RESEARCH

HOME RESEARCH
Behavior Computing
States and Traits
Other: Computation Methods for Health
Other: Signal Modeling for Understanding
Sensing with Contexts: Crying Reason Classification for Infant Care Center with Environmental Fusion
Abstract
Crying is the only communication way from infants due to immature larynx and pharynx. It usually represents the urgent demand from infants. Caregivers and parents need to solve the urgent demand as soon as possible. Unfortunately, they are suffered from not knowing why infants cried and try many methods to comfort infants, in a result of that, there are a lot of material to teach parents how to understand infants' cry sound. However, it didn't work in the baby care center. The law in Taiwan allows caregiver taking care at most 5 infants in a time. If one of infants cried and caregiver could not solve immediately, chain problem will happen such as all infants crying simultaneously. Therefore, cry reason classification are important for the baby care center. Furthermore, event in the center may induce infants cry due to sensitivity to environment of infants. In this paper, we proposed a deep network that learns context information from the cry sound and leverages the event and environment factor to increase the performances of reason classification.
Figures
Status of event for specific day in the baby care center. It shows routine annotation of baby care center in a day from around 8 a.m. to 4 p.m.
Status of event for specific day in the baby care center. It shows routine annotation of baby care center in a day from around 8 a.m. to 4 p.m.
Context box generate the acoustic feature with spectrogram with 20 seconds as well as environmental information are presented with inter-environmental vector and intra-environmental vector, then fusion with simple concatenate to predict the result.
Context box generate the acoustic feature with spectrogram with 20 seconds as well as environmental information are presented with inter-environmental vector and intra-environmental vector, then fusion with simple concatenate to predict the result.
Keywords
acoustic signal processing | health care | inference mechanisms | paediatrics | patient care | signal classification
Authors
Chun-Min Chang Huan-Yu Chen Hsiang-Chun Chen Chi-Chun Lee
Publication Date
2020/12/07
Conference
Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)
Publisher
IEEE