Science & Enterprise subscription

Follow us on Twitter

  • A new analysis from technology intelligence company GlobaData shows the top 2 enterprises attracting venture financ… https://t.co/Qfo4gwNJqW
    about 1 hour ago
  • New post on Science and Enterprise: Infographic – Germany Beats U.K. in Top Venture Funds https://t.co/IdlaDYgl9U #Science #Business
    about 1 hour ago
  • New contributed post on Science and Enterprise: https://t.co/sGLg6tmIwx Protecting A Modern Agricultural Business
    about 3 hours ago
  • An engineering-psychology team is developing a system connecting virtual reality with brain signals in real time t… https://t.co/mF081BrzQX
    about 1 day ago
  • New post on Science and Enterprise: Virtual Reality Coupled with EEG for Autism https://t.co/PTQ1ZTFwia #Science #Business
    about 1 day ago

Please share Science & Enterprise

RSS
Follow by Email
Facebook
Facebook
Google+
Twitter
Visit Us
LinkedIn
INSTAGRAM

Algorithm Improves Activity Tracking for Wellness Apps

iPhone (William Hook/Flickr)

(William Hook/Flickr)

Engineers and physiologists at Northwestern University in Chicago developed an algorithm to improve the way health and wellness apps on smartphones track a user’s physical movements. Professor of physical medicine and rehabilitation Konrad Kording, with colleagues Stephen Antos and Mark Albert, published an advance version of their findings online in the Journal of Neuroscience Methods (paid subscription required).

The Northwestern team tackled the problem of smartphone users carrying their phones in different places during the day, depending on their activity at the time or what they’re wearing. The place owners carry their phones, however, can have an impact on the effectiveness of health and wellness apps that use built-in accelerometers for tracking physical activity.

In the study, researchers asked 12 healthy volunteers to carry a smartphone with an activity tracking app in designated carrying places (e.g. belt, pocket, or purse), while engaging in specified activities. The data were collected to highlight the variations in recording physical activity among those conditions second-by-second during the day. That same method was used to record physical activity with smartphones by two people with Parkinson’s disease.

The data collected were used to construct an algorithm to adjust the apps readings for different phone-carrying locations and activities. That algorithm is based on a hidden Markov model, a statistical technique used to associate heterogeneous sources with specified outcomes; “hidden” in this case refers to the changes from one state or condition to another, not the properties of the model.

Tests of the algorithm show it can help improve activity tracking of healthy individuals, and has the potential at least, to be used with Parkinson’s patients. “I believe we will have apps running on smart phones,” says Kording in a university statement, “that will know exactly what we’re doing activity-wise and will warn us of diseases before we even know that we have those diseases.”

Read more:

*     *     *

Please share Science & Enterprise ...

Comments are closed.