Towards Clinical Application of Enhanced Timed Up and Go with Markerless Motion Capture and Machine Learning for Balance and Gait Assessment

Balance and gait impairments play a key role in falls among the elderly. Traditional clinical scales such as the Berg Balance Scale (BBS) to assess fall risk are often subjective, time consuming, and does not assess gait performance. Shorter assessments such as Timed Up and Go (TUG) are available, but most clinicians only look into the completion time. This study aimed to develop a fast, low-cost, and automated framework for balance function assessment and comprehensive gait analysis by enhancing the traditional TUG test with a markerless motion capture (MoCap) system and machine learning models. In total, we included TUG datasets of 70 participants with varying degrees of fall risk based on the BBS scores. We segmented TUG trials into five phases automatically using data from the MoCap system and extracted features from the phases. These features were then analyzed to identify those that significantly discriminate between high and low fall risk groups. Using the identified features, various machine learning models were tested to estimate the BBS scores. The markers obtained from the markerless MoCap system were used for detailed gait analysis, and lower limb kinematics were compared between the markerless and marker-based methods. Our findings indicate that individuals at high risk of falling had longer completion times, lower performance velocities, and smaller ranges of motion in lower-limb joints. Among the tested machine learning models, random forest demonstrated the best performance in predicting BBS scores (RMSE: 0.98, : 0.94). Additionally, our markerless MoCap system showed comparable accuracy to state-of-the-art systems, eliminating the need to attach markers or sensors. The findings could help develop a quick and objective tool for balance and gait assessment in older adults, providing quantitative data to improve screening and intervention planning.