  Gait has been used in clinical and healthcare applications to assess the
physical and cognitive health of older adults. Acoustic based gait detection is
a promising approach to collect gait data of older adults passively and
non-intrusively. However, there has been limited work in developing acoustic
based gait detectors that can operate in noisy polyphonic acoustic scenes of
homes and care homes. We attribute this to the lack of good quality gait
datasets from the real-world to train a gait detector on. In this paper, we put
forward a novel machine learning based filter which can triage gait audio
samples suitable for training machine learning models for gait detection. The
filter achieves this by eliminating noisy samples at an f(1) score of 0.85 and
prioritising gait samples with distinct spectral features and minimal noise. To
demonstrate the effectiveness of the filter, we train and evaluate a deep
learning model on gait datasets collected from older adults with and without
applying 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 gait
samples. The proposed filter will help automate the task of manual annotation
of gait samples for training acoustic based gait detection models for older
adults in indoor environments.
