Yiling LiuによるDoplor Sleep 睡眠モニタリング・モバイルアプリケーションは、モバイル技術・アプリケーション・ソフトウェアデザインコンペティションカテゴリーにおける非常に権威ある2021 A' Design Award 鉄賞の優れた受賞作品です。
鉄モバイル· 2021
Doplor Sleep 睡眠モニタリング・モバイルアプリケーション
デザイナー： Yiling Liu
クライアント: Reinier de Graaf Gasthuis
モバイルカテゴリーのA' Design Award 鉄賞受賞者
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Doplor Sleep 睡眠モニタリング・モバイルアプリケーション について
Doplor Sleepは、病院での使用を想定した睡眠モニタリングアプリケーションです。本アプリはスマートフォンのマイクを用いて環境音を録音します。次に、アルゴリズムが各音を医療機器のアラーム音、話し声、偶発的な物音、いびきなどのカテゴリーに分類します。睡眠データは接続された睡眠トラッカーから収集され、音のデータと組み合わせて日次レポートにまとめられます。シンプルなイラストレーションによって、音のレベルと睡眠段階が一つのタイムライン上に表示されます。その狙いは、病院における睡眠の質の低下を招く要因としての騒音について、患者と医療スタッフの意識を高めることにあります。
受賞
A' Design Award 鉄賞 について
Doplor Sleep 睡眠モニタリング・モバイルアプリケーションは、よく設計された創造物に期待される専門的・産業的要件を満たす優れたデザインに授与される、権威ある評価である A' Design Award 鉄賞の受賞作品です。鉄賞の受賞作品は、制作者の高い技量、高度な専門性、専門家としての理解、創造的な能力を示しています。Doplor Sleep 睡眠モニタリング・モバイルアプリケーションは、高く評価され、称賛され、愛される、実用的で革新的な創造物であることが期待されます。この A' Design Award 鉄賞を受賞したデザインは、業界のベストプラクティスを統合し、ノウハウと才能に結びついた有能な技術的特性を備えています。Doplor Sleep 睡眠モニタリング・モバイルアプリケーションは、その徹底ぶりで尊敬され、世界をより良い場所にするための生活の質の向上をもたらすよう設計されています。
デザインの裏側
プレスキット
デザイナーが提供するクレジット、チームメンバー、技術的な詳細。
- 独自の特性
- Doplor Sleep is a bedtime monitoring application that provides insights into sound disturbances in the hospital. It captures the environmental sounds using the microphone of the smartphone, and the implemented algorithms directed classify the data recorded into different sound source categories, such as medical alarms, speech, incidental sounds, and snoring sounds. Doplor sleep is connected with the Fitbit sleep tracker and retrieves data from Fitbit API to form daily sleep-sound quality reports. As for the UI, we used friendly-looking illustrations to present the data and information, and a creative way of interaction to let users view the dynamic of their sleep status and the acoustical environment throughout the night.
- 寸法とパッケージ
- The mobile application currently operates on Android devices, such as Android phones and devices.
- タグ
- Soundscape, Sleep, Sleep hygiene, Sleep quality, Hospital, sleep deprivation.
- 製造技術
- The whole system is built with the Android Studio IDE and used the Fitbit API for retrieving sleep data.
- デザインの課題
- The first challenge is how to design a sound classification algorithm that could accurately classify the sounds of the hospital. The second one is to investigate how can we present the sound data and sleep data on the same timeline in a user-friendly way.
- リサーチ
- Recently in the Netherlands, researchers have found
that sleep duration and quality were suboptimal in the
hospital. Evidence proved that many modifiable hospital-related
factors negatively associated with patients' sleep
(JAMA Internal Medicine, 2018). The sound factor is
the most significant sleep disturbance in the hospital.
In this graduation project, collaborating with Reiner de Graaf
hospital and Critical Alarms lab, an information exchange
the system was proposed to raise awareness of sound as sleep
disturbance. The system captures the sound-producing
events and visualizes them with visually attractive graphics.
In this system, we use the smartphone as the sound captor.
The recorded sounds are processed locally on the phone
and converted into information such as sound level and the
category it belongs to (alarm, speech, incidental sounds, or
snore). Fitbit is implemented in the system to collect sleep
information. To both patients and medical staff, The Doplor
sleep system presents the influence of sound on sleep in
a friendly and comprehensive way. During this project, a
functioning prototype was developed. We have tested its
functionality and user experience with the potential users. - インスピレーション
- Sleep deprivation caused by environmental factors can be fatal to critically ill patients. For example, various noises caused by false medical alarms, activities of hospital staff, conversations, etc. can cause delirium in patients with brain diseases. When it happens, patients are in a state of mental confusion and emotional disruption, which prolongs their hospital stay and, like a vicious circle, disturbs the rest of the other patients. A first-step solution can be creating awareness.
- プロジェクト期間
- The project started in March 2020 and ends in October 2020 in Delft, the Netherlands.
- オペレーションフロー
- Before going to bed, the user presses the ship-icon button to start recording the environmental sounds. The next morning, the user presses the ship-icon again to stop recording. Then a sleep summary page occurs with information displayed such as sleep quality and sound quality. By Swiping to the left, the next screen occurs and the user sees more details about sees the presence of sounds (loudness levels and classifications) and sleep stages (wake, Light sleep, REM, and Deep sleep) during the whole by moving the boat forward along the river. Here we used boats as a time indicator and a representation of the sleeper, and the river indicating the timeline. The color blocks on the river represent the sleep stages. The icons of picnic, flashlight, rocks, and nose bubble indicate different sound classifications (speech, medical alarms, incidental sounds, and snores). Finally, by swiping to the left again, the user could fill in a short survey on their subjective feelings about sleep quality. The data will then be sent to the medical staff.
- チームメンバー
- Yiling Liu
Elif Özcan
Jered Vroon
Daan Kamphuis - 画像クレジット
- Image #1: Illustrator Yiling Liu, Doplor Sleep Mockup, 2020.
Image #2: Illustrator Yiling Liu, Doplor Sleep Phone Mockup, 2020.
Image # 3: Illustrator Yiling Liu, Doplor Sleep Tablet Mockup, 2020.
Image #4: Creator Yiling Liu, Doplor Sleep Phone Mockup, 2020.
Image #5: Creator Yiling Liu, Doplor Sleep Phone Mockup, 2020.
Video Credits: Creator Yiling Liu, Doplor Sleep Showcase, 2020
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