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Identifying Gait-Related Functional Outcomes in Post-Knee Surgery Patients Using Machine Learning: A Systematic
Christos Kokkotis1, Georgios Chalatsis2, Serafeim Moustakidis3
1Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece.
Summary
Advanced machine learning algorithms can analyze gait data to assess knee surgery recovery. This technology offers a non-invasive, cost-effective way to guide personalized patient rehabilitation and return to daily activities.
Area of Science:
- Biomedical Engineering
- Rehabilitation Science
- Data Science
Background:
- Modern lifestyles necessitate objective tools for assessing post-knee surgery functional recovery.
- Existing quantitative instruments often lack high discrimination, non-invasiveness, or affordability.
Approach:
- A systematic literature review was conducted following PRISMA guidelines across Scopus, PubMed, and Semantic Scholar.
- Six studies out of 405 were included, focusing on machine learning algorithms applied to gait data for recovery assessment.
Key Points:
- Machine learning (ML) offers a promising approach to meet the requirements for quantitative recovery assessment tools.
- Analysis of gait-related changes using ML algorithms can effectively determine functional recovery status.
- Recent literature shows a rise in sophisticated ML techniques for personalized post-treatment interventions.
Conclusions:
- ML-driven gait analysis provides robust decision-making support for knee surgery patients.
- This approach facilitates personalized rehabilitation strategies, improving the return to daily activities.
- Advanced ML algorithms are crucial for bridging knowledge gaps in quantitative functional recovery assessment.
