A new type of wearable technology may one day be able to monitor a person’s eating, drinking, coughing, and even social habits. This information could give health care providers useful information when treating obesity, diabetes, asthma—and even depression.
Unlike existing technology, this next generation device, called “HeadScan,” is radio-based, which means it’s less intrusive, better able to protect a person’s privacy, and more comfortable to wear.
“HeadScan uses wireless radio signals to sense the targeted activities and provides a nonintrusive and privacy-preserving solution that overcomes the drawbacks of current wearable technologies,” says Mi Zhang, assistant professor of electrical and computer engineering at Michigan State University.
The way it works, Zhang says, is radio waves from two small antennas, which can be placed on the shoulders, are bounced off the patient’s head, capturing movements of the mouth and head caused by eating, drinking, coughing, and speaking. The information is then relayed to a health care professional who can analyze the data.
“For example, it can monitor how often a person eats,” Zhang says. “Dietary monitoring is important. However, humans are not good at tracking these sorts of things. Fortunately computers are.”
It also provides much more accurate information. “In some cases, the patient may not want to reveal how much he or she has eaten,” Zhang says. “This will provide objective information on a continuous basis.”
The device can also monitor how much a person talks. This may seem relatively unimportant, but measuring how much a person talks and engages with others is an indicator of one’s mental health, especially depression.
“Existing technology often uses cameras and microphones to measure this, which can track your voice as well as others around you,” Zhang says. “This offers a lot more privacy.”
Testing continues and researchers hope to have the device available for practical use within the next couple of years. They recently presented the work at the 2016 ACM/IEEE International Conference on Information Processing in Sensor Networks.
Source: Michigan State University
