Stress and poor sleep remain pervasive challenges in modern life, yet traditional relaxation practices such as pranayama (breathing exercises) require guidance, discipline, and environments that are often difficult to sustain. VR–based relaxation tools have emerged as alternatives, but their abrupt immersion into fully virtual environments can feel disruptive and misaligned with the gradual nature of meditative practices. To address this gap, we collaborated with pranayama practitioners in a co-design process to develop Zenflow, an MR system that blends subtle visuals and breathing cues to gradually transform the user’s surroundings into a restorative virtual space. We evaluated the system in a 3 week within-subjects study (N=12), comparing traditional Pranayama with two variations of Zenflow. Results show that Zenflow transition design significantly improved self-reported sleep quality and objective measures of stress and sleep. Our work contributes design insights and evidence that gradual environmental transition can improve MR systems for stress management.

Unmanned Aerial Vehicles (UAVs) or drones, are increasingly used in search and rescue missions to detect human presence. Existing systems primarily leverage vision-based methods which are prone to fail under low-visibility or occlusion. Drone-based audio perception offers promise but suffers from extreme ego-noise that masks sounds indicating human presence. Existing datasets are either limited in diversity or synthetic, lacking real acoustic interactions, and there are no standardized setups for drone audition. To this end, we present DroneAudioset (The dataset is publicly available at https://huggingface.co/datasets/ahlab-drone-project/DroneAudioSet/ under the MIT license), a comprehensive drone audition dataset featuring 23.5 hours of annotated recordings, covering a wide range of signal-to-noise ratios (SNRs) from -57.2 dB to -2.5 dB, across various drone types, throttles, microphone configurations as well as environments. The dataset enables development and systematic evaluation of noise suppression and classification methods for human-presence detection under challenging conditions, while also informing practical design considerations for drone audition systems, such as microphone placement trade-offs, and development of drone noise-aware audio processing. This dataset is an important step towards enabling design and deployment of drone-audition systems.