So there I was last week, thinking about how to best summarize my experience in ENP 162 in a Blog while I was getting my kids ready for school one morning. My phone made a buzz for an incoming text message, but when I looked it was actually a notification from my Maps app.
The message said, “7 min to High St, Take Boston Ave, Traffic is light”. This was very peculiar to me, partially because it was the first time Maps had ever sent me a message, unprovoked. But the oddest part, is that I was literally about to drive my kids to their school on High Street, taking my usual route along Boston Ave.
As a Human Factors Engineer this strange message obviously excited me! Questions immediately swirled in my head.
Why did I receive the message? How did Maps know where I was going to drive before I even left? Was it a coincidence that I was about to drive to High Street when I received directions and suggestions from Maps to get to High Street, or just perfect timing?
Luckily, ENP 162 armed me with plenty of tools and knowledge to help answer these questions. This Blog will explore many of the Human-Machine System Design topics we learned this semester to explain the origins of my Apple Maps message.
GIS and GPS
Apple Maps is a Geographic Information System (GIS) which exploits the features and capabilities of our Global Positioning System (GPS). GPS is a system of satellites orbiting the Earth which instantaneously “feed” a receiver in your phone, computer, car your exact position, time, and velocity. A GIS such as Apple Maps simply utilizes GPS data in order to calculate directions and display positions and routes on a map or satellite imagery. So, Apple Maps uses GPS to find and display my position … but this is only part of the picture.
Big Data & Machine Learning
Working behind the scenes for Apple, often not visible to every day users, are processes known as Big Data and Machine Learning. These processes analyze extremely large sets of data in order to reveal patterns, trends, and associations. It is very likely that Apple Maps has been saving and storing my driving routes around Boston. A Machine Learning process analyzes this data, and in short order, can “learn” that I drive to High Street every morning at 7:30am to bring my kids to school.
Signal Detection Theory
Machine Learning and Big Data are advanced forms of Signal Detection Theory. Detection Theory is a means to measure the ability to differentiate between actual patterns (information-bearing) and random patterns (noise). Once again, Apple Maps uses very sophisticated technology/algorithms to predict my future driving activity by separating the random noise from the patterns in my driving history.
Automation and Alerts
Now that Apple Maps has “learned” my driving history and where/when I go, it appears the application’s Automation was utilized to deliver a sleek notification Alert to my phone. Of all the places I go, my daily trip to my kid’s school each morning at 7:30am is probably the most defined pattern, and Maps clearly identified this. So, I received my Alert and in a sense, should be better off now.
Internet of Things
This entire process of utilizing signals and sensors to analyze my driving data, and automation to deliver an alert to my phone, is a rather beautiful example of the Internet of Things (IoT). IoT is a system of interrelated computer devices with the ability to transfer and exchange data over a network without human-to-human or human-to-machine interaction. While it may seem mysterious to people who do not understand the foundation of Human-Machine System Design, IoT is happening constantly behind the scenes everyday and everywhere. We will rely on IoT capability more and more in the future as our technology expands.
I hope this blog helps shine some light on the many factors involved in complex Human Machine System Design. In the case of my “mysterious” Apple Maps message, we can peel back the onion and explore many of these concepts which are actively employed by many tech and communication companies now.
I thoroughly enjoyed ENP 162 and I would recommend the course to anyone who wishes to expand their horizons when it comes to understanding Human-Machine design topics. Thanks for reading.