Walking in Place

Moving through a virtual environment takes some method of locomotion. This work uses walking in place and adapts the method of step detection to be taken from the headset. This offers a low-cost (from the device) way of measuring steps. Here we present a number of projects using this work, and show how deep neural nets can be used to make this recognition better

  • Deep Neural Nets
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Deep Neural Nets

Walking in Place

Walking in place is a standard method for moving through large virtual environments when physical space or positional tracking is limited. This technique has become increasingly prominent with the advent of mobile virtual reality in which external tracking may not be present. In this paper, we revisit walking in place algorithms to address some of their technical challenges. Namely, our solutions attend to improving starting, stopping, and speed control for individual users. From a hand-tuned threshold based algorithm, we provide a new, fast method for individualizing the walking in place algorithm based on biomechanic measures of step rate. In addition, we introduce a new walking in place model based on a convolutional neural network trained to differentiate walking and standing. Over two experiments we assess these methods against a traditional threshold based algorithm on two mobile virtual reality platforms. The assessments are based on controllability, scale, and presence. Our results suggest that an adequately trained convolutional neural network can be an effective way of implementing walking in place.

Improving Walking in Place Methods with Individualization and Deep Networks
Sara Hanson, Richard A. Paris, Haley Adams, and Bobby Bodenheimer
IEEE Virtual Reality 2019

Work

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