由設計師 Yiling Liu 創作嘅 Doplor Sleep 睡眠監測手機應用程式係備受推崇嘅 2021 A' Design Award 鐵獎得獎作品，獲獎類別為流動設計。

鐵流動· 2021

Doplor Sleep 睡眠監測手機應用程式

由 Yiling Liu

客戶: Reinier de Graaf Gasthuis

流動類別嘅 A' Design Award 鐵獎得獎者

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經驗證嘅獲獎者

驗證設計 #114626 嘅獲獎狀態

訪問網站喺 A' Design Award 查看新聞稿獎項徽章

色彩

設計

關於 Doplor Sleep 睡眠監測手機應用程式

Doplor Sleep係一款專為醫院使用而設計嘅睡眠監測應用程式。呢個應用程式透過智能手機嘅麥克風收錄環境聲音，然後利用演算法將每種聲音分類，例如醫療警報聲、說話聲、偶發聲響同鼻鼾聲等。睡眠數據會由連接嘅睡眠追蹤器收集，再同聲音數據結合，整合成每日報告。簡潔嘅圖像化設計將聲音水平同睡眠階段一併顯示喺同一條時間軸上，一目了然。呢個設計嘅目的係要令病人同醫護人員意識到噪音係導致醫院內睡眠質素欠佳嘅其中一個主要原因。

榮譽認可

關於A' Design Award 鐵獎

Doplor Sleep 睡眠監測手機應用程式是 A' Design Award 鐵獎得主，這是授予滿足人們對設計精良的創作所期待的專業與工業要求的優良設計的聲望認可。鐵獎得主展現出其創作者的高超技藝、高度專業化、專家級的理解力和創造能力。預計Doplor Sleep 睡眠監測手機應用程式將成為一件備受推崇、令人欽佩且被珍愛的創作，既實用又創新。這件贏得 A' Design Award 鐵獎的設計整合了行業最佳實踐，並擁有與專業知識和才華相結合的稱職技術特性。Doplor Sleep 睡眠監測手機應用程式因其嚴謹而受到尊重，旨在提供有助於讓世界變得更美好的生活品質改善。

設計背後

新聞資料包

由設計師提供嘅製作人員名單、團隊成員同技術詳情。

獨特屬性: 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.
尺寸同包裝: The mobile application currently operates on Android devices, such as Android phones and devices.
標籤: Soundscape, Sleep, Sleep hygiene, Sleep quality, Hospital, sleep deprivation.
生產技術: The whole system is built with the Android Studio IDE and used the Fitbit API for retrieving sleep data.
設計挑戰: 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.
研究: 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.
靈感: 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.
項目時長: The project started in March 2020 and ends in October 2020 in Delft, the Netherlands.
操作流程: 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.
團隊成員: Yiling Liu
Elif Özcan
‪Jered Vroon
Daan Kamphuis
圖片來源: 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