Daily Locomotion Recognition and Prediction: A Kinematic Data-Based Machine Learning Approach
Open accessAbstract
More versatile, user-independent tools for recognizing and predicting locomotion modes (LMs) and LM transitions (LMTs) in natural gaits are still needed. This study tackles these challenges by proposing an automatic, user-independent recognition and prediction tool using easily wearable kinematic motion sensors for innovatively classifying several LMs (walking direction, level-ground walking, ascend and descend stairs, and ascend and descend ramps) and respective LMTs. We compared diverse state-of-the-art feature processing and dimensionality reduction methods and machine-learning classifiers to find an effective tool for recognition and prediction of LMs and LMTs. The comparison included kinematic patterns from 10 able-bodied subjects. The more accurate tools were achieved using min-max scaling [-1; 1] interval and “mRMR plus forward selection” algorithm for feature normalization and dimensionality reduction, respectively, and Gaussian support vector machine classifier. The developed tool was accurate in the recognition (accuracy >99% and >96%) and prediction (accuracy >99% and >93%) of daily LMs and LMTs, respectively, using exclusively kinematic data. The use of kinematic data yielded an effective recognition and prediction tool, predicting the LMs and LMTs one-step-ahead. This timely prediction is relevant for assistive devices providing personalized assistance in daily scenarios. The kinematic data-based machine learning tool innovatively addresses several LMs and LMTs while allowing the user to self-select the leading limb to perform LMTs, ensuring a natural gait.
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Prior locomotion prediction systems that did not filter noise from raw sensor data had problematic outcomes.
“Though the proposed study achieved good accuracy results, it did not filter the noise from raw sensor data, causing problematic outcomes.”
From A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network (2023) · cites this paper as [11]
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- Authors
- Figueiredo, J., Carvalho, S. P., Goncalve, D., Moreno, J. C., & Santos, C. P.
- Affiliations
- University of Minho (PT)
- Consejo Superior de Investigaciones Científicas (ES)
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 33250-33262
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3005390993
- Cited by
- 37
- References
- 28
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 6 · 2023: 5 · 2022: 7 · 2021: 8 · 2020: 2
- FWCI
- 2.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Funders
- Fundação para a Ciência e a Tecnologia (SFRH/BD/108309/2015, SFRH/BD/147878/2019)
- FEDER Funds through the Programa Operacional Regional do Norte
- European Regional Development Fund (NORTE-01-0145-FEDER-030386, POCI-01-0145-FEDER-006941)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Keywords
- locomotion mode recognition, kinematic data, wearable motion sensors, gait classification, stair climbing, level walking, walking direction, feature selection, dimensionality reduction, support vector machine, assistive devices, user-independent recognition
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