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Gait has been used in clinical and healthcare applications to assess thephysical and cognitive health of older adults. Acoustic based gait detection isa promising approach to collect gait data of older adults passively andnon-intrusively. However, there has been limited work in developing acousticbased gait detectors that can operate in noisy polyphonic acoustic scenes ofhomes and care homes. We attribute this to the lack of good quality gaitdatasets from the real-world to train a gait detector on. In this paper, we putforward a novel machine learning based filter which can triage gait audiosamples suitable for training machine learning models for gait detection. Thefilter achieves this by eliminating noisy samples at an f(1) score of 0.85 andprioritising gait samples with distinct spectral features and minimal noise. Todemonstrate the effectiveness of the filter, we train and evaluate a deeplearning model on gait datasets collected from older adults with and withoutapplying the filter. The model registers an increase of 25 points in its f(1)score on unseen real-word gait data when trained with the filtered gaitsamples. The proposed filter will help automate the task of manual annotationof gait samples for training acoustic based gait detection models for olderadults in indoor environments.

A Gait Triaging Toolkit for Overlapping Acoustic Events in Indoor Environments