This study aims to predict the risk of cardiovascular complications in elderly patients after open-heart surgery based on multimodal data. I used single-lead electrocardiogram (ECG) signals and tri-axial accelerometer data from 80 patients, recorded through a chest-worn heart rate monitor during various physical tests, including the veloergometry test, six-minute walk test, stair climbing test, time up and go (TUG) test, and gait analysis on a treadmill. To ensure data reliability and analytical accuracy, I conducted multi-level preprocessing of the ECG and accelerometer data, including resampling, filtering, outlier detection and removal, and data segmentation. In data analysis, I initially employed machine learning algorithms, including decision trees, KNN, SVM, and random forests, to classify the types of physical activities (e.g., stair climbing, walking, and cycling) performed by the patients.