A new article on EDN Network by Debbie Meduna, Dev Rajnarayan, and Jim Steele of Sensor Platforms, Inc. discusses how to make mobile platforms context-aware. The team writes, “Using the sensors available on these platforms, one can infer the context of the device, its user, and its environment.  This can enable new useful applications that make smart devices smarter.  It is common to utilize machine learning to determine the underlying meaning in the large amount of sensor data being generated all around us. However, traditional machine learning methods (such as neural networks, polynomial regression, and support vector machines) do not directly lend themselves to application in a power-conscious mobile environment.  The necessary techniques for ensuring effective implementation of sensor context are discussed.”

The article continues, “Context is the inference of user or environment from sensor measurements on mobile devices. Sensors are in all smart devices such as phones and tablets, as well as many wearable devices such as activity wrist bands, shoes, headphones, and even glasses. Interpreting this sensor data is getting more sophisticated as well.  For example: an accelerometer can detect that a user is biking; a front-facing camera can detect whether a user is skeptical of what he is reading; GPS and Wi-Fi positioning can detect which store a user entered; audio can detect whether a user is in a meeting or a night club; and blood pressure sensors can detect whether a person is relaxed or agitated.”

Read more here.

Image: Courtesy Flickr/ thetimchannel