Aurora Hana Nui: Polokalamu Kelepona Paʻalima by Ping Chen

Aurora Hana Nui: Polokalamu Kelepona Paʻalima by Ping Chen is a highly esteemed winner of the very prestigious 2026 A' Design Award Hao in Hoʻolālā Paʻalima, Polokalamu a me Lako Paʻalima Competition Category.

HaoPaʻalima· 2026

Aurora Hana Nui: Polokalamu Kelepona Paʻalima

by Ping Chen

Client: Aurora

A' Design Award Hao Winner in Paʻalima Category

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Verified Award Winner

Verify Award Status for #182515

View at A' Design Award Press Release Award Badge

Colors

The Design

About Aurora Hana Nui: Polokalamu Kelepona Paʻalima

ʻO Aurora he polokalamu kelepona paʻalima e kākoʻo ana i ka hoʻoponopono naʻau no ka poʻe me ka ADHD ma o ka launa pū ʻana e pili ana i ke mele. Hoʻohui ka ʻili launa i ka hana papa mele e kūlike ana me ke kūlana o ka naʻau, he puke moʻolelo naʻau lehulehu, a me nā hōʻuluʻulu pakuhi o kēlā me kēia lā. Hoʻopili kēlā me kēia komo puke moʻolelo i ke mele o kēia manawa me nā paheʻe ikaika naʻau a me kahi memo pōkole. Hōʻike ka ʻike ʻano i nā manawa nui i hoʻomaikaʻi ai ke mele i ka noʻonoʻo a i hōʻemi paha i ke koʻikoʻi. Hāʻawi kahi ʻano ʻike i hoʻopaʻa pū ʻia me ka mele i nā hōʻailona neʻe akahai i ka wā e hoʻolohe ai e kākoʻo i ka hoʻokūpaʻa ʻana.

The Recognition

About the A' Design Award Hao

Ua lanakila ka hoʻolālā Aurora Hana Nui: Polokalamu Kelepona Paʻalima i ka A' Design Award Hao, he ʻike hoʻohanohano i hāʻawi ʻia i nā hoʻolālā maikaʻi e hoʻokō ana i nā koi ʻoihana a ʻoihana hana i manaʻo ʻia mai nā mea hoʻokumu i hana maikaʻi ʻia. Hōʻike nā lanakila Hao i ka mākau nui, ka loea kiʻekiʻe, ka ʻike akamai a me ka hiki hana noʻeau o ka poʻe i hana iā lākou. Manaʻo ʻia ʻo ka hoʻolālā Aurora Hana Nui: Polokalamu Kelepona Paʻalima he mea hoʻokumu i mahalo nui ʻia, hoʻohiwahiwa ʻia a aloha ʻia e pono a hou. ʻO kēia hoʻolālā i lanakila i ka A' Design Award Hao e hoʻohui ana i nā hana maikaʻi loa o ka ʻoihana a loaʻa nā ʻano ʻenehana mākaukau i hui pū ʻia me ka ʻike hana a me ka makakoho. Mahalo ʻia ka hoʻolālā Aurora Hana Nui: Polokalamu Kelepona Paʻalima no kona nānā pono a ua hoʻolālā ʻia e hāʻawi i nā hoʻomaikaʻi ola e kōkua ai i ka hana ʻana i ka honua i wahi maikaʻi aku.

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