Sensyne is our proprietary machine learning software to calibrate sensor networks ensuring their data remains accurate and reliable over time.
Sensyne’s machine learning remotely calibrates the CleanSpace Tags, processing over 16 million+ data points per day to calculate new Tag baselines. The revised baselines are pushed to the CleanSpace App for near-realtime view of accurate CO readings. The CO sensor calibration is performed against the Tag temperature for each tag and benchmarked against the whole network.
CleanSpace Tag uses Sensyne’s machine learning to adapt optimal sensing frequency automatically based on the user movement state (e.g. every 60 secs walking, 30 secs cycling. The user movement state is derived from our proprietary machine learning based journey engine using smart phone built-in sensor data e.g. accelerometer, GPS.