and medical informatics: a systematic literature review

Anti-drift pose tracker (ADPT): A transformer-based network for robust animal pose estimation cross-species

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