An unsupervised acoustic fall detection system using source separation for sound interference suppression
Muhammad Salman Khan
Miao Yu
Pengming Feng
Liang Wang
Jonathon Chambers
2134/16531
https://repository.lboro.ac.uk/articles/journal_contribution/An_unsupervised_acoustic_fall_detection_system_using_source_separation_for_sound_interference_suppression/9564260
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.
2014-12-22 10:42:38
Health care
Fall detection
Unsupervised classification
Source separation
Mel-frequency cepstral coefficient
One class support vector machine
Mechanical Engineering not elsewhere classified