Aurora Mobile App

Aurora is built on one principle: turn everyday listening into intentional emotional support. It was inspired by interviews with people with ADHD who use music to regulate focus and mood, but struggle with generic “focus playlists” and cannot easily remember what worked in past moments.

My main focus was reducing cognitive load while making emotional patterns visible. I wanted users to log a moment in seconds, then receive clear insights and state matched playlists that support focus, calm, or task initiation.

Aurora can be extended into a working product by integrating with existing streaming services, refining personalization models, and running longer pilot studies. I also plan to explore accessibility options and personalization controls so users can adjust the experience to their needs.

The rhythm synced visual mode came from a simple observation during research: many participants did not experience music only as sound. They described music as something that could “pull them back,” “slow things down,” or “give their mind a rhythm to follow.” During interviews with ten adults with ADHD, several people talked about using music not only to change mood, but to create structure around attention. This was an important shift in my thinking.At first, Aurora was focused mainly on playlists and journaling. But the more I listened to users, the more I realized that regulation is often sensory. Music has tempo, repetition, and energy. These qualities are felt in the body, not only heard. For users with ADHD, who may experience racing thoughts, distraction, or overstimulation, a steady rhythm can act as an anchor.That insight led to the visual mode. I wanted to extend the music experience from hearing to seeing, without making it visually noisy. The visuals respond to BPM and audio energy through soft, repeated motion. They are not designed as decorative effects. They are designed as gentle cues that help users stay with the rhythm and return to the present moment.The key design decision was restraint. The visuals had to be rhythmic enough to support focus, but calm enough to avoid overstimulation. This balance became central to the feature.

Aurora reframes everyday listening as a form of lightweight emotional support. Many people already use music to focus, calm down, start tasks, or recover from stress. However, most music platforms treat these behaviors as entertainment preferences rather than regulation patterns. Aurora starts from a different point of view: listening habits can carry emotional information, and that information can help users understand what supports them.For neurodiverse wellness design, this is an important shift. Support does not always need to feel clinical, heavy, or separate from daily life. It can be built into habits people already have. Music is familiar, accessible, and emotionally meaningful. By connecting music with mood journaling and pattern recognition, Aurora turns small listening moments into a clearer self regulation practice.I think designers in digital wellness can learn two things from Aurora. First, low effort interaction matters. If a tool asks too much from users during moments of stress or distraction, it will not be used consistently. Second, insight is more valuable than data display. Aurora does not ask users to analyze every entry. It highlights what mattered, such as when music helped reduce stress or improve focus.The broader cultural message is that neurodiverse support should respect real daily behavior. Instead of forcing users into rigid wellness routines, design can meet them through tools they already use.

Aurora’s state matched playlist model is based on the connection between three types of information: the music a user is listening to, the emotional state they log in that moment, and the later pattern of whether that music seemed to help. Each journal entry links a track with a time stamp, mood intensity values, and a short contextual note. Over time, these entries create a personal map of how different sounds relate to focus, calm, stress, or task initiation.This is different from genre based recommendation. A song is not categorized only as pop, ambient, or electronic. In Aurora, the meaning of a song is shaped by the user’s lived experience. For one user, a steady beat may help start work. For another, the same tempo may feel too activating. The system therefore learns from personal emotional context, not only from listening history.In practice, Aurora uses this pattern to generate playlists for a target state. For example, if a user wants to focus, the app can prioritize tracks that were previously linked with improved focus or reduced distraction. If the user wants to calm down, it can surface tracks associated with lower stress or more stable emotional patterns.For neurodiverse communities, this approach suggests a more respectful model of personalization. Instead of assuming that one “focus playlist” works for everyone, the product learns from individual regulation patterns. This can influence digital health tools by moving them away from generic content libraries and toward adaptive support based on real user context.