Developing a novel hybrid method based on dispersion entropy and adaptive boosting algorithm for human activity recognition.

Background: With the rapid development of technology, human activity recognition (HAR) from sensor data has become a key element for many real-world applications, such as healthcare, disease diagnosis and smart home systems. Although there have been several studies conducted on HAR, traditional methods remain inadequate in balancing efficiency, accuracy and speed. Moreover, existing studies have not identified a solution to managing imbalanced data in different activities groups of HAR, although that is major issue in determining satisfactory performance.

Methods: this study proposes a new hybrid approach involving hierarchical dispersion entropy (HDE) and Adaptive Boosting with convolutional neural networks (AdaB_CNN) for classifying human activities, such as running downstairs/upstairs, walking and other daily activities, from sensor data. The proposed model is comprised of the following steps: firstly, HAR data are segmented into intervals using a sliding window technique, and then the segmented data are decomposed into different frequency bands. Following this, the dispersion entropy of different frequency bands is computed to produce a feature vector set. Then, the extracted features are reduced using Joint Approximate Diagonalization of Eigenmatrices (JADE) to further eliminate redundant information. The final feature vector set is then fed into the AdaB_CNN method to classify human activities.

Results: The proposed approach is tested on three publicly available datasets: WISDM, UCI_HAR 2012, and PAMAP2. The experimental results demonstrate that the proposed model attains a superior performance in HAR to most current methods.

Conclusions: The findings reveal that the proposed HDE based AdaB_CNN model has the capability to efficiently recognize different activities from sensor technologies. It has the potential to be implemented in a hardware system to classify human activity.