RNN-Aided Human Velocity Estimation from a Single IMU.

Pedestrian Dead Reckoning (PDR) uses inertial measurement units (IMUs) and combines velocity and orientation estimates to determine a position. The estimation of the velocity is still challenging, as the integration of noisy acceleration and angular speed signals over a long period of time causes large drifts. Classic approaches to estimate the velocity optimize for specific applications, sensor positions, and types of movement and require extensive parameter tuning. Our novel hybrid filter combines a convolutional neural network (CNN) and a bidirectional recurrent neural network (BLSTM) (that extract spatial features from the sensor signals and track their temporal relationships) with a linear Kalman filter (LKF) that improves the velocity estimates. Our experiments show the robustness against different movement states and changes in orientation, even in highly dynamic situations. We compare the new architecture with conventional, machine, and deep learning methods and show that from a single non-calibrated IMU, our novel architecture outperforms the state-of-the-art in terms of velocity (≤0.16 m/s) and traveled distance (≤3 m/km). It also generalizes well to different and varying movement speeds and provides accurate and precise velocity estimates.

Sick Moves! Motion Parameters as Indicators of Simulator Sickness.

We explore motion parameters, more specifically gait parameters, as an objective indicator to assess simulator sickness in Virtual Reality (VR). We discuss the potential relationships between simulator sickness, immersion, and presence. We used two different camera pose (position and orientation) estimation methods for the evaluation of motion tasks in a large-scale VR environment: a simple model and an optimized model that allows for a more accurate and natural mapping of human senses. Participants performed multiple motion tasks (walking, balancing, running) in three conditions: a physical reality baseline condition, a VR condition with the simple model, and a VR condition with the optimized model. We compared these conditions with regard to the resulting sickness and gait, as well as the perceived presence in the VR conditions. The subjective measures confirmed that the optimized pose estimation model reduces simulator sickness and increases the perceived presence. The results further show that both models affect the gait parameters and simulator sickness, which is why we further investigated a classification approach that deals with non-linear correlation dependencies between gait parameters and simulator sickness. We argue that our approach could be used to assess and predict simulator sickness based on human gait parameters and we provide implications for future research.