An Approach towards Position-Independent Human Activity Recognition Model based on Wearable Accelerometer Sensor
Naima Qamar, Nasir Siddiqui, Muhammad Ehatisham-ul-Haq, Muhammad Awais Azam, Usman Naeem
Abstract
The continuous progress in wearable sensing technologies has motivated the researchers to develop novel models for human activity and behavior monitoring. As wearable sensors possess more liberty in their placement at multiple positions on the user’s body to track human motion patterns, hence, they have been extensively utilized in activity recognition systems. However, wearable inertial sensors are prone to their position and orientation sensitivity, thus leading to poor recognition performance in real-time scenarios. Therefore, in this study, we address the problem of position-independent human activity recognition using the wearable sensor. In this aspect, we propose a set of linear and non-linear transformations for 3D-sensor data to minimize the position and orientation sensitivity of the inertial sensor. We also present a feature extraction framework to efficiently recognize human activities independent of any sensor position. Finally, we validate our proposed scheme using the PAMAP dataset, which achieves the best average performance of 94.7% and 91.7% for position-dependent and position-independent activity recognition.