Performance was evaluated using coefficient of determination ( \(R^2\)) and root mean square error (RMSE). Weight shifting was directly measured by 3D ground reaction forces (GRF) from force plates, and estimated using a linear regression model, a long-short term memory (LSTM) model and a decision tree model (XGBoost). Full-body 3D motion capture (3DMoCap) data and standard 2D digital video (2D-DV) was recorded. Twelve healthy older adults (mean age 72 (± 4.2), 10 F) played a custom exergame that required repeated weight-shifts. Therefore, the aim of this study was to investigate the performance of ML models in estimation of weight-shifting during exergaming using kinematic data. Machine learning (ML) models have been shown to perform well in weight-shifting estimation in other settings. Being able to useg such data to estimate weight-shifting would be a great advantage. Exergames often use kinematic data as input for game control. Providing biofeedback on weight-shifting during in-home balance exercise improves exercise efficiency, but suitable equipment for measuring weight-shifting is lacking. Exergames can be used for in-home guided exercise, which greatly increases availability and facilitates independence. Balance training exercise games (exergames) are a promising tool for reducing fall risk in elderly.
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