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article imageChicago start-up aims to make wearable tech smarter

By James Walker     Jul 11, 2015 in Technology
A start-up company founded by a graduate student is aiming to overcome some of the major shortcomings of today's smart fitness tracking wearables by developing a new software platform. It will allow advanced differentiation between types of movements.
As Digital Trends reports, Rithmio was formed by a University of Illinois graduate and his Ph.D. advisor. The company wants to get its products integrated into devices from next year but has not yet released any apps.
Those apps will be powered by Rithmio's intelligent software platform. It is designed to track the gestures that you perform with your wearable devices, learn what you do most and then deliver more precise analytics based on that.
A common problem faced by users of fitness trackers is their inaccuracies regarding the activities that you actually perform. Identifying whether you are walking, running, cycling or climbing stairs can be hard to determine while differentiating between gym activities such as squats or bicep curls can be even more complex.
Rithmio's platform aims to solve this. Its complex algorithm notices subtle differences in your movements and posture, identifying what you are doing and remembering it for next time. It could make future wearables much smarter regarding fitness tracking, assuming manufacturers are happy to rely upon Rithmio's tracking platform.
According to the company's website, Rithmio "automatically learns" the properties of a new sensor device, making it easy for developers to get started with. The system then learns how the person moves, becoming progressively more accurate over time. The company raised $3 million in funding last month.
The platform is another example of how Internet of Things devices can work together to make each other more accurate. Rithmio could theoretically be used on today's fitness trackers with just a software upgrade, using its open software development kit (SDK).
Rithmio could be an important step towards more accurate fitness trackers for the future. By allowing for the more reliable identification of activities, users can be more confident that their wearable is telling them the truth.
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