Discriminative human full-body pose estimation from wearable inertial sensor data

In this paper, a method is presented that allows reconstructing the full-body pose of a person in real-time, based on the limited input from a few wearable inertial sensors. Our method uses Gaussian Process Regression to learn the person-specific functional relationship between the sensor measurements and full-body pose. We generate training data by recording sample movements for different activities simultaneously using inertial sensors and an optical motion capture system. Since our approach is discriminative, pose prediction from sensor data is efficient and does not require manual initialization or iterative optimization in pose space. We also propose a SVM-based scheme to classify the activities based on inertial sensor data. An evaluation is performed on a dataset of movements, such as walking or golfing, performed by different actors. Our method is capable of reconstructing the full-body pose from as little as four inertial sensors with an average angular error of 4-6 degrees per joint, as shown in our experiments.