论文标题
步态三叶式工具包，用于在室内环境中重叠的声学事件重叠
A Gait Triaging Toolkit for Overlapping Acoustic Events in Indoor Environments
论文作者
论文摘要
步态已用于临床和医疗保健应用中，以评估老年人的身体和认知健康。基于声学的步态检测是一种有前途的方法，可以被动地和非侵入性地收集老年人的步态数据。但是，在开发基于声学的步态探测器方面的工作有限，这些步态探测器可以在房屋和养老院的嘈杂的多形声学场景中运行。我们将其归因于现实世界中缺乏高质量的步态数据集来训练步态探测器。在本文中，我们提出了一个新型的基于机器学习的过滤器，该过滤器可以分类步态音频样本，适用于训练机器学习模型以进行步态检测。该滤镜通过在0.85的F（1）得分下消除嘈杂的样品来实现这一目标，并优先考虑具有不同光谱特征和最小噪声的步态样品。为了证明过滤器的有效性，我们训练并评估了从有没有应用过滤器的老年人收集的步态数据集上的深度学习模型。使用过滤步态样品训练时，该模型在未见的房地产步态数据上的F（1）分数中增加了25分。提出的过滤器将有助于自动化步态样本的手动注释任务，以训练室内环境中老年人的基于声学的步态检测模型。
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.
