Physical human locomotion prediction using manifold regularization
Open accessAbstract
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.
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Prior locomotion prediction systems that lacked good features had lower accuracy rates.
“However, the lack of good features caused lower accuracy rates.”
From A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network (2023) · cites this paper as [12]
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Cited by
Sorted by citations
Javeed, M., Mudawi, N. A., Alabduallah, B. I., Jalal, A., & Kim, W. (2023) · Sensors · doi:10.3390/s23104716 · Cited by 18 · see citing works
Self-citation
- Authors
- Javeed, M., Shorfuzzaman, M., Alsufyani, N., Chelloug, S. A., Jalal, A., & Park, J.
- Affiliations
- Air University (PK)
- Taif University (SA)
- Princess Nourah bint Abdulrahman University (SA)
- Tech University of Korea (KR)
- Corresponding author
- Chelloug, Samia Allaoua
- Park, Jeongmin
- ORCID
- Venue
- PeerJ Computer Science
- Publisher
- PeerJ
- Published
- 2022-10-12
- Volume
- 8
- Pages
- e1105
- ISSN
- 2376-5992 (ISSN-L)
- PMC
- PMC9575869
- OpenAlex
- W4304811509
- Cited by
- 18
- References
- 57
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 3 · 2023: 8 · 2022: 2
- FWCI
- 1.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PeerJ Computer Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- MSIT (Ministry of Science and ICT), Korea
- ITRC (Information Technology Research Center) Support Program (IITP-2023-2018-0-01426)
- IITP
- Deanship of Scientific Research at Princess Nourah bint Abdulrahman University
- Princess Nourah bint Abdulrahman University Researchers Supporting Project Number (PNURSP2022R239)
- Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
- Taif University Researchers, Taif University, Taif, Saudi Arabia (TURSP-2020/79)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Human Motion and Animation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- manifold regularization, inertial signals, physiological signals, Bessel filter, feature extraction, quadratic discriminant analysis
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