actionable knowledge extraction from time series.
Imagine having access to a wealth of valuable information hidden within your engineering sensor data. In many engineering applications, a lot of sensor data is being recorded offering a huge potential to obtain useful information and create additional functionalities. We use the term ‘actionable knowledge’ once we can extract information (patterns, anomalies) from the data based on which follow-up actions can be taken. For example, identifying anomalous breathing patterns from sensor data (pressures, flows) in a respiratory device to create a warning system for the physician.
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unlock the power of sensor data with actionable knowledge extraction.
In many applications, the available sensor data is however very noisy due to high variability in the use environment or poor sensor quality. Advanced statistical- and machine learning techniques enable us to extract actionable knowledge even in these situations. Integrating/embedding these smart algorithms into your system offers new functionalities.