Aurora Basa Rekutanga: Application yeFoni Inofambiswa by Ping Chen

Aurora Basa Rekutanga: Application yeFoni Inofambiswa by Ping Chen is a highly esteemed winner of the very prestigious 2026 Mubairo weSimbi we A' Design Award in Tekinorojii yeFoni, Maapurikesheni neSofutiweya Competition Category.

SimbiFoni· 2026

Aurora Basa Rekutanga: Application yeFoni Inofambiswa

by Ping Chen

Client: Aurora

Mubairo weSimbi we A' Design Award Winner in Foni Category

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Colors

The Design

About Aurora Basa Rekutanga: Application yeFoni Inofambiswa

Aurora i-application yefoni inofambiswa inotsigira kudzora manzwiro evanhu vane Adhd kuburikidza nekudyidzana kunoshandisa mimhanzi. Chiso chechirongwa chinosanganisa kugadzirwa kwemazita enziyo anoenderana nemamiriro emanzwiro, dhayari rine manzwiro akasiyana-siyana, uye pfupiso dzemamiriro ezuva nezuva. Chinyorwa chimwe nechimwe chedhayari chinobatanidza rwiyo rwuri kuridzwa nezvishandiso zvinoyera kusimba kwemanzwiro pamwe nenote pfupi. Maonero emaitiro anoratidza nguva dzakakosha apo mimhanzi wakavandudza kutarisisa kana kuderedza kushushikana. Maitiro anoonekwa anoenderana nedunhu rwiyo anopa zviratidzo zvekufamba zvinyoro panguva yekuteerera kutsigira kudzika midzi.

The Recognition

About the Mubairo weSimbi we A' Design Award

Mubairo weSimbi we A' Design Award wakahwinwa ne Aurora Basa Rekutanga: Application yeFoni Inofambiswa, rukudzo runokudzwa runopiwa kumadhizaini akanaka anozadzisa zvinodiwa zvehunyanzvi neyeindasitiri zvinotarisirwa kuzvigadzirwa zvakagadzirwa zvakanaka. Vakunda veSimbi vanoratidza unyanzvi hukuru, hunyanzvi hwakakwirira, kunzwisisa kwevakachenjera uye kugona kwekugadzira kwevagadziri vavo. Aurora Basa Rekutanga: Application yeFoni Inofambiswa inotarisirwa kuva chigadzirwa chinokudzwa zvakanyanya, chinoremekedzwa uye chinodiwa, chinoshanda uye chine zvitsva. Dhizaini iyi yakahwina Mubairo weSimbi we A' Design Award inosanganisa maitiro akanakisa eindasitiri uye ine hunhu hwetekinoroji hwakakwana hwakabatana neruzivo nehunyanzvi. Nekushanda kwayo zvakanaka, Aurora Basa Rekutanga: Application yeFoni Inofambiswa inoremekedzwa uye yakagadzirirwa kupa kuvandudzwa kwehupenyu kunobatsira kuita kuti nyika ive nzvimbo iri nani.

Behind the Design

Press Kit

Credits, team members and technical details provided by the designer.

Unique Properties: Aurora is a music-based emotional support app for people with ADHD. Unlike traditional music platforms that recommend by genre or history, Aurora learns from users' mood journals to generate state-matched playlists, highlight moments when music helped, and reveal daily regulation patterns. Rhythm-synced visuals add gentle grounding during listening. The result is a low-effort tool that turns everyday songs into long-term self-regulation habits for wellbeing support.
Dimensions & Packaging: Platform: Mobile app (iOS and Android concept). Core inputs: current track metadata, time stamp, multi-mood intensity sliders, and short text notes. Core outputs: state-matched playlists, daily mood pattern summaries, highlighted key moments, and rhythm-synced visual modes. Data model links each journal entry to a song and a time period to support calendar navigation and trend views. Visual modes include multiple selectable patterns designed for low stimulation.
Tags: ADHD support, emotional regulation, mood based playlists, music journaling, rhythm synced visuals, neurodiverse wellness, focus support, calm support, behavior design
Production Technology: Aurora is designed as a mobile app concept with interactive prototypes. The experience is structured around three modules: playlist generation, mood journaling, and visual listening mode. Mood inputs are captured with multi-dimensional sliders and saved as time-stamped entries linked to the current music track. The app then summarizes entries into daily patterns and generates state-matched playlists based on past mood and listening records. Rhythm-synced visuals are designed to respond to BPM and audio energy.
Design Challenge: The main challenge was designing for mixed emotional states without adding cognitive load. Many mood trackers force a single label, while detailed journaling can feel too demanding for ADHD users. This was addressed by using a small set of mood dimensions with quick sliders, blended visual cues to reflect mixed states, and an insight-first trend view that highlights only the most meaningful moments instead of showing every entry.
Research: Research Objectives: Understand how people with ADHD use music to manage focus and mood.
Methodology: Semi structured interviews and concept testing. Data Collection and Research Tools Used: Interview notes, affinity mapping, storyboard and UI prototypes.
Participants: 10 adults with ADHD. Results: Music helps regulation but playlists rarely match the moment and helpful tracks are hard to recall.
Insights and Impacts: Led to mood logging, pattern highlights, and state matched playlists. Effect of the Research in Real-Life Phenomena such as Business, Society and Design Itself: Supports neurodiverse wellbeing and reframes music as intentional support.
Inspiration: After interviewing 10 people with ADHD, I saw a repeated pattern: music helps them focus or calm down, but generic playlists rarely match the moment, and it is hard to remember what worked later. This gap inspired Aurora. I combined mood journaling, pattern insights, and rhythm-synced visuals to turn listening into a repeatable self-regulation tool.
Project Duration: Aurora was designed over approximately 8 weeks in the United States (Boston, MA). Work included user interviews, synthesis, concept development, wireframing, and high-fidelity UI prototyping.
Operation Flow: Users listen to music, then log a quick moment: what they are listening to, how they feel across a few mood sliders, and a short note. Aurora turns these moments into state matched playlists and a daily trend view that highlights when music helped stabilize focus or calm. A visual mode adds gentle rhythm synced motion during listening to support grounding and reduce overstimulation.
Patents: Copyrights belong to Ping Chen, 2025.
Team Members: Ping Chen
Haowei Zang