This study assessed the accuracy and reliability of a 2D image-based deep learning algorithm for posture analysis by comparing it with a 3D motion capture system. Twenty healthy adult males participated, and nine balance parameters were measured using both methods: body tilt (ML/AP), shoulder tilt, pelvis tilt (ML/AP), knee tilt, left/right varus/valgus, and forward head posture. We evaluated agreement and reliability using root mean square error (RMSE), mean absolute error (MAE), Pearson correlation coefficients, and intraclass correlation coefficients (ICC). Most parameters exhibited RMSE and MAE within 3°, while forward head posture, pelvis tilt (AP), and varus/valgus had errors below 10°. High correlations were found for shoulder tilt (r = 0.886) and forward head posture (r = 0.681), whereas knee tilt and left varus/valgus showed lower correlations due to methodological differences. Both methods demonstrated high repeatability (3D: ICC > 0.90, 2D: ICC > 0.80), with moderate-to-high agreement between methods (ICC ≥ 0.5 for most parameters). Shoulder tilt (ICC = 0.919) and forward head posture (ICC = 0.799) showed particularly high agreement. These findings indicate that 2D image-based posture analysis can provide accurate and reliable assessments comparable to 3D motion capture, presenting a more accessible and cost-effective alternative for posture evaluation in clinical and research contexts.

Predicting fall risk is necessary for rescue and accident prevention in the elderly. In this study, deep learning regression models were used to predict the acceleration sum vector magnitude (SVM) peak value, which represents the risk of a fall. Twenty healthy adults (aged 22.0±1.9 years, height 164.9±5.9 cm, weight 61.4±17.1 kg) provided data for 14 common daily life activities (ADL) and 11 falls using IMU (Inertial Measurement Unit) sensors (Movella Dot, Netherlands) at the S2. The input data includes information from 0.7 to 0.2 seconds before the acceleration SVM peak, encompassing 6-axis IMU data, as well as acceleration SVM and angular velocity SVM, resulting in a total of 8 feature vectors used to model training. Data augmentations were applied to solve data imbalances. The data was split into a 4 : 1 ratio for training and testing. The models were trained using Mean Squared Error (MSE) and Mean Absolute Error (MAE). The deep learning model utilized 1D-CNN and LSTM. The model with data augmentation exhibited lower error values in both MAE (1.19 g) and MSE (2.93g²). Low-height falls showed lower predicted acceleration peak values, while ADLs like jumping and sitting showed higher predicted values, indicating higher risks.