Objective: To review the methodologies and outcomes of kinematic analysis using R and Python in robotic-assisted balance training, highlighting their role in enhancing rehabilitation strategies.
Background: Robotic-assisted balance training has emerged as a key intervention for individuals with neurological and musculoskeletal impairments. Advanced kinematic analysis, powered by R and Python, enables precise movement tracking, biomechanical assessment, and real-time feedback. However, the application of these tools remains fragmented across studies, necessitating a comprehensive review of their methodologies and outcomes.
Method: A systematic review of peer-reviewed studies was conducted using PubMed, IEEE Xplore, Scopus, and Web of Science databases up to March 2024. Inclusion criteria encompassed studies utilizing R or Python for kinematic data analysis in robotic-assisted balance training. Extracted data included analytical frameworks, motion capture techniques, statistical modeling approaches, and rehabilitation outcomes. Descriptive and meta-analytical techniques were applied to synthesize findings.
Results: A total of 25 studies (n = 890 participants) met inclusion criteria. Python-based machine learning models improved motion classification accuracy by 17%, while R-based statistical models enhanced gait variability assessment by 22%. Studies incorporating real-time kinematic feedback demonstrated superior motor adaptation (p < 0.001). Additionally, robotic platforms integrated with Python libraries such as NumPy and SciPy facilitated precise force and trajectory analysis, whereas R’s linear mixed-effects modeling provided robust longitudinal assessments of motor recovery
Conclusion: The integration of R and Python in kinematic analysis has significantly advanced robotic-assisted balance training by improving motion quantification and rehabilitation efficiency. However, standardization of analytical pipelines and interoperability between software platforms remain key challenges. Future research should focus on unified frameworks leveraging both languages for enhanced real-time biomechanical assessment and adaptive rehabilitation strategies.
To cite this abstract in AMA style:
M. Ali, Z. Hegazy, K. Ahmed, O. Sabry, S. Elsenbawy, G. Abozeid, M. M. Elsayed. Using R and Python for Kinematic Analysis in Robotic-Assisted Balance Training: A Review of Methodologies and Outcomes [abstract]. Mov Disord. 2025; 40 (suppl 1). https://www.mdsabstracts.org/abstract/using-r-and-python-for-kinematic-analysis-in-robotic-assisted-balance-training-a-review-of-methodologies-and-outcomes/. Accessed November 20, 2025.« Back to 2025 International Congress
MDS Abstracts - https://www.mdsabstracts.org/abstract/using-r-and-python-for-kinematic-analysis-in-robotic-assisted-balance-training-a-review-of-methodologies-and-outcomes/