An unsupervised acoustic fall detection system using source separation for sound interference suppression
journal contributionposted on 22.12.2014 by Muhammad Salman Khan, Miao Yu, Pengming Feng, Liang Wang, Jonathon Chambers
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We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person׳s normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove the possible interferences from other background sound sources. Mel-frequency cepstral coefficient (MFCC) features are next extracted from the processed signals and used to construct a data description model based on a one class support vector machine (OCSVM) method, which is finally applied to distinguish fall from non-fall sounds. Experiments on a recorded dataset confirm that our proposed fall detection system can achieve better performance, especially with high level of interference from other sound sources, as compared with existing single microphone based methods.
M.S. Khan acknowledges the financial support of UET, Peshawar, the Higher Education Commission (HEC) of Pakistan and Engineering and Physical Sciences Research Council (EPSRC), [Grant no. EP/K014307/1].
- Mechanical, Electrical and Manufacturing Engineering