Cross-Domain Self-Supervised Complete Geometric Representation Learning for Real-Scanned Point Cloud Based Pathological Gait Analysis

Gait analysis has traditionally been carried out in a laboratory environment using expensive equipment, but, recently, reliable, affordable, and wearable sensors have enabled integration into clinical applications as well as use during activities of daily living. Real-time gait analysis is key to the development of gait rehabilitation techniques and assistive devices such as neuroprostheses. This article presents a systematic review of wearable sensors and techniques used in real-time gait analysis, and their application to pathological gait. From four major scientific databases, we identified 1262 articles of which 113 were analyzed in full-text. We found that heel strike and toe off are the most sought-after gait events. Inertial measurement units (IMU) are the most widely used wearable sensors and the shank and foot are the preferred placements. Insole pressure sensors are the most common sensors for ground-truth validation for IMU-based gait detection. Rule-based techniques relying on threshold or peak detection are the most widely used gait detection method. The heterogeneity of evaluation criteria prevented quantitative performance comparison of all methods. Although most studies predicted that the proposed methods would work on pathological gait, less than one third were validated on such data. Clinical applications of gait detection algorithms were considered, and we recommend a combination of IMU and rule-based methods as an optimal solution.

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Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning

Proceedings of the 28th ACM International Conference on Information and Knowledge Management - CIKM '19 ◽

10.1145/3357384.3357989 ◽

2019 ◽

Cited By ~ 1

Author(s):

Zhuoren Jiang ◽

Jian Wang ◽

Lujun Zhao ◽

Changlong Sun ◽

Yao Lu ◽

...

Keyword(s):

Representation Learning ◽

Graph Representation ◽

Cross Domain

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Multi-part Representation Learning For Cross-domain Web Content Classification Using Neural Networks

Proceedings of the 28th ACM Conference on Hypertext and Social Media - HT '17 ◽

10.1145/3078714.3078745 ◽

2017 ◽

Author(s):

Ganesh J ◽

Himanshu Sharad Bhatt ◽

Manjira Sinha ◽

Shourya Roy

Keyword(s):

Neural Networks ◽

Representation Learning ◽

Web Content ◽

Cross Domain ◽

Content Classification

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An Applicative Survey on Few-Shot Learning

Recent Patents on Engineering ◽

10.2174/1872212115666210715121344 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jianwei Zhang ◽

Xubin Zhang ◽

Lei Lv ◽

Yining Di ◽

Wei Chen

Keyword(s):

Large Scale ◽

Representation Learning ◽

Language Models ◽

Data Sets ◽

Research Directions ◽

Large Scale Data ◽

Cross Domain ◽

Meta Learning ◽

Definition Of ◽

Future Work

Background: Learning discriminative representation from large-scale data sets has made a breakthrough in decades. However, it is still a thorny problem to generate representative embedding from limited examples, for example, a class containing only one image. Recently, deep learning-based Few-Shot Learning (FSL) has been proposed. It tackles this problem by leveraging prior knowledge in various ways. Objective: In this work, we review recent advances of FSL from the perspective of high-dimensional representation learning. The results of the analysis can provide insights and directions for future work. Methods: We first present the definition of general FSL. Then we propose a general framework for the FSL problem and give the taxonomy under the framework. We survey two FSL directions: learning policy and meta-learning. Results: We review the advanced applications of FSL, including image classification, object detection, image segmentation and other tasks etc., as well as the corresponding benchmarks to provide an overview of recent progress. Conclusion: FSL needs to be further studied in medical images, language models, and reinforcement learning in future work. In addition, cross-domain FSL, successive FSL, and associated FSL are more challenging and valuable research directions.

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AE-GAN-Net: Learning Invariant Feature Descriptor to Match Ground Camera Images and a Large-Scale 3D Image-Based Point Cloud for Outdoor Augmented Reality

Remote Sensing ◽

10.3390/rs11192243 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2243 ◽

Cited By ~ 1

Author(s):

Weiquan Liu ◽

Cheng Wang ◽

Xuesheng Bian ◽

Shuting Chen ◽

Wei Li ◽

...

Keyword(s):

Image Matching ◽

Point Cloud ◽

Spatial Relationship ◽

3D Image ◽

Image Patch ◽

3D Space ◽

Cross Domain ◽

Feature Descriptors ◽

Outdoor Environments ◽

2D And 3D

Establishing the spatial relationship between 2D images captured by real cameras and 3D models of the environment (2D and 3D space) is one way to achieve the virtual–real registration for Augmented Reality (AR) in outdoor environments. In this paper, we propose to match the 2D images captured by real cameras and the rendered images from the 3D image-based point cloud to indirectly establish the spatial relationship between 2D and 3D space. We call these two kinds of images as cross-domain images, because their imaging mechanisms and nature are quite different. However, unlike real camera images, the rendered images from the 3D image-based point cloud are inevitably contaminated with image distortion, blurred resolution, and obstructions, which makes image matching with the handcrafted descriptors or existing feature learning neural networks very challenging. Thus, we first propose a novel end-to-end network, AE-GAN-Net, consisting of two AutoEncoders (AEs) with Generative Adversarial Network (GAN) embedding, to learn invariant feature descriptors for cross-domain image matching. Second, a domain-consistent loss function, which balances image content and consistency of feature descriptors for cross-domain image pairs, is introduced to optimize AE-GAN-Net. AE-GAN-Net effectively captures domain-specific information, which is embedded into the learned feature descriptors, thus making the learned feature descriptors robust against image distortion, variations in viewpoints, spatial resolutions, rotation, and scaling. Experimental results show that AE-GAN-Net achieves state-of-the-art performance for image patch retrieval with the cross-domain image patch dataset, which is built from real camera images and the rendered images from 3D image-based point cloud. Finally, by evaluating virtual–real registration for AR on a campus by using the cross-domain image matching results, we demonstrate the feasibility of applying the proposed virtual–real registration to AR in outdoor environments.

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Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data

Applied Sciences ◽

10.3390/app10228073 ◽

2020 ◽

Vol 10 (22) ◽

pp. 8073

Author(s):

Min Woo Ryu ◽

Sang Min Oh ◽

Min Ju Kim ◽

Hun Hee Cho ◽

Chang Baek Son ◽

...

Keyword(s):

Point Cloud ◽

Indoor Environment ◽

Three Dimensional ◽

Satellite System ◽

Normal Vector ◽

Geometric Representation ◽

Point Cloud Data ◽

Cloud Data ◽

Data Process ◽

Global Navigation Satellite

This study proposes a new method to generate a three-dimensional (3D) geometric representation of an indoor environment by refining and processing an indoor point cloud data (PCD) captured through backpack laser scanners. The proposed algorithm comprises two parts to generate the 3D geometric representation: data refinement and data processing. In the refinement section, the inputted indoor PCD are roughly segmented by applying random sample consensus (RANSAC) to raw data based on an estimated normal vector. Next, the 3D geometric representation is generated by calculating and separating tangent points on segmented PCD. This study proposes a robust algorithm that utilizes the topological feature of the indoor PCD created by a hierarchical data process. The algorithm minimizes the size and the uncertainty of raw PCD caused by the absence of a global navigation satellite system and equipment errors. The result of this study shows that the indoor environment can be converted into 3D geometric representation by applying the proposed algorithm to the indoor PCD.

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