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Physical human locomotion prediction using manifold regularization
Human locomotion is an imperative topic to be conversed among researchers. Predicting the human motion using multiple techniques and algorithms has always been a motivating subject matter. For this, different methods have shown the ability of recognizing simple motion patterns. However, predicting t...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575869/ https://www.ncbi.nlm.nih.gov/pubmed/36262158 http://dx.doi.org/10.7717/peerj-cs.1105 |
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author | Javeed, Madiha Shorfuzzaman, Mohammad Alsufyani, Nawal Chelloug, Samia Allaoua Jalal, Ahmad Park, Jeongmin |
author_facet | Javeed, Madiha Shorfuzzaman, Mohammad Alsufyani, Nawal Chelloug, Samia Allaoua Jalal, Ahmad Park, Jeongmin |
author_sort | Javeed, Madiha |
collection | PubMed |
description | Human locomotion is an imperative topic to be conversed among researchers. Predicting the human motion using multiple techniques and algorithms has always been a motivating subject matter. For this, different methods have shown the ability of recognizing simple motion patterns. However, predicting the dynamics for complex locomotion patterns is still immature. Therefore, this article proposes unique methods including the calibration-based filter algorithm and kinematic-static patterns identification for predicting those complex activities from fused signals. Different types of signals are extracted from benchmarked datasets and pre-processed using a novel calibration-based filter for inertial signals along with a Bessel filter for physiological signals. Next, sliding overlapped windows are utilized to get motion patterns defined over time. Then, polynomial probability distribution is suggested to decide the motion patterns natures. For features extraction based kinematic-static patterns, time and probability domain features are extracted over physical action dataset (PAD) and growing old together validation (GOTOV) dataset. Further, the features are optimized using quadratic discriminant analysis and orthogonal fuzzy neighborhood discriminant analysis techniques. Manifold regularization algorithms have also been applied to assess the performance of proposed prediction system. For the physical action dataset, we achieved an accuracy rate of 82.50% for patterned signals. While, the GOTOV dataset, we achieved an accuracy rate of 81.90%. As a result, the proposed system outdid when compared to the other state-of-the-art models in literature. |
format | Online Article Text |
id | pubmed-9575869 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95758692022-10-18 Physical human locomotion prediction using manifold regularization Javeed, Madiha Shorfuzzaman, Mohammad Alsufyani, Nawal Chelloug, Samia Allaoua Jalal, Ahmad Park, Jeongmin PeerJ Comput Sci Bioinformatics Human locomotion is an imperative topic to be conversed among researchers. Predicting the human motion using multiple techniques and algorithms has always been a motivating subject matter. For this, different methods have shown the ability of recognizing simple motion patterns. However, predicting the dynamics for complex locomotion patterns is still immature. Therefore, this article proposes unique methods including the calibration-based filter algorithm and kinematic-static patterns identification for predicting those complex activities from fused signals. Different types of signals are extracted from benchmarked datasets and pre-processed using a novel calibration-based filter for inertial signals along with a Bessel filter for physiological signals. Next, sliding overlapped windows are utilized to get motion patterns defined over time. Then, polynomial probability distribution is suggested to decide the motion patterns natures. For features extraction based kinematic-static patterns, time and probability domain features are extracted over physical action dataset (PAD) and growing old together validation (GOTOV) dataset. Further, the features are optimized using quadratic discriminant analysis and orthogonal fuzzy neighborhood discriminant analysis techniques. Manifold regularization algorithms have also been applied to assess the performance of proposed prediction system. For the physical action dataset, we achieved an accuracy rate of 82.50% for patterned signals. While, the GOTOV dataset, we achieved an accuracy rate of 81.90%. As a result, the proposed system outdid when compared to the other state-of-the-art models in literature. PeerJ Inc. 2022-10-12 /pmc/articles/PMC9575869/ /pubmed/36262158 http://dx.doi.org/10.7717/peerj-cs.1105 Text en © 2022 Javeed et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Javeed, Madiha Shorfuzzaman, Mohammad Alsufyani, Nawal Chelloug, Samia Allaoua Jalal, Ahmad Park, Jeongmin Physical human locomotion prediction using manifold regularization |
title | Physical human locomotion prediction using manifold regularization |
title_full | Physical human locomotion prediction using manifold regularization |
title_fullStr | Physical human locomotion prediction using manifold regularization |
title_full_unstemmed | Physical human locomotion prediction using manifold regularization |
title_short | Physical human locomotion prediction using manifold regularization |
title_sort | physical human locomotion prediction using manifold regularization |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575869/ https://www.ncbi.nlm.nih.gov/pubmed/36262158 http://dx.doi.org/10.7717/peerj-cs.1105 |
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