How can we solve this challenge? In my project, I want to show an example of how you can significantly increase the complexity of intelligence functionality, and determine what a wearable device can recognize with a very small and accurate neural network. The majority of wearable devices should operate in the always-on mode, which places serious demands on the energy efficiency of such solutions. This would obviously quickly drain the battery of the IoT device where this neural network would be embedded. However, recognition of more complex activities requires large and complex neural networks which require a great amount of computation. What if they could recognize more specific, similar, and complex activities? It would open up the never-before-seen possibility of creating a great many new interesting applications and devices. It seems to me that we are ready for much more complex analytics from our devices. They determine whether one is running or sleeping, they count the steps, and they determine if one falls. Prototyping: a Hand Tracker with Nicla Sense ME and Bosch sensorsīosch BHI260 sensor Software apps and online servicesĭespite the incredible variety of wearable devices today, most of the AI features come down to merely defining very simple actions.
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