GUD WIP: Gait-Understanding-Driven Walking-In-Place

Abstract

Many Virtual Environments require walking interfaces to explore virtual worlds much larger than available real-world tracked space. We present a model for generating virtual locomotion speeds from Walking-In-Place (WIP) inputs based on walking biomechanics. By employing gait principles, our model – called Gait-Understanding-Driven Walking-In-Place (GUD WIP) – creates output speeds which better match those evident in Real Walking, and which better respond to variations in step frequency, including realistic starting and stopping. The speeds output by our implementation demonstrate considerably less within-step fluctuation than a good current WIP system – Low-Latency, Continuous-Motion (LLCM) WIP – while still remaining responsive to changes in user input. We compared resulting speeds from Real Walking, GUD WIP, and LLCM-WIP via user study: The average output speeds for Real Walking and GUD WIP respond consistently with changing step frequency – LLCM-WIP is far less consistent. GUD WIP produces output speeds that are more locally consistent (smooth) and step-frequency-to-walk-speed consistent than LLCM-WIP.

A New Approach to Walking in Place

“…Different technologies have been used to detect steps in place: magnetic trackers [2,4,5], force sensors placed on shoe insoles [5], optical camera trackers [8], Wiimote Nintendo TM accelerometers [14] and Wii Balance Boards [13]. Several different body segment motions are tracked to generate virtual output, including the head [4], knees [5,10] and shins [2,8]. These evolutions in user input have enabled the improvement of footstep latency times (starting and stopping travel) [2,10] and have assured the continuity and smoothness of the movement between and within steps [2,8].…”

“…Several different body segment motions are tracked to generate virtual output, including the head [4], knees [5,10] and shins [2,8]. These evolutions in user input have enabled the improvement of footstep latency times (starting and stopping travel) [2,10] and have assured the continuity and smoothness of the movement between and within steps [2,8]. In addition, different types of virtual locomotion control laws are based on neural networks [2], knee pattern recognition [5], signal processing [2] and biomechanical state machines [8].…”

“…These evolutions in user input have enabled the improvement of footstep latency times (starting and stopping travel) [2,10] and have assured the continuity and smoothness of the movement between and within steps [2,8]. In addition, different types of virtual locomotion control laws are based on neural networks [2], knee pattern recognition [5], signal processing [2] and biomechanical state machines [8]. In reference to viewpoint movement, the Slater WIP system [4] updates a predefined virtual distance only when a footstep is detected.…”

“…This type of displacement motion is discrete and confusing for users. Later systems [2,8] updated the viewpoint in each frame and attempted to simulate a sinusoidal forward velocity curve.…”

Using Head Tracking Data for Robust Short Term Path Prediction of Human Locomotion

“…a physical gait model together with a Kalman filter could provide far better smoothing with low latency than exponential smoothing. Similar approaches used for so-called walk-in-place interaction devices might be adapted for this purpose [25].…”