TIIC 2016 North America: Non-Contact Temperature Sensor for the iPhone

University: Michigan State University
Team Members: Ian Bacus, Yujie Hao
TI Parts Used:

  • MSP430F1611
  • TMP007
  • TPS79733 LDO

Project Description

This project integrates a clean, low-power, light based temperature sensor with a smartphone application. The external temperature collection device will be referred to as THERM. This device has a high potential for marketability in both consumer and hospital settings. Indeed, one of the most common symptoms of disease or infection is an abnormal body temperature. This can be detected using a temperature sensor of some kind. The most ubiquitous tool for clinical temperature measurement for the past half century has been the contact-based digital thermometer, dating back to circa 1952. The procedure involved for safely using this type of temperature sensor presents notable weaknesses in clinical settings. It requires sanitation between uses, and in some cases a wasteful disposable cap is utilized. The information collected from this type of sensor must be copied manually into a record. The invasive nature of some temperature measuring procedures can create discomfort for patients as well. THERM eases the temperature measuring process by directly addressing each of the previously mentioned weaknesses of contact-based digital thermometers: it does not require sanitation or disposable components, it can automatically communicate with digital record-keeping systems, and it minimizes patient discomfort. It also is marketable in a commercial setting, because it is easy to share the physical device with others almost as easily as it is to share the information with others. Personal health devices are of growing interest nowadays, as is evident from the success of consumer items such as Fitbit or the Apple Watch. Data from these electronic devices can be collected passively from a smartphone and shared with medical institutions and hospitals. If the use of such devices became more widespread, given their ease of use, data brokers could make use of and sell the information to hospitals to help in determining “illness forecasts” by observing the distribution of abnormal temperatures in different regions. Upon encountering a medical condition, a patient’s health information is only a few clicks away, which helps clinicians and doctors to make better treatment decisions by accessing continuous data throughout the day, week, month, or even years.










 https:// github.com/ianbacus/IRtemperature.git