Sensor-Based Human Activity Recognition Using a Hybrid CNN-SVM Approach

Human Activity Recognition (HAR) is the process of interpreting human actions from sensor data. This paper presents a hybrid approach for HAR utilizing Convolutional Neural Network (CNN) for feature extraction and Support Vector Machine (SVM) for classification. The model is end-to-end trainable, where the SVM classifier replaces the softmax layer of the CNN. Evaluation of the approach was conducted on two benchmark datasets, UCI HAR and UniMiB SHAR, achieving accuracies of 96.13% and 87.85%, respectively. These results surpass those reported in the state-of-the-art and demonstrate the effectiveness of the proposed approach in interpreting human activities.