Hardware-accelerated machine learning for human localization, identification and behavior inference
Use energy-efficient machine learning techniques, including (recursive) neural networks, for tagless indoor human localization, identification and behavior inference using data from multiple sensor types. For high energy efficiency, data should be processed using hardware accelerators and close to source, e.g., using embedded low power FPGAs. Behavioral inference can much benefit from (accelerated) cloud processing, using self-correlation and cross-user correlation of large amounts of data.
- PE7_7 Signal processing
- PE6_11 Machine learning, statistical data processing and applications using signal processing (e.g. speech, image, video)
- Machine learning
- Low-power electronics
- Embedded machine learning