Dataset Information

Auditory chaos classification in real-world environments.

ABSTRACT:

Background & motivation

Household chaos is an established risk factor for child development. However, current methods for measuring household chaos rely on parent surveys, meaning existing research efforts cannot disentangle potentially dynamic bidirectional relations between high chaos environments and child behavior problems.

Proposed approach

We train and make publicly available a classifier to provide objective, high-resolution predictions of household chaos from real-world child-worn audio recordings. To do so, we collect and annotate a novel dataset of ground-truth auditory chaos labels compiled from over 411 h of daylong recordings collected via audio recorders worn by N=22 infants in their homes. We leverage an existing sound event classifier to identify candidate high chaos segments, increasing annotation efficiency 8.32× relative to random sampling.

Result

Our best-performing model successfully classifies four levels of real-world household auditory chaos with a macro F1 score of 0.701 (Precision: 0.705, Recall: 0.702) and a weighted F1 score of 0.679 (Precision: 0.685, Recall: 0.680).

Significance

In future work, high-resolution objective chaos predictions from our model can be leveraged for basic science and intervention, including testing theorized mechanisms by which chaos affects children's cognition and behavior. Additionally, to facilitate further model development we make publicly available the first and largest balanced annotated audio dataset of real-world household chaos.

SUBMITTER: Khante P

PROVIDER: S-EPMC10764466 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Auditory chaos classification in real-world environments.

Khante Priyanka P Thomaz Edison E de Barbaro Kaya K

Frontiers in digital health 20231221

<h4>Background & motivation</h4>Household chaos is an established risk factor for child development. However, current methods for measuring household chaos rely on parent surveys, meaning existing research efforts cannot disentangle potentially dynamic bidirectional relations between high chaos environments and child behavior problems.<h4>Proposed approach</h4>We train and make publicly available a classifier to provide objective, high-resolution predictions of household chaos from real-world ch ...[more]

PMID: 38178925

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Project description:BackgroundWalking patterns can provide important indications of a person's health status and be beneficial in the early diagnosis of individuals with a potential walking disorder. For appropriate gait analysis, it is critical that natural functional walking characteristics are captured, rather than those experienced in artificial or observed settings. To better understand the extent to which setting influences gait patterns, and particularly whether observation plays a varying role on subjects of different ages, the current study investigates to what extent people walk differently in lab versus real-world environments and whether age dependencies exist.MethodsThe walking patterns of 20 young and 20 elderly healthy subjects were recorded with five wearable inertial measurement units (ZurichMOVE sensors) attached to both ankles, both wrists and the chest. An automated detection process based on dynamic time warping was developed to efficiently identify the relevant sequences. From the ZurichMOVE recordings, 15 spatio-temporal gait parameters were extracted, analyzed and compared between motion patterns captured in a controlled lab environment (10 m walking test) and the non-controlled ecologically valid real-world environment (72 h recording) in both groups.ResultsSeveral parameters (Cluster A) showed significant differences between the two environments for both groups, including an increased outward foot rotation, step width, number of steps per 180° turn, stance to swing ratio, and cycle time deviation in the real-world. A number of parameters (Cluster B) showed only significant differences between the two environments for elderly subjects, including a decreased gait velocity (p = 0.0072), decreased cadence (p = 0.0051) and increased cycle time (p = 0.0051) in real-world settings. Importantly, the real-world environment increased the differences in several parameters between the young and elderly groups.ConclusionElderly test subjects walked differently in controlled lab settings compared to their real-world environments, which indicates the need to better understand natural walking patterns under ecologically valid conditions before clinically relevant conclusions can be drawn on a subject's functional status. Moreover, the greater inter-group differences in real-world environments seem promising regarding the sensitive identification of subjects with indications of a walking disorder.

Dataset Information

Auditory chaos classification in real-world environments.

Background & motivation

Proposed approach

Result

Significance

Publications

Auditory chaos classification in real-world environments.

Similar Datasets

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