In this article, I examine the current trends in wearable computing in healthcare. Also, I explore the gaps between what can be done with current hardware offerings and their analytic capabilities. You'll learn how cognitive computing platforms like Watson can accelerate time to market for wearable device makers and also how Watson can fill the gap between the potential of wearables and their current rather weak offerings.
One of the hottest trends among hardware developers is the development of small wearable sensors, orwearables, specifically for collecting health and lifestyle data. This trend includes everything from simple devices such as the Fitbit to more sophisticated Lab-on-a-Chip devices that measure everything from blood sugar and hormone levels to complex proteins.
Unfortunately, most of these devices generate data that is underutilized. Either the user cannot derive anything but simple metrics from the devices, such as step count, or the data to is just not accessible to users. This underutilization often occurs because many hardware developers cannot afford to develop either the big data capabilities that are needed to manage all that data or the analytic capabilities that are needed to derive useful information from the wearables.
Services like the IBM Watson API, however, provide developers with the ability to offer valuable information to their users who use wearables, without having to build their own PaaS offerings. With the help of Watson, developers can create solutions that combine and compare data, find patterns and look for trends in that data, and even learn about the patients who are using the wearables.
For this article, I define wearables as a device with central processing capability and sensors that are designed to provide services to the user with the least amount of user interaction as possible for a specific task or need. So, while a smartphone can be worn on your arm as a wearable fitness sensor, it's not designed to do that. So it requires a lot of interaction to do tasks such as downloading an application, enabling the application, and then strapping the setup to your arm in an often cumbersome manner. As such, a smartphone can be worn, but it is not a wearable. A good example is something like a Fitbit device, which is designed to help a user track their steps or activity with the idea of promoting healthy behaviors. Keeping this definition in mind, wearables offer a plethora of potential capabilities within healthcare if the information and data created by these devices can be turned into actionable intelligence and insights.