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3D Interaction Design: Increasing the stimulus-response correspondence by using stereoscopic vision
(2015)
This paper presents a hand-based interaction approach for grabbing and manipulating virtual objects within immersive virtual environments. Considering the stimulus-response of humans, interacting with objects is easier if depth information is provided by stereoscopic vision. However, most people are used to work in monoscopic virtual environments. Therefore, we propose a case study, to analyze if an interaction approach using stereoscopic vision, compared to a conventional monoscopic vision, results in a higher user acceptance and stimulus-response correspondence. Our interaction approach is based on a robust and fast running 3D hand- and head-tracking provided by Microsoft's Kinect. We present a simple non-collision-based method for grabbing and manipulating objects within a 3D virtual environment. By tracking the head, the user is able to move within the environment and interact depending on his current position. The use of stereoscopic vision allows a natural and instinctive hand interaction with the augmented object. Finally, the experimental results and studies show how the use of stereoscopic vision improves the mental mapping and thus the user acceptance and stimulus-response correspondence in consideration of using the here presented interaction approach.
The Smartphone-Based Offline Indoor Location Competition at IPIN 2016: Analysis and Future Work
(2017)
Comparison of 2.4 GHz WiFi FTM- and RSSI-Based Indoor Positioning Methods in Realistic Scenarios
(2020)
We propose a modeling approach for position estimation based on the observed radio propagation in an environment. The approach is purely similarity-based and therefore free of explicit physical assumptions. What distinguishes it from classical related methods are probabilistic position estimates. Instead of just providing a point estimate for a given signal sequence, our model returns the distribution of possible positions as continuous probability density function, which allows for appropriate integration into recursive state estimation systems. The estimation procedure starts by using a kernel to compare incoming data with reference recordings from known positions. Based on the obtained similarities, weights are assigned to the reference positions. An arbitrarily chosen density estimation method is then applied given this assignment. Thus, a continuous representation of the distribution of possible positions in the environment is provided. We apply the solution in a Particle Filter (PF) system for smartphone-based indoor localization. The approach is tested both with radio signal strength (RSS) measurements (Wi-Fi and Bluetooth Low Energy RSSI) and round-trip time (RTT) measurements, given by Wi-Fi Fine Timing Measurement. Compared to distance-based models, which are dedicated to the specific physical properties of each measurement type, our similarity-based model achieved overall higher accuracy at tracking pedestrians under realistic conditions. Since it does not explicitly consider the physics of radio propagation, the proposed model has also been shown to work flexibly with either RSS or RTT observations.