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Supporting Information

Lab-on-Mask for Remote Respiratory Monitoring

Liang Pan‡, Cong Wang‡, Haoran Jin‡, Jie Li, Le Yang, Yuanjin Zheng, Yonggang Wen, Ban Hock Tan, Xian Jun Loh* and Xiaodong Chen*

Supplementary Methods

Fabrication of the embedded sensor

1) PDMS Fabrication: To embed the sensor with soft PDMS, liquid PDMS with the PMDS curing

agent with a ratio of 10:1 were mixed. The mixture was then stirred until it was homogeneous by

using a centrifugal mixer. The mixture was next placed inside a vacuum chamber to eliminate air

bubbles.

2) Embedding sensor: The sensors for HR, BP, SpO2 and T were placed in a mould. A sliver paper

was added on the LED to avoid the influence of PDMS. Next, the degassed PDMS was infused

into the mould, followed by a heat treatment at 60 °C for 12 hours. The embedded sensors were

wired on to the mask with internal MCU and wireless Bluetooth circuit.

Vital signs collected and comparison

For SpO2, a pulse oximeter from ANDON HEALTH Co., LTD. was used for comparison measurements.

We collected the SpO2 from the artery of the index finger of a healthy person under resting state. We

recorded the data every 2 minutes over a period of 10 to 15 minutes. For HR and BP, an electrical

blood pressure monitoring from Omron was used for comparison measurements. Here, we collected

the HR and BP from the artery in the wrist. Similarly, we recorded the relative data at the same time.

To calibrate the temperature of LOM, we measured the forehead temperature as body temperature by

an infrared thermometer. There is a compensation value of ~2.9 °C between the LOM and body

temperature from Figure S3.

Design of circuit

The non-contact LOM mainly integrates three modules: signal-receiving, data processing, and

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Bluetooth modules. The signal-receiving module utilizes near-infrared (880nm) and red light (660nm)

to monitor the optical absorptions of arterial blood; uses green light (550nm) to record pulse waves, and

employs a highly sensitive thermometer to measure the human temperature. These received signals

are sent to an MCU (STM32F103C8T6) via IIC, UART, and unique one-wire communication protocols,

respectively. MCU is responsible for calibrating and analyzing the raw data to extract the oxygen

saturation, blood pressure, heart rate, and temperature information, and then pushing the vital signs into

the Bluetooth module. The wireless Bluetooth (HC-05) broadcasts these vital signs to various

terminals such as mobile phones and PCs. During measuring, MCU not only reads the data sent from

the sensors but also monitors the status of mask-wearing. Once the mask has been re-worn, MCU will

automatically recalibrates the optical properties of tissue and blood to ensure the accuracy of

measurements. Also, it guarantees the LOM can adapt to different individuals and be repeatedly used.

Vital signs visualization and analysis

Aside from the embedded sensors, we also have a mobile application for data visualization and analytics.

The communication protocol between the LOM and smart phone is Bluetooth, which is released in 2004

and widely used in IoT area. For the detailed implementation of our mobile app, firstly we developed

Bluetooth related functions including search, connect and disconnect functions. To better display the

data, on the one hand we display both the values in numbers with certain precisions, and a round

progress bar to intuitively show the status of the measured parameters.

In terms of the data analytics, features such as the warning of abnormal heart rate and blood pressure

would allow users to be notified directly via their phone’s built-in notification. Additionally, these

measured data can be sent to our centralized database of the remote monitoring system for further

analysis by getting machine learning algorithms involved. With a large number of samples, it would

be possible to predict the healthy status of the patients and track their progress. Hence, the remote

monitoring system will benefit the overall progress of defending against respiratory pandemics such as

the COVID-19.

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Figure S1. (a) Details of sensor parts and data processing part of the LOM. For experiments, we used a 9V rechargeable Li-battery with 880 mAh which make the LOM work more than 80 hours. (b) Real-time monitoring of HR, BP, SpO2, and T, through an app of a mobile phone.

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Figure S2. Coefficient of data dispersion, c, of HR (a), SpO2 (b), T (c) and PB (d) based on LOM and different commercial products.

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Figure S3. The temperatures of the interior of the LOM and the forehead. There is a compensation temperature of 2.9 °C between them.

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Figure S4. The HR of 10 healthy peoples based on the LOM and the pulse oximeter (PO).

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Figure S5. The SpO2 of 10 healthy peoples based on the LOM and PO.

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Figure S6. The temperatures of 10 healthy persons based on the interior temperature of the LOM and forehead temperature measured by IR-T.

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Figure S7. The SYS of 10 healthy persons based on the LOM and Omron.

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Figure S8. The DIA of 10 healthy persons based on the LOM and Omron.