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Wen Ku

Publications and source records attributed to Wen Ku.

2 recordsLinked to original sources

Glide-in-Place: Foot-Steered Differential-Drive for Hands-Free VR Locomotion

Seated VR locomotion in constrained environments, including homes, offices, and transit settings, calls for hardware that is lightweight and deployable, steering that remains continuous enough for curved motion, and a control channel that leaves the hands free for concurrent interaction. Inspired by the steering logic of self-balancing scooters, we present Glide-in-Place, a seated foot locomotion system that maps per-foot fore-aft pressure to a differential-drive model: the two feet act as virtual wheels whose relative drive continuously determines translation and yaw. This lets users move forward, rotate in place, and follow arcs in one unified vocabulary without hand-held input or discrete mode switches. We evaluated Glide-in-Place in a counterbalanced within-subject study with 16 participants against two baselines: joystick control and a seated walking-in-place technique with discrete snap motions. Across two steering-heavy navigation tasks, zig-zag path following with multitasking and curved-path traversal, Glide-in-Place was consistently faster than Seated-WIP, reduced physical demand, and lowered fatigue-related discomfort without significantly differing from joystick control on total VRSQ. We position Glide-in-Place as a deployable hardware-control design point for constrained seated VR: thin insole sensing, continuous foot steering, and lightweight calibration packaged in one compact artifact.

cs.HC

GenPod: Constructive News Framing in AI-Generated Podcasts More Effectively Reduces Negative Emotions Than Non-Constructive Framing

AI-generated media products are increasingly prevalent in the news industry, yet their impacts on audience perception remain underexplored. Traditional media often employs negative framing to capture attention and capitalize on news consumption, and without oversight, AI-generated news could reinforce this trend. This study examines how different framing styles-constructive versus non-constructive-affect audience responses in AI-generated podcasts. We developed a pipeline using generative AI and text-to-speech (TTS) technology to create both constructive and non-constructive news podcasts from the same set of news resources. Through empirical research (N=65), we found that constructive podcasts significantly reduced audience's negative emotions compared to non-constructive podcasts. Additionally, in certain news contexts, constructive framing might further enhance audience self-efficacy. Our findings show that simply altering the framing of AI generated content can significantly impact audience responses, and we offer insights on leveraging these effects for positive outcomes while minimizing ethical risks.

cs.HC