Imagimob AI is a system for Edge AI applications that enables small devices with intelligence and data processing capabilities, to empower you and your customers with actionable insights—in real-time.
A radical approach to Edge AI
When the Internet of Things and Edge AI come together, the possibilities are tremendously exciting. Imagine a product that not only has the capacity to collect data, make informed decisions, learn, and improve—but to do all of these things independently of network connections. The result can bring added safety, convenience and efficiency to the user experience, and even save lives.
However, for many, the reality of incorporating such technology into a single device is too costly and impractical. Even some of the most promising machine learning models, widely applied in the cloud and digital industries, are still using too much computing power and memory to be able to fit the small microprocessors on the market today. And the alternative of engineering customized systems is often far too expensive to bring to fruition.
At Imagimob, we’ve figured out a way to create AI systems that can run efficiently on small devices while still delivering the performance and accuracy typically only found in bigger systems. Combining inspiration from the machine learning field with our own innovations, each of our algorithms is designed with small MCUs, low CPU usage, and low memory consumption in mind.
The Imagimob effect
Imagimob AI software offers high accuracy in real-time, on low-cost hardware with low power consumption. This lends itself to a world of exciting applications, where the AI model is pre-trained with data to create specific instincts for use in all kinds of intelligent products—from fall detection clothing to predictive maintenance tools. The pre-trained application collects data with its sensors, analyzes it for relevant patterns, then sends a signal only when action is needed.
Actionable insights in real-time
With zero to low latency between sensing, decision, and action, Imagimob AI allows for real time operations, including making critical decisions where milliseconds matter.
Imagimob AI makes devices autonomous, meaning they can think for themselves and don’t need to connect to the internet or a central processing hub in order to work properly.
Imagimob AI learns quickly and easily. This means your application becomes smarter and more sophisticated over time.
Thanks to a tiny memory footprint, Imagimob AI software runs efficiently even on the most low-end hardware.
Low running costs
Imagimob AI operates locally, and only sends data when it matters. This means significantly less power consumption and data transmission costs—crucial for wearable devices.
Constantly streaming data to the cloud for storage makes a system vulnerable to privacy violations. By processing data locally on each device, these problems can be avoided entirely.
Imagimob AI Motion uses input from motion sensors such as accelerometers, gyros and magnetometers, and translates this into motions. It includes the following components:
Imagimob AI Capture
Capture comprises a mobile app and a capture device. It’s used to capture synchronised motion sensor data and video during the data capture phase. Once the data capture is ready, it is sent to SensorBeat Analyser for analysis.
Imagimob AI Analyser
Analyser is a software package for analyzing and collecting action from motion sensors and video. This is where you do the AI learning. You can playback video with synchronised sensor data, in order to identify important patterns. The result of the analysis is the motion pattern database for Imagimob AI Lib.
Imagimob AI Lib
Imagimob AI Lib is a embedded software package that includes the AI engine and a trained AI model. It can be easily integrated into 3rd party hardware platforms. Imagimob AI Lib is fast, accurate and has a very small footprint, which means that it can run on low-cost and low-power hardware devices. For OEM devices the Lib is a source code / pre-compiled library written in ANSI C. The Lib is also available on iOS, Android and Apple watchOS.
Download this fact sheet that explains the actual power consumption for the Lib for a specific application.