Unsupervised End-to-End Deep Model for Newborn and Infant Activity Recognition
Open accessAbstract
Human activity recognition (HAR) works have mostly focused on the activities of adults. However, HAR is typically beneficial to the safety and wellness of newborn or infants because they have difficulties in verbal communication. The activities of infants are different from those of adults in terms of its types and intensity. Hence, it is necessary to study the behavior of infants separately. We study newborn and infant activity recognition by analyzing accelerometer data from the sensors attached to body. We aim to classify four types of activities: sleeping, moving in agony, moving in normal condition, and movement by external force. For this work, we collected 11 h videos and corresponding sensor data from 10 infant subjects. For recognition, we propose an end-to-end deep model using autoencoder and k-means clustering, which is trained in an unsupervised way. From a set of performance tests, our model can achieve 0.96 in balanced accuracy and F-1 score of 0.95.
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Using a three-layer autoencoder combined with k-means clustering, Jun and Choi achieve 96% weighted accuracy in an unsupervised way for classifying newborn and infant activities.
“Using the data from an accelerometer attached to the body and a three-layer autoencoder combined with k-means clustering, they achieve 96% weighted accuracy in an unsupervised way.”
From Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2022) · cites this paper as [142]
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- Authors
- Jun, K., & Choi, S.
- Affiliations
- Incheon National University (KR)
- ChoisTechnology, Incheon 21984, Korea
- Corresponding author
- Jun, Kyungkoo
- ORCID
- Jun, Kyungkoo
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-11-12
- Volume
- 20
- Issue
- 22
- Pages
- 6467
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7696802
- OpenAlex
- W3099729056
- Cited by
- 21
- References
- 43
- Citations by year
- 2026: 1 · 2024: 5 · 2023: 4 · 2022: 9 · 2021: 2
- FWCI
- 1.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Incheon National University (2016-2031)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Health Sciences › Health Professions › Pharmacy › Infant Health and Development
- Keywords
- wearable accelerometers, unsupervised learning, autoencoder, K-means clustering, infant safety
- MeSH terms
- Adult, Computer Simulation, Humans, Human Activities, Infant, Infant, Newborn, Movement, Cluster Analysis, Recognition, Psychology
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