Bert Van Den Broeck

Footstep localization based on in-home microphone-array signals

This paper describes a system able to detect footstep locations. Through acoustic information retrieved from a wireless sensor network with small and relatively cheap microphone arrays. A dataset was recorded in order to validate the accuracy of the detection. Results on this dataset show that a best median of errors of 31cm per time moment are achievable, but results heavily depend on the positions of the microphones relative to the footsteps.

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.

Monitoring 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) of 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. Results indicate that the classification performance of the WASN is 75.3±4.3% on clean acoustic data selected from the node receiving with the highest SNR. By incorporating spatial information extracted by the WASN, the classification accuracy further increases to 78.6±1.4%. In addition, the classification performance of the WASN in noisy conditions is in absolute average 8.1% to 9.0% more accurate compared to highest obtained single microphone results.