Human Activity Recognition with IMU and Vital Signs Feature Fusion

Combining data from different sources into an integrated view is a recent trend taking advantage of the Internet of Things (IoT) evolution over the last years. The fusion of different modalities has applications in various fields, including healthcare and security systems. Human activity recognition (HAR) is among the most common applications of a healthcare or eldercare system. Inertial measurement unit (IMU) wearable sensors, like accelerometers and gyroscopes, are often utilized for HAR applications. In this paper, we investigate the performance of wearable IMU sensors along with vital signs sensors for HAR. A massive feature extraction, including both time and frequency domain features and transitional features for the vital signs, along with a feature selection method were performed. The classification algorithms and different early and late fusion methods were applied to a public dataset. Experimental results revealed that both IMU and vital signs achieve reasonable HAR accuracy and Fl-score among all the classes. Feature selection significantly reduced the number of features from both IMU and vital signs features while also improved the classification accuracy. The rest of the early and late level fusion methods also performed better than each modality alone, reaching an accuracy level of up to 95.32%.