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Yanbing Cui, Sarah Kramer, Michael Tracey, Amanda Urick , Eric Wong. Department of Biomedical Engineering, Columbia University, New York, NY. Design Motivation. Prototype. Testing. Location Tracking, Environmental, and Physiological Parameters Particulate Collector

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Yanbing Cui, Sarah Kramer, Michael Tracey, Amanda Urick , Eric Wong

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Yanbing cui sarah kramer michael tracey amanda urick eric wong

Yanbing Cui, Sarah Kramer, Michael Tracey, Amanda Urick, Eric Wong

Department of Biomedical Engineering, Columbia University, New York, NY

Design Motivation



  • Location Tracking, Environmental, and Physiological Parameters

  • Particulate Collector

  • User Interface and Generated Report

Relevance of Environmental and Physiological Parameters

Airborne Particulate Collection

  • Asthma, a chronic inflammatory disease of the airways, affects over 300 million people worldwide. It is the #1 chronic disease in children, the most vulnerable population.

  • Asthma is most prevalent in low income, urban, and industrial areas.

  • Risk factors for asthma include: genetic predisposition, allergies, and environmental particles such as dust and pollen.

  • Avoiding asthma triggers is an effective, non-medicated method for managing asthma symptoms.

  • Few specialized asthma clinics send technicians for chemical swabs of households to identify possible triggers.

  • asthmaID’s device identifies triggers for children 3-10, who may be unable to describe their triggers accurately, and too small for traditional skin testing. Our device will (A) reduce medical costs, (B) expand the range of tracking to a child’s day, and (C) pinpoint specific location, chemical, and environmental cues.

Figure 2: Location, ambient temperature, ambient humidity, and body temperature are all automatically logged to flash memory through a portable microcontroller. All parameters can then be correlated via timestamp and uploaded to a report which is generated for each user. Images are also taken via a time-lapse, and will be used as waypoints to which time correlated data can be added. Figure 2 shows the GPS, sensors, and flash memory interfaced with an ArduinoDuemalonove. The entire unit has a very small form factor.

Figure 5: Data was exported to a .csv file and manipulated to display raw humidity, environmental and body temperature. This data is also correlated with the map waypoints for user viewing. One can see the body temperature reducing as the cold air interacts slightly with the thermistor. Environmental temperature and humidity increases midway, while walking past John Jay cafeteria.

Design Specifications

  • Portability: Compact, Lightweight, Wearable, Long battery life (school day), Durable, Flash storage, disposable batteries

  • Environmental Tracking: GPS location, ambient temperature, environmental humidity, image geotagging.

  • Biochemical Tracking: Body Temperature, time-sensitive filtering of air sample particulates.

  • Affordability & Accessibility: Not limited to specialized asthma centers, purchasable, user-friendly, long-term data collection.

Figure 3: The particulate collector uses a high power fan to draw air through the 30 micron nylon mesh filter. The cartridge is actuated by the servo motor at programmed time intervals to allow air to flow through each individual wedge. A total of eight samples can be taken over the course of the day, and the fan runs for 10 minutes at each designated sampling time.

Figure 1: Our current prototype encompasses all of these design specifications. Electronic components and a particulate filter reside in the outermost pocket. Discreet external sensors collect environmental parameters and track location – this data is stored in removable and easily accessible memory.

Pollen was collected for ten minutes, filters removed, and analyzed. The image at left is representative of isolated pollen, while the image at right shows the pollen that has collected on the surface of our nylon mesh filter. Images taken at 10x.

Future Plans

  • Incorporate c328 camera which will record in timelapse and automatically geotag images on flash memory.

  • Fully automate correlation of all parameters, and create a google maps *.kml file from this data. This would allow us to provide a url directly to the report for each user

  • Add a breathing monitor .

  • Use a softer polymer to reduce brittle fractures.

  • Upload to Arduino directly from GUI, further automating the device and reducing user input.


Figure 4: A GUI is used to quickly program the microcontroller which actuates the particulate collector. (a) Sampling times can be entered at a standard interval – ie. class times – or individually. (b) The user report can also be accessed via this GUI. (c) All parameters are added to photo EXIF data, and displayed via Google Maps. Future iterations will include a search engine which will allow a user to search for specific triggers, locations, or stress responses.

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