Energy efficient monitoring of activities of daily living using wireless acoustic sensor networks in clean and noisy conditions

This work examines the use of a Wireless Acoustic Sensor Network (WASN) for the classification of clinically relevant activities of daily living (ADL) from elderly people. The aim of this research is to automatically compile a summary report about the performed ADLs which can be easily interpreted by caregivers. In this work the classification performance of the WASN will be evaluated in both clean and noisy conditions. Moreover, the computational complexity of the WASN and solutions to reduce the required computational costs are examined as well. The obtained classification results indicate that the computational cost can be reduced by a factor of 2.43 without a significant loss in accuracy. In addition, the WASN yields a 1.4% to 4.8% increase in classification accuracy in noisy conditions compared to single microphone solutions.