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In 2013 and 2014, the first activity trackers, such as Fitbit Zip Clip and Withings Pulse, were introduced at CES and hit the market a few months later. It was the start of a lot of hype around collecting data about your daily level of activity. The accuracy of those early gadgets was not good compared to the devices we use today, but it was the first time that consumers could track their steps, measure their heart rate (HR), and get a hint of calories burned per day. With the first smartphone apps, people started to collect tons of data. Back in 2014, we were still far away from big data analytics, smart predictive healthcare, and augmented reality (AR). But everybody was happy to collect their activity data and share it with the devices’ manufacturers via their internet platforms.
After a while, though people got bored and stopped using these devices. What happened? Initially, people were happy to use the devices because they were something new and exciting. But after several months, the novelty wore off, and it was not exciting anymore since there was no real feedback from the devices or the connected apps. Plus the data accuracy of the first HR trackers was far from great.
At this point, the device manufacturers started to think about new features and complete ecosystems (being prompted by customers who were asking them for information and analytics from all that collected personal data). That was when IoT and big data hit the stage. The first algorithms analyzed your training routines and tried to guide you to better performance based on standard training plans. The user could choose between endurance, speed, or strength enhancement, and also just to lose weight.
"Smart devices offer useful guidance to users to help meet personal health and wellness goals"
Real personalization started with Artificial Intelligence (AI), also referred to as machine learning. Here the device, e.g., a smartwatch or professional sports watch (such as Apple Watch, Polar Vantage, Withings Steel HR Sport, Garmin Forerunner, Suunto Spartan, etc.), combined different sources of data (pulled from the ecosystem) and calculated either on the device itself, on the connected smartphone (edge computing), or directly from the cloud, providing your personal pattern of activity, sleep, and social behavior. If desired, the user could get personalized guidance for their favorite sport as well as outdoor activities. With features including ECG tracking, companies such as Apple and Withings entered the market of predicted medicine. In what way? For example, by tracking the ECG signal of a person, they could calculate and predict the possibility of atrial fibrillation. Armed with that crucial data, people became more inclined to change their behavior to help prevent critical health situations.
Sleep analysis also plays an essential role in the overall picture of one’s wellbeing. The market is crowded with a lot of different devices, from wristbands to special mattresses. Smartwatches play an important role by monitoring the data from all these various sources. They combine it and feed it into intelligent smartphone apps, which then provide a clear picture of activity and sleep patterns, and offer some useful guidance to users to help meet personal health and wellness goals.
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