Previous studies have indicated the relation between a person’s gait related parameters and their health. Therefore, the ability to continuously monitor a person’s gait characteristics would be an advantage for caregivers. This paper proposes a solution that is able to estimate footstep locations based on audio measurements in a wireless acoustic sensor network (WASN). In realistic noisy environment this can however be difficult. A system proposed in previous work is first described and it is then discussed that it has difficulties to handle noisy environments. This paper proposes different modifications in order to improve noise robustness, i.e. average subtraction, multichannel Wiener filter and a noise robust footstep detector. These modifications and the original system are tested on a simulated dataset using stationary noise. This shows that an error reduction of 70% compared to the original system can be achieved. This improvement was confirmed on a real life dataset (error reduction of 60%). Finally the limits of the system are tested under highly non-stationary noise conditions. One modification was able to handle that difficult scenario under all SNR conditions (at best an error reduction of about 33% is observed in these experiments).
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