Adaptive predictive systems applied to gait analysis: A systematic review
Abstract
Background Due to the high susceptivity of the walking pattern to be affected by several disorders, accurate analysis methods are necessary. Given the complexity and relevance of such assessment, the utilization of methods to facilitate it plays a significant role, provided that they do not compromise the outcomes.Research questions This paper aimed at identifying the standards for the application of adaptive predictive systems to gait analysis, given the extensive research on this field. Furthermore, we also intended to check whether such methods can effectively support clinicians in determining the number of physiotherapy sessions necessary to recover gait-related dysfunctions.Methods Through a screening process of scientific databases, we considered studies encompassed from 1968 to April 2019. Within these 50 years, we found 24 papers that met our inclusion criteria. They were analyzed according to their data acquisition and processing methods via ad hoc questionnaires. Additionally, we examined quantitatively the adaptive approaches.Results Concerning data acquisition, the included papers presented a mean score of 6.1 SD 1.0, most of them applying optoelectronic systems, and the ground reaction force (GRF) was the most used parameter. The AI quality assessment showed an above-average rate of 7.8 SD 1.0, and artificial neural networks (ANN) being the paradigm most frequently utilized. Our systematic review identified only one study that addressed therapeutics including a predictive method.Significance While much progress has been identified to predict assessment aspects, there is little effort to assist healthcare professionals in establishing the rehabilitation duration and prognostics. Therefore, future studies should focus on accomplishing the production of applications of predictive methods to therapeutics and prognosis, not lingering extremely on the analysis of gait features.
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Inertial sensors analyze motion state by detecting mechanical changes such as acceleration and angular velocity, and are used for joint angle recognition, gait and posture analysis, and orientation perception.
“Analyze motion state by detecting mechanical | changes such as acceleration and angular | velocity.”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [199]
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- Authors
- Caldas, R., Fadel, T., Buarque, F., & Markert, B.
- Affiliations
- RWTH Aachen University (DE)
- Universidade de Pernambuco (BR)
- Corresponding author
- Caldas, Rafael
- Venue
- Gait & Posture
- Publisher
- Elsevier BV
- Published
- 2020-03
- Volume
- 77
- Pages
- 75-82
- ISSN
- 0966-6362 (ISSN-L)
- OpenAlex
- W3002741736
- Cited by
- 50
- References
- 38
- Citations by year
- 2026: 6 · 2025: 6 · 2024: 9 · 2023: 9 · 2022: 6 · 2021: 12 · 2020: 2
- FWCI
- 0.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Gait & Posture (publishedVersion)
- PubMed (publishedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (310121/3015-9, CNPQ 207445/2014-1)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- gait analysis, rehabilitation duration, prognosis, artificial neural networks, optical motion capture, ground reaction force, physiotherapy
- MeSH terms
- Gait Analysis, Biomechanical Phenomena, Gait, Humans, Musculoskeletal Diseases, Walking, Neural Networks, Computer, Gait Disorders, Neurologic, Physical Therapy Modalities
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