Everyone today seems to have a fitness tracker or smart watch. From wrist-wear to foot-wear and smart clothes, wearables are the largest segment of the consumer Internet of Things (IoT) market. These devices are not only increasingly popular; they are also getting smarter.
In the future, features such as counting steps and tracking sleep will go beyond simple data recording to include actionable guidance and true artificial intelligence (AI), marking the next revolution in wearables. And we’re not just talking about fitness trackers – from smart jewelry to smart implants, the possibilities for wearables 2.0 are almost limitless.
But moving from simple data recording to actionable guidance and AI requires us to move up the IoT chain. In addition, we must improve data transfer functionality and technical interoperability. Without resolving these issues, the next generation of wearables will hit a wall.
Moving On Up
How do we move up the chain?
While there are five levels of AI, getting from one layer to the next takes great effort. The first layer is notification, which is easily understood. The next layer is perception, which is very systematic and where tasks are well-defined. Think of these two layers as “You’ve reached 10,000 steps” on your fitness tracker. We’ve already mastered these two levels.
To get to Wearables 2.0, we need to reach the cognition and prediction stages. In order to get here, we need to further develop our deep-learning and big data capabilities. The cognitive and prediction stages involve the analysis of big data from multiple data sources that drive intelligence and insights that ultimately drive action. For example, multiple devices in your home and wearables – bed sensors, smart clothing, smart watch, which are gathering your health data – might be able to identify and alert you of any irregularities in your body. Furthermore, this stage involves self-learning of the device itself, where it can efficiently learn and share knowledge from multiple user tasks and adapt to a variety of situations.
The Data Interoperability Issue
Solving interoperability issues are key to a Wearables 2.0 future affecting knowledge and service levels of IoT. If your smart devices and even whole IoT ecosystems cannot share data with one another, how can we enable deep learning?
Today, data transferability and data continuity between devices are difficult. It isn’t easy to transfer your history and data from generation to generation of devices such as fitness trackers, and even more difficult is to transfer your data from one manufacturer’s device to another’s. For example, Apple, Google and Microsoft all make smart wearables. But their proprietary technologies and ecosystems make it really difficult to transfer user data from one IoT system to another.
At the service level, wouldn’t it be nice to be able to enable all your wearables to exchange information so they can collectively provide you with meaningful and actionable information including notifications and recommendations? Solving this issue means having compatible data formats that can be shared securely and confidently between IoT systems and devices.
The Technical Interoperability Issue
Solving the technology interoperability issue goes a step beyond making sure that devices can communicate with each other. Standards and rules on data and privacy protection, and how data is used and controlled by device manufacturers needs to be addressed. Consumers need to be in control of their user data, where it goes, where it’s stored, and how it’s used. Setting industry-wide standards on interoperability requires all stakeholders – from consumers, government and enterprises – to come together to address these issues.
The ideal system would be able to provide interoperability at all levels. Aside from data and technical levels discussed, standards should be in place on issues such as Human Machine Interaction (HMI), knowledge and services. But for now, the most pressing concern for the wearables sector is to remedy data transfer functionality and technical interoperability issues. Innovating for the next generation of devices requires that we tackle those now.
And when we can advance in AI and interoperability, Wearables 2.0 may beat our wildest expectations.
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