{"title":"人体运动软件的去噪和补全滤波器:带代码的调查","authors":"Enrico Martini, Andrea Calanca, Nicola Bombieri","doi":"10.1016/j.cosrev.2025.100780","DOIUrl":null,"url":null,"abstract":"<div><div>Software platforms for human motion analysis are increasingly utilized across various fields, from Healthcare to Industry 5.0. However, the inherent inaccuracies of these platforms often lead to noisy observations of human poses or periods of missing information. As a result, data filtering for denoising or completion is a fundamental step before data analysis. Over the years, different techniques have been proposed, from general-purpose solutions based on low-pass filters to more advanced and embedded approaches based on state observers rather than deep learning. This survey presents the current state-of-the-art filtering solutions for denoising and completing data generated by software platforms for human motion analysis. It focuses on 3D positional data extrapolated through marker-based or marker-less motion capture systems. The survey proposes a concise taxonomy based on filter technology and application assumptions. For each class, it summarizes the basic concepts and reports application feedback collected from the literature. The survey also includes implementation codes or links to the authors’ original codes, enabling readers to quickly reproduce all the algorithms in different experimental settings (<span><span>https://github.com/PARCO-LAB/mocap-refinement</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"58 ","pages":"Article 100780"},"PeriodicalIF":12.7000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Denoising and completion filters for human motion software: A survey with code\",\"authors\":\"Enrico Martini, Andrea Calanca, Nicola Bombieri\",\"doi\":\"10.1016/j.cosrev.2025.100780\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Software platforms for human motion analysis are increasingly utilized across various fields, from Healthcare to Industry 5.0. However, the inherent inaccuracies of these platforms often lead to noisy observations of human poses or periods of missing information. As a result, data filtering for denoising or completion is a fundamental step before data analysis. Over the years, different techniques have been proposed, from general-purpose solutions based on low-pass filters to more advanced and embedded approaches based on state observers rather than deep learning. This survey presents the current state-of-the-art filtering solutions for denoising and completing data generated by software platforms for human motion analysis. It focuses on 3D positional data extrapolated through marker-based or marker-less motion capture systems. The survey proposes a concise taxonomy based on filter technology and application assumptions. For each class, it summarizes the basic concepts and reports application feedback collected from the literature. The survey also includes implementation codes or links to the authors’ original codes, enabling readers to quickly reproduce all the algorithms in different experimental settings (<span><span>https://github.com/PARCO-LAB/mocap-refinement</span><svg><path></path></svg></span>).</div></div>\",\"PeriodicalId\":48633,\"journal\":{\"name\":\"Computer Science Review\",\"volume\":\"58 \",\"pages\":\"Article 100780\"},\"PeriodicalIF\":12.7000,\"publicationDate\":\"2025-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Science Review\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574013725000565\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science Review","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574013725000565","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Denoising and completion filters for human motion software: A survey with code
Software platforms for human motion analysis are increasingly utilized across various fields, from Healthcare to Industry 5.0. However, the inherent inaccuracies of these platforms often lead to noisy observations of human poses or periods of missing information. As a result, data filtering for denoising or completion is a fundamental step before data analysis. Over the years, different techniques have been proposed, from general-purpose solutions based on low-pass filters to more advanced and embedded approaches based on state observers rather than deep learning. This survey presents the current state-of-the-art filtering solutions for denoising and completing data generated by software platforms for human motion analysis. It focuses on 3D positional data extrapolated through marker-based or marker-less motion capture systems. The survey proposes a concise taxonomy based on filter technology and application assumptions. For each class, it summarizes the basic concepts and reports application feedback collected from the literature. The survey also includes implementation codes or links to the authors’ original codes, enabling readers to quickly reproduce all the algorithms in different experimental settings (https://github.com/PARCO-LAB/mocap-refinement).
期刊介绍:
Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.