AI processing of time series for smart sensors
Published : 8 February 2020
Today, sensors are used to acquire data of a given modality (acoustics, pressure, image, etc.). Usually, such data are stored before being analysed, for instance with machine learning methods. The relevant information is thereby extracted. A large variety of sensors and use cases can be considered:
• Microphones for the automatic classification of acoustic landscapes,
• Pressure sensors to study deformation and monitor various architectures (brides, dams, wind turbines, etc.),
• Seismometers to detect signals warning of a seisms or of a volcanic eruption,
• Smart watches or bracelets to detect stress phases,
The issue of smart sensors consists in creating and designing sensors whose output is the relevant information, straightaway (see Fig. 1). Most of the time the raw signal no longer has to be transferred and stored. Smart sensors are a challenge in numerous fields, typically when sensors have to run autonomously in remote environments, or with limited power and storage access. For instance when studying acoustic landscapes pour environmental monitoring (forest, underwater areas, etc). IoT and wearable sensors are also targeted.
Turning a sensor into a smart sensor presents a challenge at many levels. For instance, efficient AI methods to process the data need to be designed, with constraints in term of computation power and energy. Another challenge consists in building those analysis tools from small datasets, or from weakly supervised datasets. CEA is already conducting researches on those issues, and AI based methods are particularly relevant.
This PhD subjects focuses on sensors recording time series: IMU, microphones, connected bracelets, etc. The core of the issue is to work on AI methods for time series, in one or several applicative fields. The PhD registers in a larger subject, namely AI reliability (anomaly detection, detection of events of class unseen during the training stage, etc.), but also the development of AI methods under labelling constraint. The topic is ambitious and several approaches are considered.