TouchTools allows users to instantly summon and manipulate virtual tools with ease and precision. Editing, creating, working, and collaborating on touch surfaces have never been so intuitive.
- Powered by Qeexo AutoML, TouchTools looks at the pose of the user’s hand to determine the user’s intent and summons the appropriate virtual tool.
- Qeexo AutoML leverages data from medium-to-large-sized touch surfaces and analyzes the time-series relationship between touch-points – not just counting the number of fingers on the screen.
- TouchTools has applications in almost every environment, including tablets, automotive, education, and industrial.
Qeexo AutoML enables many creative applications of machine learning – anyone can use this tool to build innovative solutions without ML expertise.
Governments all over the world are investing in machine learning and sensors attached to bridges and other structures to monitor their health. In Japan, for example, regular earthquakes increase the fatigue of bridges, railroads, roads, and buildings, which need to be monitored in the interest of public safety.
- Machine learning models built with Qeexo AutoML are optimized to run locally on MCUs, so decisions can now be made in real-time, on-device. Only critical information such as alerts, not the raw data, are relayed to the monitoring station.
- Due to the remoteness of some structural health monitoring sensors, they typically are required to run on limited battery power for extended periods of time. Qeexo AutoML generates extremely efficient models with ultra-low power consumption, extending the battery life of these devices.
- The build and deployment of machine learning models can be entirely automated with Qeexo AutoML so that daily/weekly upgrade based on new data is feasible.