Window-Free IMU-Based Classification of Stair-Climbing Wheelchair Activities Using Machine Learning and Adaptive Boosting

To ensure safety, autonomy, and context-aware control, reliable state recognition is still a challenge for stair-climbing wheelchairs, which offer mobility in environments with steps, curbs, and landings. This study uses instantaneous Inertial Measurement Unit (IMU) data to develop a simplified, window-free method for classifying wheelchair stair-related activities. Instead of sliding-window preprocessing or temporal sequence modeling, this study uses an 18-channel feature set that includes orientation, gyroscope, accelerometer, magnetometer, linear acceleration, and gravity signals. Stratified evaluation and several metrics, such as accuracy, macro-F1, Matthews Correlation Coefficient (MCC), ROC-AUC, and per-class precision-recall, were used to systematically benchmark eight classifiers: Multinomial Logistic Regression (MLR), Gaussian Naïve Bayes (GNB), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), RBF-kernel Support Vector Classifier (SVC), a compact Multilayer Perceptron (MLP), and an adaptive CatBoost configuration. The results show that CatBoost performed almost flawlessly (Accuracy = 0.999, MCC = 0.999), closely followed by compact MLP. RF, KNN, and SVC formed a solid middle tier. In a window-free regime, feature importance analysis showed that instantaneous gyroscope and linear acceleration made very little contribution, while orientation and magnetometer channels were found to be the most crucial features. These results show that accurate and computationally efficient recognition of stair-climbing wheelchair states is possible by features driven by posture and heading. The proposed method facilitates low-latency embedded deployment and identifies areas for future development, such as lightweight temporal enhancements, angle encoding, and magnetometer calibration.