Procesamiento óptico y físico para el análisis de lesiones musculares preexistentes en extremidades superiores

Introduction: Diagnosing muscle injuries is a challenge in low-access healthcare contexts. Integrating motion analysis techniques can favor and comprehensively improve these injuries analysis. Objective: To develop an optical and physical processing approach for analyzing pre-existing muscle injuries in the upper body extremities. Methods: An exploratory observational study was designed involving 7 Peruvian adults. Maneuvers were conducted to assess specific segments of the upper limb (conventional method), and the Skillspector software was used to collect and analyze biomechanical data based on optical and physical processing. Two methods were compared using 5 parameters of the Six Sigma methodology. Results: 71.4% of participants had injuries in the forearm, and 42.9% showed a significant change in biomechanical variability. The conventional method had a lower Sigma (3.45) than the alternative method (5.55). Despite its advantages, the complexity in data manipulation and susceptibility to variables were limitations of the alternative method during patient digital modeling. Conclusions: Optical and physical processing with Skillspector is highly effective in analyzing muscle injuries, surpassing the biomechanical maneuvers method.