Digital healthcare for the elderly: Smartphone-based joint angle analysis during sit-to-stand

Accurate kinematic analysis of sit-to-stand (STS) movements in elderly populations is crucial for assessing mobility and physical fitness. This study compares the validity and reliability of a novel smartphone application (App) for kinematic analysis during STS tests against optical motion capture systems (OMCs). We analyzed data from 57 trials involving elderly participants using both systems. The STS App used Linear Regression (LR) to refine polynomial equations, minimizing discrepancies in trunk, hip, and knee joint angle measurements relative to OMCs. The predictive model achieved high accuracy, with R2 values of 0.92-0.96 across joints. MAE and RMSE values showed good agreement between the STS App and OMCs, indicating effective polynomial coefficient optimization. No significant differences were observed between the STS App and the OMCs across all parameters. The STS App’s automated features improved data collection and accuracy, mitigating manual timekeeping biases. Time duration comparisons between the STS App and the OMCs showed no significant differences, further supporting the STS App’s applicability in real-world scenarios. Future enhancements should focus on improving the approach to offer a cost-effective and practical alternative for kinematic analysis, with significant potential for clinical and community applications.