If fitness trackers like Fitbit have proven anything, it’s that people really like to have quantitative data about their daily habits. As the name suggests, they originally gained popularity among people who were concerned about their level of personal fitness. Over the years, however, that has expanded to include people interested in other kinds of information, such as how much sleep they’re getting or their cardiovascular health. But fitness trackers generally only have two sensors: a heart rate monitor and an accelerometer. MIT’s new PAL (Personalized Active Learner) has a whole host sensors to collect data and help improve your life.
Personalized Active Learner was developed by researchers at the MIT Media Lab. It’s a wearable device that uses machine learning and a variety of sensors to perform many functions that could help people improve their lives. Those sensors include a camera, microphone, sweat sensors, ECG (electrocardiogram) sensors, and EEG ( electroencephalography) sensors. Those allow PAL to collect a huge amount of data about what the user is experiencing, and computer vision and machine learning are used to make sense of it. That, in turn, can be utilized by the user to better understand their habits and improve how they interact with the world.
At the most basic level, PAL can do the same things as traditional fitness trackers — though the additional sensors provide far more accurate and comprehensive data. With its computer vision abilities, PAL can also recognize the world around you. It can, for instance, tell what you’re eating. It can also help you with language learning by seeing what you’re interacting with and then telling you its name in the language you choose. Perhaps the most interesting application, however, is in memory augmentation. PAL can recognize the people you interact with, and remind you who they are. That could significantly improve the quality of life for people living with memory loss, such as sufferers of Alzheimer’s.